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2157 topics across 305 pages. If you know what you want to read about but not where it sits in a syllabus, start here.

0-9

100% vs Sampling: Acceptance Sampling for Attributes — Statistical Quality Control (Unit 4)
2×2 Game: Game Theory — Optimization Techniques (Unit 4)
2⁷ Designs: Factorial Experiments Beyond Two Factors: 2^k, 3^2 and Single-Degree Components — Design and Analysis of Experiments (Unit 2)
3-D Plots: Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5)
3² Factorial: Factorial Experiments Beyond Two Factors: 2^k, 3^2 and Single-Degree Components — Design and Analysis of Experiments (Unit 2)
4 M's of SQC: Introduction to SQC — Statistical Quality Control (Unit 1)

A

Absolute Advantage: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Absolute Poverty: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Accessibility & Communication: Unit IX: Stochastic Processes — UGC NET Statistics
Accessing form elements: Client-Side Scripting — Web Technologies (Unit 4)
Accounting Equation: What Accounting Is: Concepts, Conventions and the Rules of Debit and Credit — Financial Accounting (Unit 1)
Accrual: What Accounting Is: Concepts, Conventions and the Rules of Debit and Credit — Financial Accounting (Unit 1)
ACF and PACF: Advanced Topics — Data Science with R (Unit 5)
Activation functions: Foundations of Deep Learning — Neural Networks and Deep Learning (Unit 1)
Activation functions in deep networks: Deep Neural Networks — Neural Networks and Deep Learning (Unit 2)
Addition Theorem: Elementary Probability — Theory of Probability (Unit 1) · Mathematical Expectation — Theory of Probability (Unit 4)
Additive Model: Time Series — Applied Statistics (Unit 1)
Additive Property: Gamma & Beta Distributions — Continuous Distributions (Unit 3) · Negative Binomial Distribution — Discrete Distributions (Unit 3) · Normal Distribution — Continuous Distributions (Unit 4) · Poisson Distribution — Discrete Distributions (Unit 2) · Uniform, Bernoulli & Binomial — Discrete Distributions (Unit 1)
Adjusted Cash Book: Bank Reconciliation Statement — Financial Accounting (Unit 4)
Adjusted R^2: Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
Admissibility: Decision Theory, Bayes and Minimax, and Density Estimation — Estimation Theory (Unit 4)
Adolescent and Adult Learners: Unit I: Teaching Aptitude — UGC NET Paper I
Advanced visualizations: Dashboard Design and Business Insights — Business Intelligence Tools (Unit 5)
Advantages: Completely Randomised Design (CRD) — Design & Analysis of Experiments (Unit 2) · Introduction & LPP Formulation — Operations Research (Unit 1) · Latin Square Design (LSD) — Design & Analysis of Experiments (Unit 4) · Randomised Block Design (RBD) — Design & Analysis of Experiments (Unit 3)
Advantages / Disadvantages: Non-parametric Tests — Inferential Statistics (Unit 5)
Advantages of cloud in machine learning: Virtualization and Deployment Models — Cloud Computing for Data Science (Unit 2)
Advantages of ER modelling: The Entity-Relationship Model — Database Management Systems (Unit 2)
Advantages of MongoDB over RDBMS: MongoDB Architecture, Data Modeling and Basics — Document Oriented Database (Unit 2)
Age at Death X: Survival Distribution & Life Tables — Actuarial Statistics (Unit 3)
Age-Specific Death Rate: Vital Statistics — Applied Statistics (Unit 4)
Aggregate Claims S: Premium Principles & Individual Risk Models — Actuarial Statistics (Unit 2)
Aggregate functions: Structured Query Language — Database Management Systems (Unit 4)
Aggregation: Practical — Data Science using Python (STS-208)
Aggregation and SQL, side by side: Data Modelling and Aggregation — Document Oriented Database (Unit 4)
aggregation framework: Data Modelling and Aggregation — Document Oriented Database (Unit 4)
Agricultural Policy: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
AI ethics: Advanced and Emerging Topics — Neural Networks and Deep Learning (Unit 5)
AI ethics and societal impact: Expert Systems, Probabilistic and Emerging AI — Artificial Intelligence (Unit 5)
AIaaS and GPUaaS: Cloud Platforms for Data Science and ML — Cloud Computing for Data Science (Unit 4)
Air, Water, Soil and Noise Pollution: Unit IX: People, Development and Environment — UGC NET Paper I
airline model: Non-Stationary and Seasonal Models — Time Series Analysis and Forecasting (Unit 3)
Aitken GLS: Linear Models: Estimability, Gauss-Markov and Aitken — Linear Algebra & Linear Models (Unit 4)
Algebraic vs Geometric Multiplicity: Characteristic Roots, Cayley-Hamilton and Spectral Decomposition — Linear Algebra & Linear Models (Unit 2)
Algorithms and flowcharts: Introduction to Computer Programming — Problem Solving Using C (Unit 1)
Aliases: Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs — Design and Analysis of Experiments (Unit 3)
Allocation Methods: Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Almost Sure Convergence: Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
Alternative Solutions: Graphical Method — Operations Research (Unit 2)
Ambiguity: Introduction to NLP and Language Fundamentals — Natural Language Processing (Unit 1)
Analogies: Unit VI: Logical Reasoning — UGC NET Paper I
Analysis of Covariance: Practical — Design and Analysis of Experiments, Conventional (STS-206 Section A) · Two-Way ANOVA with Several Observations per Cell, Multiple Comparisons and ANCOVA — Design and Analysis of Experiments (Unit 1)
Analysis of Surrogate Data: Surrogate End Points and Meta-Analysis — Clinical Trials (Unit 5)
Analysis ToolPak: Excel Basics for Data Analysis — MS-Excel (Unit 1)
Angle between Lines: Regression — Statistical Methods (Unit 4)
Animations and sliders: Advanced Topics — Data Science with R (Unit 5)
Annuities Certain: Life Annuities — Advanced Actuarial Statistics (Unit 3)
Annuity Certain: Life Annuities & Premiums — Actuarial Statistics (Unit 5)
Annuity-Due: Life Annuities — Advanced Actuarial Statistics (Unit 3)
Annuity-Immediate: Life Annuities — Advanced Actuarial Statistics (Unit 3)
Anomaly Detection: Practical — Data Science using Python (STS-208)
ANOVA: Heteroscedasticity — Econometrics (Unit 3) · Hypothesis Testing in Excel — MS-Excel (Unit 5) · Inferential Statistics & Hypothesis Testing — R Programming (Unit 4) · Practical — Statistical Methods using Python (STS-105)
ANOVA Table: Analysis of Variance (ANOVA) — Design & Analysis of Experiments (Unit 1)
Anthropogenic Impacts: Unit IX: People, Development and Environment — UGC NET Paper I
Anumana and Vyapti: Unit VI: Logical Reasoning — UGC NET Paper I
AOQ / AOQL: Acceptance Sampling for Attributes — Statistical Quality Control (Unit 4)
AOQ Computation: Single Sampling Plan — Statistical Quality Control (Unit 5)
Applications: Convolutional Neural Networks — Neural Networks and Deep Learning (Unit 3) · Introduction & LPP Formulation — Operations Research (Unit 1) · Measures of Dispersion — Descriptive Statistics (Unit 4)
Applications of AI: Introduction to AI and Intelligent Agents — Artificial Intelligence (Unit 1)
Applications of BI across functional domains: Introduction to BI and Decision Support Systems — Business Intelligence Tools (Unit 1)
Applications of Computers: Computer Basics — Computational Statistics & R Programming (Unit 1)
Applications of machine learning: Introduction to Machine Learning — Machine Learning (Unit 1)
Applications of unsupervised learning: Unsupervised Learning — Machine Learning (Unit 5)
Applied Research Problems: Questionnaire Design and Fieldwork — Research Methodology (Unit 4)
apply Family: Basics of R for Statistical Data Handling — R Programming (Unit 1)
Apportionable: Net Premiums — Advanced Actuarial Statistics (Unit 4)
Apriori algorithm: Association Analysis — Data Mining (Unit 3)
AQL: Acceptance Sampling for Attributes — Statistical Quality Control (Unit 4)
AR(1) Scheme: Autocorrelation — Econometrics (Unit 5)
AR, MA and ARIMA: Advanced Topics — Data Science with R (Unit 5)
Architecture and components: Data Warehousing and OLAP — Data Mining (Unit 1) · Preparation, Visualization and Storytelling with Tableau — Business Intelligence Tools (Unit 3)
Area Property: Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Arithmetic and broadcasting: NumPy Essentials — Python for Data Analysis and Visualization (Unit 1)
Arithmetic and data alignment: Pandas Basics and Data Structures — Python for Data Analysis and Visualization (Unit 2)
Arithmetic Functions: Excel Basics for Data Analysis — MS-Excel (Unit 1)
Arithmetic Mean: Measures of Central Tendency — Descriptive Statistics (Unit 3)
Arithmetic Operators: Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Array queries: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Arrays: JavaScript — Web Technologies (Unit 3)
Artificial Variables: Big-M & Two-Phase Methods — Operations Research (Unit 4)
ASFR: Life Tables, Fertility & Population Growth — Applied Statistics (Unit 5)
ASN / ATI: Acceptance Sampling for Attributes — Statistical Quality Control (Unit 4)
ASN Function: Sequential Analysis and Decision Theory — Testing of Hypotheses (Unit 4)
Assertions: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
Assignable Causes: Introduction to SQC — Statistical Quality Control (Unit 1)
Assignment: Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Assignment & Modes: R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4)
assignment operator: Basics of R Programming — Data Science with R (Unit 2)
Association: Theory of Attributes — Statistical Methods (Unit 5)
Association rules with item constraints: Association Analysis — Data Mining (Unit 3)
Association Scheme: PBIBD(2), Lattice and Youden Designs, and Response Surface Methodology — Design and Analysis of Experiments (Unit 4)
Assumptions: Analysis of Variance (ANOVA) — Design & Analysis of Experiments (Unit 1) · Models and Estimation — Econometrics (Unit 2) · Sequencing Problem — Optimization Techniques (Unit 3)
ATI Computation: Single Sampling Plan — Statistical Quality Control (Unit 5)
Audio and Video Conferencing: Unit VIII: Information and Communication Technology — UGC NET Paper I
Audio-Visual Aids: Report Writing and Presentation — Research Methodology (Unit 5)
Augmented Dickey–Fuller test: Non-Stationary and Seasonal Models — Time Series Analysis and Forecasting (Unit 3)
Auto-scaling a SageMaker endpoint: Use CloudWatch/Stackdriver to monitor endpoints, set alarms and auto-scale — Cloud Computing for Data Science (Experiment 13)
Autocorrelation Function (ACF): Unit VII: Time Series — UGC NET Statistics
Autocovariance and autocorrelation: Fundamentals and Stationary Processes — Time Series Analysis and Forecasting (Unit 1)
Autocovariance Function (ACVF): Unit VII: Time Series — UGC NET Statistics
AutoML: Cloud Platforms for Data Science and ML — Cloud Computing for Data Science (Unit 4)
AutoSum: Data Processing in Excel — Computational Statistics & R Programming (Unit 2)
Average Sample Number (ASN): Unit V: Testing of Hypotheses — UGC NET Statistics
Average-Quality Approach: Single Sampling Plan — Statistical Quality Control (Unit 5)
Averages: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
AWS Glue (the managed option): A simple ETL job: extract, transform, load into a cloud warehouse — Cloud Computing for Data Science (Experiment 12)
Axiomatic Definition: Elementary Probability — Theory of Probability (Unit 1)
Axiomatic Definition (Kolmogorov, 1933): Unit I: Probability and Distributions — UGC NET Statistics
Azure and GCP: Create and configure file storage on a cloud VM (EFS) — Cloud Computing for Data Science (Experiment 6)
Azure Automated ML: Use cloud AutoML services for a dataset prediction task — Cloud Computing for Data Science (Experiment 14)

B

Background: CSS — Web Technologies (Unit 2)
Backward propagation: Deep Neural Networks — Neural Networks and Deep Learning (Unit 2)
Bag of Words and N-grams: Information Extraction and Representation — Natural Language Processing (NLP) (Unit 3)
Balance c/d and b/d: Journal and Ledger: the Accounting Process Worked End to End — Financial Accounting (Unit 2)
Balance of Payments: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Balance Sheet: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Bank Charges: Bank Reconciliation Statement — Financial Accounting (Unit 4)
Bank Portfolio: Money, Banking and Credit Creation — Economics (Unit 3)
Bank Reconciliation: Bank Reconciliation Statement — Financial Accounting (Unit 4)
Bar Chart: Data Visualization in R — R Programming (Unit 3)
Bar Charts and Histograms: Unit VII: Data Interpretation — UGC NET Paper I
Bar Diagram: Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
Barriers to Communication: Unit IV: Communication — UGC NET Paper I
Bartlett's Approximation: Hotelling's T-squared, Mahalanobis D-squared, Wilks' Lambda and Discriminant Analysis — Multivariate Analysis (Unit 3)
Bartlett's Formula: Unit VII: Time Series — UGC NET Statistics
Base Shifting: Index Numbers (Advanced) — Applied Statistics II (Unit 2)
Basic data validation: Client-Side Scripting — Web Technologies (Unit 4)
Basic functions: Spreadsheet Basics — Computer Fundamentals and Office Automation (Unit 4)
Basic Solution: Simplex Method — Operations Research (Unit 3)
Basic visualizations: Preparation, Visualization and Storytelling with Tableau — Business Intelligence Tools (Unit 3)
Basics of the Internet: Unit VIII: Information and Communication Technology — UGC NET Paper I
Batch against streaming for ML pipelines: Cloud Storage and Data Management — Cloud Computing for Data Science (Unit 3)
Batch and streaming together: Data Ingestion and Serialization — Big Data Technologies (Unit 4)
Bayes and Minimax Rules: Sequential Analysis and Decision Theory — Testing of Hypotheses (Unit 4)
Bayes Estimation: Decision Theory, Bayes and Minimax, and Density Estimation — Estimation Theory (Unit 4)
Bayes' theorem: Fundamentals of Probability and Basic Statistics — Statistical Foundations for Data Science (Unit 1)
Bayes' Theorem: Elementary Probability — Theory of Probability (Unit 1) · Unit I: Probability and Distributions — UGC NET Statistics
Bayesian classifiers: Classification — Data Mining (Unit 4)
Benefit Reserve: Policy Reserves — Advanced Actuarial Statistics (Unit 5)
Benefits, honestly weighed: Cloud Platforms for Data Science and ML — Cloud Computing for Data Science (Unit 4)
Bernoulli: Uniform, Bernoulli & Binomial — Discrete Distributions (Unit 1)
Bernoulli & Chebyshev WLLN: Laws of Large Numbers and Central Limit Theorems — Probability Theory (Unit 4)
BERT: Transformers and Modern NLP — Natural Language Processing (Unit 5)
BERT and GPT: Advanced and Emerging Topics — Neural Networks and Deep Learning (Unit 5)
Best Critical Region: Testing of Hypothesis — Inferential Statistics (Unit 2)
Beta of First Kind: Gamma & Beta Distributions — Continuous Distributions (Unit 3)
Beta of Second Kind: Gamma & Beta Distributions — Continuous Distributions (Unit 3)
Between-Cluster Mean Square: Cluster Sampling: the Intra-Cluster Correlation, the Design Effect and Optimum Cluster Size — Sampling Theory (Unit 3)
Bhattacharya Bounds: UMVU Estimation, Cramér-Rao and Rao-Blackwell — Estimation Theory (Unit 1)
BI lifecycle: Introduction to BI and Decision Support Systems — Business Intelligence Tools (Unit 1)
BI tools overview and comparison: Introduction to BI and Decision Support Systems — Business Intelligence Tools (Unit 1)
BI vs. Data Analytics vs. Data Science: Introduction to BI and Decision Support Systems — Business Intelligence Tools (Unit 1)
Bias and Random Error: Introduction to Clinical Trials — Clinical Trials (Unit 1)
Bias Correction: Missing Values & Efficiency Comparisons — Design & Analysis of Experiments (Unit 5)
Bias of a Ratio: Ratio and Regression Estimators: Exact Bias, the Difference Estimator, Separate and Combined — Sampling Theory (Unit 2)
bias–variance trade-off: Model Preparation, Evaluation and Feature Engineering — Machine Learning (Unit 2)
BIBD: Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs — Design and Analysis of Experiments (Unit 3) · Practical — Design and Analysis of Experiments, Conventional (STS-206 Section A)
Big data against a traditional database: Foundations of Big Data and the Hadoop Ecosystem — Big Data Technologies (Unit 1)
Big-M Method: Big-M & Two-Phase Methods — Operations Research (Unit 4)
BigQuery: Connect to cloud-hosted database services (RDS, BigQuery, Cosmos DB) — Cloud Computing for Data Science (Experiment 8)
Binary Files: Practical — Data Science using Python (STS-208)
Binomial: Uniform, Bernoulli & Binomial — Discrete Distributions (Unit 1)
Binomial distribution: Probability Distributions — Statistical Foundations for Data Science (Unit 3)
Binomial Pₐ: Single Sampling Plan — Statistical Quality Control (Unit 5)
Bioequivalence Trials: Design of Clinical Trials — Clinical Trials (Unit 3)
Biological vs artificial neurons: Foundations of Deep Learning — Neural Networks and Deep Learning (Unit 1)
BIRCH: Clustering Techniques — Data Mining (Unit 5)
Bits and Bytes: Unit VIII: Information and Communication Technology — UGC NET Paper I
Bivariate Data: Curve Fitting — Statistical Methods (Unit 1)
Bivariate data and scatter plots: Correlation and Regression — Statistical Foundations for Data Science (Unit 4)
Bivariate Frequency: Correlation — Statistical Methods (Unit 2)
Bivariate r.v.: Bivariate Random Variables — Theory of Probability (Unit 3)
Bivariate Table: Bivariate Random Variables — Theory of Probability (Unit 3)
Block diagram of a computer: Number Systems, Evolution, Block Diagram and Generations — Computer Fundamentals and Office Automation (Unit 1)
Blocking public access: Create and manage storage buckets; upload and access datasets — Cloud Computing for Data Science (Experiment 4)
Blocks: Hadoop Distributed File System and YARN — Big Data Technologies (Unit 2)
Blood Relations and Directions: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
BLUE: Linear Models: Estimability, Gauss-Markov and Aitken — Linear Algebra & Linear Models (Unit 4) · Models and Estimation — Econometrics (Unit 2)
Book of Original Entry: Journal and Ledger: the Accounting Process Worked End to End — Financial Accounting (Unit 2)
Boole's Inequality: Elementary Probability — Theory of Probability (Unit 1)
Boolean indexing: NumPy Essentials — Python for Data Analysis and Visualization (Unit 1)
Bootstrap: Completeness, Lehmann-Scheffé, CAN and BAN, Jackknife and Bootstrap — Estimation Theory (Unit 2) · Practical — Estimation Theory, Conventional (STS-205 Section A)
Borders: CSS — Web Technologies (Unit 2)
Borel Sets: Measure Theory and Probability as a Measure — Probability Theory (Unit 1)
Borel Sigma-Field: Univariate Random Variables — Theory of Probability (Unit 2)
Borel SLLN: Laws of Large Numbers and Central Limit Theorems — Probability Theory (Unit 4)
Borel–Cantelli: Convergence of Sequences of Random Variables — Probability Theory (Unit 3)
Bounded Variation: Bounded Variation and Integrals Depending on a Parameter — Mathematical Analysis (Unit 3)
Bowley's Skewness: Moments, Skewness & Kurtosis — Descriptive Statistics (Unit 5)
box model: CSS — Web Technologies (Unit 2)
Boxplot: Data Visualization in R — R Programming (Unit 3)
Box–Muller: Practical — Distribution Theory Conventional and using R (STS-107)
Breadth First Search: Problem Solving — State Space and Uninformed Search — Artificial Intelligence (Unit 2)
Bretton Woods: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Breusch–Godfrey: Autocorrelation — Econometrics (Unit 5)
Breusch–Pagan: Heteroscedasticity — Econometrics (Unit 3)
Building a dashboard: Data Analysis and Visualization — Computer Fundamentals and Office Automation (Unit 5)
Building and running: Containerize an ML model with Docker — Data Engineering and MLOps (Experiment 10)
Building blocks: The Entity-Relationship Model — Database Management Systems (Unit 2)
Bulk operations: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Business Entity: What Accounting Is: Concepts, Conventions and the Rules of Debit and Credit — Financial Accounting (Unit 1)

C

c Chart: Control Charts for Attributes — Statistical Quality Control (Unit 3)
C tokens: Introduction to Computer Programming — Problem Solving Using C (Unit 1)
C4.5: Classification — Data Mining (Unit 4)
Calculated fields and LOD expressions: Preparation, Visualization and Storytelling with Tableau — Business Intelligence Tools (Unit 3)
CAN & BAN: Completeness, Lehmann-Scheffé, CAN and BAN, Jackknife and Bootstrap — Estimation Theory (Unit 2)
Canonical Correlation: Practical — Multivariate Analysis, Conventional (STS-205 Section B) · Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis — Multivariate Analysis (Unit 4)
Canonical Form: Quadratic Forms and Matrix Inequalities — Linear Algebra & Linear Models (Unit 3) · Simplex Method — Operations Research (Unit 3)
Canons of Taxation: Public Finance, Budgets and Deficits — Economics (Unit 4)
CAP theorem: Introduction to NoSQL and the Fundamentals of MongoDB — Document Oriented Database (Unit 1)
Capital Account: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5) · Public Finance, Budgets and Deficits — Economics (Unit 4)
Capital Fund: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
Caratheodory Extension: Measure Theory and Probability as a Measure — Probability Theory (Unit 1)
CART: Classification — Data Mining (Unit 4)
Case Report Forms: Introduction to Clinical Trials — Clinical Trials (Unit 1)
Case studies: Unsupervised Learning — Machine Learning (Unit 5)
Case studies and industry applications: Training and Deployment of ML on the Cloud — Cloud Computing for Data Science (Unit 5)
Cash Account: Journal and Ledger: the Accounting Process Worked End to End — Financial Accounting (Unit 2)
Cash Book: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
Cash Book Balance: Bank Reconciliation Statement — Financial Accounting (Unit 4)
Cash Discount: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
Cash Reserve Ratio: Money, Banking and Credit Creation — Economics (Unit 3)
Categorical clustering: Clustering Techniques — Data Mining (Unit 5)
Categorical Outcomes: Reporting and Analysis — Clinical Trials (Unit 4)
Categorical Propositions: Unit VI: Logical Reasoning — UGC NET Paper I
Cauchy: Lognormal, Weibull, Pareto, Laplace and Cauchy — Distribution Theory (Unit 1)
Cauchy MLE: Practical — Estimation Theory, Conventional (STS-205 Section A)
Cauchy's MVT: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Cauchy-Schwarz Inequality: Mathematical Expectation — Theory of Probability (Unit 4)
Cauchy–Schwarz: Quadratic Forms and Matrix Inequalities — Linear Algebra & Linear Models (Unit 3)
Cayley–Hamilton: Characteristic Roots, Cayley-Hamilton and Spectral Decomposition — Linear Algebra & Linear Models (Unit 2)
CBR: Life Tables, Fertility & Population Growth — Applied Statistics (Unit 5)
CDF: Continuous Uniform Distribution — Continuous Distributions (Unit 1) · Exponential Distribution — Continuous Distributions (Unit 2)
CDF (Distribution Function): Univariate Random Variables — Theory of Probability (Unit 2)
CDR: Vital Statistics — Applied Statistics (Unit 4)
Cell referencing: Spreadsheet Basics — Computer Fundamentals and Office Automation (Unit 4)
Cell Referencing: Excel Basics for Data Analysis — MS-Excel (Unit 1)
Central Composite Design: PBIBD(2), Lattice and Youden Designs, and Response Surface Methodology — Design and Analysis of Experiments (Unit 4)
Central Limit Theorem: Generating Functions, LLN & CLT — Theory of Probability (Unit 5) · Probability Distributions — Statistical Foundations for Data Science (Unit 3)
Central Moments: Moments, Skewness & Kurtosis — Descriptive Statistics (Unit 5)
Central Tendency: Descriptive Statistics in Excel — MS-Excel (Unit 3) · Descriptive Statistics in R — R Programming (Unit 2) · Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5)
Centred Moving Average: Seasonal Component — Applied Statistics (Unit 2) · Time Series — Applied Statistics (Unit 1)
CF, CGF, PGF: Poisson Distribution — Discrete Distributions (Unit 2)
CGF: Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
Chain Relatives: Seasonal Component — Applied Statistics (Unit 2)
Chain-Base: Index Numbers (Advanced) — Applied Statistics II (Unit 2)
Chance Causes: Introduction to SQC — Statistical Quality Control (Unit 1)
Characteristic Function: Expectation, Characteristic Functions and Inequalities — Probability Theory (Unit 2) · Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
Characteristic Roots: Characteristic Roots, Cayley-Hamilton and Spectral Decomposition — Linear Algebra & Linear Models (Unit 2)
Characteristics of cloud computing: Introduction to Cloud Computing — Cloud Computing for Data Science (Unit 1)
Characteristics of Teaching: Unit I: Teaching Aptitude — UGC NET Paper I
Charts: Data Processing in Excel — Computational Statistics & R Programming (Unit 2) · Practical — Statistical Analysis using SPSS (STS-207) · Spreadsheet Basics — Computer Fundamentals and Office Automation (Unit 4)
Charts & Graphs: Data Visualization & Frequency Analysis — MS-Excel (Unit 2)
Chebyshev's Inequality: Mathematical Expectation — Theory of Probability (Unit 4)
Chi-Square: Hypothesis Testing in Excel — MS-Excel (Unit 5)
Chi-square: Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Chi-Square Derivation: Sampling Distributions: Chi-Square, t and F — Distribution Theory (Unit 3)
Chi-square goodness of fit: Which Statistical Test Should I Use?
Chi-Square Goodness of Fit: Practical — Distribution Theory Conventional and using R (STS-107)
Chi-square test of independence: Which Statistical Test Should I Use?
Chi-square Tests: Reporting and Analysis — Clinical Trials (Unit 4)
Choosing a classifier: Supervised Learning — Classification — Machine Learning (Unit 4)
Choosing a Trend Curve: Time Series — Applied Statistics (Unit 1)
CI/CD for ML: Model Deployment and CI/CD Pipelines — Data Engineering and MLOps (Unit 4)
Circular Systematic Sampling: Systematic, Cluster & Multistage Sampling — Sampling Techniques (Unit 4)
CIs: Large Sample Tests — Inferential Statistics (Unit 3)
Class Properties: Unit IX: Stochastic Processes — UGC NET Statistics
classical architectures: Convolutional Neural Networks — Neural Networks and Deep Learning (Unit 3)
Classical Definition: Elementary Probability — Theory of Probability (Unit 1)
Classification: Statistical Description of Data — Descriptive Statistics (Unit 1)
Classification of attributes: The Entity-Relationship Model — Database Management Systems (Unit 2)
Classification of Data: Unit VII: Data Interpretation — UGC NET Paper I
Classification of DBMS: Overview of Database Management Systems — Database Management Systems (Unit 1)
Classification of entity sets: The Entity-Relationship Model — Database Management Systems (Unit 2)
Classroom Communication: Unit IV: Communication — UGC NET Paper I
Climate Change: Unit IX: People, Development and Environment — UGC NET Paper I
Closing Stock: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Cloud computing architecture: Introduction to Cloud Computing — Cloud Computing for Data Science (Unit 1)
Cloud data warehouses: Cloud Storage and Data Management — Cloud Computing for Data Science (Unit 3)
Cloud shapes: Data Architecture and Distributed Systems — Data Engineering and MLOps (Unit 2)
Cluster Analysis: Practical — Multivariate Analysis, Conventional (STS-205 Section B) · Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis — Multivariate Analysis (Unit 4)
Cluster Estimator: Cluster Sampling: the Intra-Cluster Correlation, the Design Effect and Optimum Cluster Size — Sampling Theory (Unit 3)
Cluster Sampling: Practical — Sampling Theory, Conventional (STS-206 Section B) · Systematic, Cluster & Multistage Sampling — Sampling Techniques (Unit 4)
Clustering and its types: Unsupervised Learning — Machine Learning (Unit 5)
Clustering paradigms: Clustering Techniques — Data Mining (Unit 5)
Cochran's Theorem: Analysis of Variance (ANOVA) — Design & Analysis of Experiments (Unit 1)
Cochrane–Orcutt: Autocorrelation — Econometrics (Unit 5)
Codd's 12 rules: The Relational Model and Normalization — Database Management Systems (Unit 3)
code: An FAQ chatbot on transformer embeddings — Natural Language Processing (NLP) (Experiment 14) · Extractive and abstractive summarization with Hugging Face — Natural Language Processing (NLP) (Experiment 13) · Masked word prediction with a pre-trained BERT — Natural Language Processing (NLP) (Experiment 12)
Coding and Decoding: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
Coding x: Curve Fitting — Statistical Methods (Unit 1)
Coefficient of Contingency: Theory of Attributes — Statistical Methods (Unit 5)
Coefficient of Determination: Concurrent Deviation, Multiple & Partial Correlation — Statistical Methods (Unit 3) · Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
Coefficient of Variation: Measures of Dispersion — Descriptive Statistics (Unit 4)
Colab against a cloud notebook: Set up Jupyter Notebook / Colab on a cloud VM — Cloud Computing for Data Science (Experiment 7)
Colligation: Theory of Attributes — Statistical Methods (Unit 5)
Colours: CSS — Web Technologies (Unit 2)
Combined Ratio Estimator: Ratio and Regression Estimators: Exact Bias, the Difference Estimator, Separate and Combined — Sampling Theory (Unit 2)
Combiners: MapReduce and High-Level Tools — Big Data Technologies (Unit 3)
Combining data with overlap: Wrangling, Reshaping and Visualization — Python for Data Analysis and Visualization (Unit 5)
Combo charts and sparklines: Data Analysis and Visualization — Computer Fundamentals and Office Automation (Unit 5)
Command Prompt: Basics of R for Statistical Data Handling — R Programming (Unit 1)
commands: Deploy a dataset on HDFS and perform simple operations — Data Engineering and MLOps (Experiment 5)
Common built-in exceptions: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
Communication: Model MCQs — UGC NET Paper I (General Paper)
Communication Dimensions: Report Writing and Presentation — Research Methodology (Unit 5)
Compact Sets: Metric Spaces, Compactness and Continuity — Mathematical Analysis (Unit 1)
Comparative Advantage: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Comparing classifiers: Classification — Data Mining (Unit 4)
Comparing methods: Forecast Evaluation and Comparison — Time Series Analysis and Forecasting (Unit 5)
Comparison operators: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Comparison with SRSWOR: Stratified Random Sampling — Sampling Techniques (Unit 3)
Comparison with SRSWOR / StRS: Systematic, Cluster & Multistage Sampling — Sampling Techniques (Unit 4)
Compensating Errors: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Compiler vs interpreter: Introduction to Computer Programming — Problem Solving Using C (Unit 1)
Completeness: Completeness, Lehmann-Scheffé, CAN and BAN, Jackknife and Bootstrap — Estimation Theory (Unit 2)
Components: Computer Basics — Computational Statistics & R Programming (Unit 1)
Components of a DBMS: Overview of Database Management Systems — Database Management Systems (Unit 1)
Compound Distributions: Transformations, Truncated, Mixture and Compound Distributions — Distribution Theory (Unit 2)
Compound indexes and the prefix rule: Advanced Query Processing and Optimization — Document Oriented Database (Unit 5)
Comprehension: Model MCQs — UGC NET Paper I (General Paper)
Computer-based Testing: Unit I: Teaching Aptitude — UGC NET Paper I
Concatenating along an axis: Wrangling, Reshaping and Visualization — Python for Data Analysis and Visualization (Unit 5)
Concentration Curves: Demand Analysis — Applied Statistics II (Unit 3)
Concept and Objectives of Teaching: Unit I: Teaching Aptitude — UGC NET Paper I
Concept of Dispersion: Measures of Dispersion — Descriptive Statistics (Unit 4)
Concept of Duality: Duality & Dual Simplex — Operations Research (Unit 5)
Concurrent Deviation: Concurrent Deviation, Multiple & Partial Correlation — Statistical Methods (Unit 3)
Condition Number: Multicollinearity — Econometrics (Unit 4)
Conditional Distributions: Bivariate Random Variables — Theory of Probability (Unit 3)
Conditional Expectation: Expectation, Characteristic Functions and Inequalities — Probability Theory (Unit 2)
Conditional formatting: Data Analysis and Visualization — Computer Fundamentals and Office Automation (Unit 5)
Conditional probability: Fundamentals of Probability and Basic Statistics — Statistical Foundations for Data Science (Unit 1)
Conditional Probability: Elementary Probability — Theory of Probability (Unit 1)
Conditional statements: Control Flow, Functions and Modules — Python Programming and Data Structures (Unit 2)
Conduct of Clinical Trials: Introduction to Clinical Trials — Clinical Trials (Unit 1)
Confidence Intervals: Theory of Estimation — Inferential Statistics (Unit 1)
Confidence Region: Unit VIII: Multivariate Analysis — UGC NET Statistics
configuration worth understanding: Install and configure Apache/XAMPP on the VM and host a page — Cloud Computing for Data Science (Experiment 2)
Confusion Matrix: Practical — Data Handling using R (STS-108)
Conjugate Priors: Decision Theory, Bayes and Minimax, and Density Estimation — Estimation Theory (Unit 4)
Connected Sets: Metric Spaces, Compactness and Continuity — Mathematical Analysis (Unit 1)
Connecting to data: Data Preparation and Visualization with Power BI — Business Intelligence Tools (Unit 2)
Connotation and Denotation: Unit VI: Logical Reasoning — UGC NET Paper I
Consequences: Autocorrelation — Econometrics (Unit 5) · Multicollinearity — Econometrics (Unit 4) · Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
Conservatism: What Accounting Is: Concepts, Conventions and the Rules of Debit and Credit — Financial Accounting (Unit 1)
Consistency: Theory of Attributes — Statistical Methods (Unit 5) · Theory of Estimation — Inferential Statistics (Unit 1)
Constant Force: Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1)
Constraint Satisfaction Problems: Informed and Advanced Search Strategies — Artificial Intelligence (Unit 3)
Construction: Life Tables, Fertility & Population Growth — Applied Statistics (Unit 5)
Construction Issues: Index Numbers — Applied Statistics (Unit 3)
Construction Rules: Network Scheduling (CPM & PERT) — Optimization Techniques (Unit 5)
Consumer's Risk: Acceptance Sampling for Attributes — Statistical Quality Control (Unit 4)
container contract: Deploy a trained ML model as a REST API endpoint — Cloud Computing for Data Science (Experiment 15)
Containerization: Model Deployment and CI/CD Pipelines — Data Engineering and MLOps (Unit 4)
Contingency Tables: Descriptive Statistics in R — R Programming (Unit 2)
Continuity: Metric Spaces, Compactness and Continuity — Mathematical Analysis (Unit 1)
Continuous Annuity: Life Annuities & Premiums — Actuarial Statistics (Unit 5)
Continuous delivery using PaaS: Introduction to Cloud Computing — Cloud Computing for Data Science (Unit 1)
Continuous Distributions: Introductory Statistics, Insurance & Utility — Actuarial Statistics (Unit 1)
Continuous Life Annuity: Life Annuities — Advanced Actuarial Statistics (Unit 3)
Continuous Reserves: Policy Reserves — Advanced Actuarial Statistics (Unit 5)
Continuous Response Variables: Determination of Sample Size — Clinical Trials (Unit 2)
Contra Entry: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
Contrast Matrix: Hotelling's T-squared, Mahalanobis D-squared, Wilks' Lambda and Discriminant Analysis — Multivariate Analysis (Unit 3)
Control Charts: Practical — Statistical Analysis using SPSS (STS-207)
Control Limits: Control Charts for Variables — Statistical Quality Control (Unit 2)
Control structures: Basics of R Programming — Data Science with R (Unit 2) · PL/SQL and Triggers — Database Management Systems (Unit 5)
Convergence in Distribution: Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
Convergence in Probability: Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
Conversion Rules: Duality & Dual Simplex — Operations Research (Unit 5)
Convex Hull: Graphical Method — Operations Research (Unit 2)
Convolution, precisely: Convolutional Neural Networks — Neural Networks and Deep Learning (Unit 3)
Copy / Paste Special: Data Processing in Excel — Computational Statistics & R Programming (Unit 2)
Copy Reading and Proof Reading: Report Writing and Presentation — Research Methodology (Unit 5)
cor(): Regression Modeling in R — R Programming (Unit 5)
cor.test(): Regression Modeling in R — R Programming (Unit 5)
Correlation: Practical — Statistical Methods using Python (STS-105)
Correlation and covariance (introduction): Fundamentals of Probability and Basic Statistics — Statistical Foundations for Data Science (Unit 1)
Correlation Distribution: Wishart Distribution, Generalized Variance and Correlation Distributions — Multivariate Analysis (Unit 2)
Correlation Matrix: Correlation, Regression & Forecasting — MS-Excel (Unit 4)
Correlation Ratio: Concurrent Deviation, Multiple & Partial Correlation — Statistical Methods (Unit 3)
Correlation Test: Large Sample Tests — Inferential Statistics (Unit 3)
Correlation vs regression: Correlation and Regression — Statistical Foundations for Data Science (Unit 4)
Correlation vs Regression: Regression — Statistical Methods (Unit 4)
Correlogram: Unit VII: Time Series — UGC NET Statistics
Cosmos DB: Connect to cloud-hosted database services (RDS, BigQuery, Cosmos DB) — Cloud Computing for Data Science (Experiment 8)
Cost-Optimum Allocation: Stratified Random Sampling — Sampling Techniques (Unit 3)
Cost-Push Inflation: Public Finance, Budgets and Deficits — Economics (Unit 4)
COUNT family: Data Visualization & Frequency Analysis — MS-Excel (Unit 2)
Counterexamples: Convergence of Sequences of Random Variables — Probability Theory (Unit 3)
Covariance: Correlation and Regression — Statistical Foundations for Data Science (Unit 4) · Correlation, Regression & Forecasting — MS-Excel (Unit 4) · Mathematical Expectation — Theory of Probability (Unit 4)
Covariance Matrix: Practical — Linear Algebra & Linear Models Conventional and using R (STS-106)
Cover and Title Page: Report Writing and Presentation — Research Methodology (Unit 5)
Cover Lines: Assignment Problem — Optimization Techniques (Unit 2)
Cox Proportional Hazards Model: Reporting and Analysis — Clinical Trials (Unit 4)
CPI & WPI: Index Numbers — Applied Statistics (Unit 3)
CPM: Network Scheduling (CPM & PERT) — Optimization Techniques (Unit 5)
CPU: Computer Basics — Computational Statistics & R Programming (Unit 1)
Cramér–Rao Inequality: UMVU Estimation, Cramér-Rao and Rao-Blackwell — Estimation Theory (Unit 1)
CRD Concept: Completely Randomised Design (CRD) — Design & Analysis of Experiments (Unit 2)
Create: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Create and mount: Create and configure file storage on a cloud VM (EFS) — Cloud Computing for Data Science (Experiment 6)
Creating and accessing nested JSON: JSON and jQuery — Web Technologies (Unit 5)
Creating ndarrays: NumPy Essentials — Python for Data Analysis and Visualization (Unit 1)
Credit Creation: Money, Banking and Credit Creation — Economics (Unit 3)
Credit Multiplier: Money, Banking and Credit Creation — Economics (Unit 3)
Critical Difference: Analysis of Variance (ANOVA) — Design & Analysis of Experiments (Unit 1) · Completely Randomised Design (CRD) — Design & Analysis of Experiments (Unit 2) · Latin Square Design (LSD) — Design & Analysis of Experiments (Unit 4) · Randomised Block Design (RBD) — Design & Analysis of Experiments (Unit 3)
Critical Path: Network Scheduling (CPM & PERT) — Optimization Techniques (Unit 5)
Critical Region: Testing of Hypothesis — Inferential Statistics (Unit 2)
Critical Values: Large Sample Tests — Inferential Statistics (Unit 3)
Cross Elasticity: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Cross-filter direction: Data Modeling and Relationships in BI Tools — Business Intelligence Tools (Unit 4)
Cross-over Designs: Design of Clinical Trials — Clinical Trials (Unit 3)
Cross-Section: Basic Econometrics — Econometrics (Unit 1)
Cross-sectional vs. Longitudinal: Design of Clinical Trials — Clinical Trials (Unit 3)
Cross-tabulations: Descriptive Statistics in R — R Programming (Unit 2)
Cross-Validation: Practical — Data Handling using R (STS-108)
Crowding Out: Public Finance, Budgets and Deficits — Economics (Unit 4)
CSO: National Income and the National Accounts — Economics (Unit 2) · National Statistical Office & Commission — Sampling Techniques (Unit 5)
CSV files: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
CSV, JSON, XML, HTML: Practical — Data Science using Python (STS-208)
Cumulants from Moments: Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
Current Account: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Cursor methods: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Cursors: PL/SQL and Triggers — Database Management Systems (Unit 5)
Curtate Lifetime K(x): Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1) · Survival Distribution & Life Tables — Actuarial Statistics (Unit 3)
CV: Descriptive Statistics in Excel — MS-Excel (Unit 3)
Cycling: Big-M & Two-Phase Methods — Operations Research (Unit 4)

D

d/p/q/r prefixes: Inferential Statistics & Hypothesis Testing — R Programming (Unit 4)
Dashboard components: Dashboard Design and Business Insights — Business Intelligence Tools (Unit 5)
Data & Control Structures: R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4)
Data Analysis ToolPak: Statistical Analysis in Excel — Computational Statistics & R Programming (Unit 3)
Data Analytics Life Cycle: Introduction to the Data Science Process — Data Science with R (Unit 1)
Data and Governance: Unit VII: Data Interpretation — UGC NET Paper I
Data cleaning: missing data: Data Mining and Preprocessing — Data Mining (Unit 2)
Data cleaning: noisy data: Data Mining and Preprocessing — Data Mining (Unit 2)
Data connection and preparation: Preparation, Visualization and Storytelling with Tableau — Business Intelligence Tools (Unit 3)
Data Definition Language: Structured Query Language — Database Management Systems (Unit 4)
Data engineering and data science: Foundations of Data Engineering — Data Engineering and MLOps (Unit 1)
Data Entry & Editing: Data Processing in Excel — Computational Statistics & R Programming (Unit 2)
Data Entry & Formatting: Excel Basics for Data Analysis — MS-Excel (Unit 1)
Data format validation: Client-Side Scripting — Web Technologies (Unit 4)
Data Frames: Basics of R for Statistical Data Handling — R Programming (Unit 1) · Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5)
Data governance: Data Modeling and Relationships in BI Tools — Business Intelligence Tools (Unit 4)
Data handling: Spreadsheet Basics — Computer Fundamentals and Office Automation (Unit 4)
Data Import / Export: Basics of R for Statistical Data Handling — R Programming (Unit 1)
Data input and output: Basics of R Programming — Data Science with R (Unit 2)
Data Interpretation: Model MCQs — UGC NET Paper I (General Paper)
Data Management: Introduction to Clinical Trials — Clinical Trials (Unit 1)
Data Manipulation Language: Structured Query Language — Database Management Systems (Unit 4)
Data mining tasks: Data Mining and Preprocessing — Data Mining (Unit 2)
Data model design best practices: Data Modeling and Relationships in BI Tools — Business Intelligence Tools (Unit 4)
Data modeling: the multidimensional model: Data Warehousing and OLAP — Data Mining (Unit 1)
Data models: Overview of Database Management Systems — Database Management Systems (Unit 1)
Data pre-processing: Model Preparation, Evaluation and Feature Engineering — Machine Learning (Unit 2)
Data preprocessing: Data Mining and Preprocessing — Data Mining (Unit 2)
Data Processing: Processing, Data Analysis and Interpretation — Research Methodology (Unit 3)
Data representation: Fundamentals of Probability and Basic Statistics — Statistical Foundations for Data Science (Unit 1)
Data science in various fields: Introduction to the Data Science Process — Data Science with R (Unit 1)
data science toolkit: Introduction to the Data Science Process — Data Science with R (Unit 1)
data scientist and the data science team: Introduction to the Data Science Process — Data Science with R (Unit 1)
Data transformation: Data Mining and Preprocessing — Data Mining (Unit 2)
Data types: Basics of R Programming — Data Science with R (Unit 2) · Introduction to Computer Programming — Problem Solving Using C (Unit 1) · NumPy Essentials — Python for Data Analysis and Visualization (Unit 1) · Structured Query Language — Database Management Systems (Unit 4)
Data Types: Demand Analysis — Applied Statistics II (Unit 3)
Data types and literals: Basics of Python Programming — Python Programming and Data Structures (Unit 1)
Data validation: Data Analysis and Visualization — Computer Fundamentals and Office Automation (Unit 5)
Data Validation: Excel Basics for Data Analysis — MS-Excel (Unit 1)
Data, information and database: Overview of Database Management Systems — Database Management Systems (Unit 1)
Database and collection management: MongoDB Architecture, Data Modeling and Basics — Document Oriented Database (Unit 2)
Database languages: Overview of Database Management Systems — Database Management Systems (Unit 1)
Database systems versus data warehouses: Data Warehousing and OLAP — Data Mining (Unit 1)
DataFrame: Pandas Basics and Data Structures — Python for Data Analysis and Visualization (Unit 2)
Dates and times: Data Handling and Visualization in R — Data Science with R (Unit 3)
DAX: Data Preparation and Visualization with Power BI — Business Intelligence Tools (Unit 2)
DBSCAN: Clustering Techniques — Data Mining (Unit 5) · Unsupervised Learning — Machine Learning (Unit 5)
De Moivre: Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1)
De Moivre–Laplace: Laws of Large Numbers and Central Limit Theorems — Probability Theory (Unit 4)
Decision making: Control Statements — Problem Solving Using C (Unit 2)
Decision Support Systems: Introduction to BI and Decision Support Systems — Business Intelligence Tools (Unit 1)
Decision trees: Classification — Data Mining (Unit 4)
Deduction and Induction: Unit VI: Logical Reasoning — UGC NET Paper I
Defects per Unit: Control Charts for Attributes — Statistical Quality Control (Unit 3)
Deferred: Life Annuities — Advanced Actuarial Statistics (Unit 3)
Deferred Annuity: Life Annuities & Premiums — Actuarial Statistics (Unit 5)
Deferred Insurance: Life-Insurance Benefits — Advanced Actuarial Statistics (Unit 2)
Deficit Financing: Public Finance, Budgets and Deficits — Economics (Unit 4)
Definition and scope of cloud computing: Introduction to Cloud Computing — Cloud Computing for Data Science (Unit 1)
definitions: Data Architecture and Distributed Systems — Data Engineering and MLOps (Unit 2)
Definitions: Statistical Description of Data — Descriptive Statistics (Unit 1)
Deflating: Index Numbers (Advanced) — Applied Statistics II (Unit 2)
Degeneracy: Big-M & Two-Phase Methods — Operations Research (Unit 4) · Transportation Problem — Optimization Techniques (Unit 1)
Delete: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Demand: Demand Analysis — Applied Statistics II (Unit 3)
Demand and Supply: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Demand-Pull Inflation: Public Finance, Budgets and Deficits — Economics (Unit 4)
Deploy: Deploy a trained ML model as a REST API endpoint — Cloud Computing for Data Science (Experiment 15)
Deployment strategies: Model Deployment and CI/CD Pipelines — Data Engineering and MLOps (Unit 4)
Depreciation: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6) · National Income and the National Accounts — Economics (Unit 2)
Depth First Search: Problem Solving — State Space and Uninformed Search — Artificial Intelligence (Unit 2)
describe(): Descriptive Statistics in R — R Programming (Unit 2)
Descriptive Measures: Unit VII: Time Series — UGC NET Statistics
Descriptive Statistics Review: Processing, Data Analysis and Interpretation — Research Methodology (Unit 3)
Descriptives: Practical — Statistical Analysis using SPSS (STS-207)
Deseasonalisation: Seasonal Component — Applied Statistics (Unit 2)
Design Effect: Cluster Sampling: the Intra-Cluster Correlation, the Design Effect and Optimum Cluster Size — Sampling Theory (Unit 3)
Design with Surrogate Endpoints: Surrogate End Points and Meta-Analysis — Clinical Trials (Unit 5)
Designing for failure: Data Architecture and Distributed Systems — Data Engineering and MLOps (Unit 2)
Detailed Balance: Unit IX: Stochastic Processes — UGC NET Statistics
Detection: Multicollinearity — Econometrics (Unit 4) · Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
Detrending: Growth Curves — Applied Statistics II (Unit 1)
Devaluation: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
DHTML: JavaScript — Web Technologies (Unit 3)
Diagnostic checking: ARMA and Forecasting — Time Series Analysis and Forecasting (Unit 2)
Dialog boxes: Client-Side Scripting — Web Technologies (Unit 4)
Dichotomous Response Variables: Determination of Sample Size — Clinical Trials (Unit 2)
Dichotomy Algebra: Theory of Attributes — Statistical Methods (Unit 5)
Dictionaries: Sequences, Sets and Mapping Types — Python Programming and Data Structures (Unit 3)
Difference Estimator: Ratio and Regression Estimators: Exact Bias, the Difference Estimator, Separate and Combined — Sampling Theory (Unit 2)
Difference of Means: Large Sample Tests — Inferential Statistics (Unit 3)
Difference of Proportions: Large Sample Tests — Inferential Statistics (Unit 3)
Digital Initiatives in Higher Education: Unit VIII: Information and Communication Technology — UGC NET Paper I
Dimensional modeling: Data Modeling and Relationships in BI Tools — Business Intelligence Tools (Unit 4)
Dimensionality reduction: Data Mining and Preprocessing — Data Mining (Unit 2)
Direct & Indirect Methods: Vital Statistics — Applied Statistics (Unit 4)
Direct and Indirect Taxes: Public Finance, Budgets and Deficits — Economics (Unit 4)
Disaster Management: Unit IX: People, Development and Environment — UGC NET Paper I
Discount Factor v: Life Insurance — Actuarial Statistics (Unit 4)
Discrete Annuity: Life Annuities & Premiums — Actuarial Statistics (Unit 5)
Discrete Distributions: Introductory Statistics, Insurance & Utility — Actuarial Statistics (Unit 1)
Discrete Reserves: Policy Reserves — Advanced Actuarial Statistics (Unit 5)
Discrete Uniform: Uniform, Bernoulli & Binomial — Discrete Distributions (Unit 1)
Discrete vs Continuous: Unit I: Probability and Distributions — UGC NET Statistics · Univariate Random Variables — Theory of Probability (Unit 2)
Discretization and binarization: Data Mining and Preprocessing — Data Mining (Unit 2)
Discriminant Analysis: Practical — Multivariate Analysis, Conventional (STS-205 Section B)
Disguised Unemployment: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Dispersion: Descriptive Statistics in Excel — MS-Excel (Unit 3)
Dispersion Matrix: Multinomial and Multivariate Normal Distributions — Multivariate Analysis (Unit 1)
Distribution Fitting: Practical — Statistical Methods using Python (STS-105)
Distribution Function: Measure Theory and Probability as a Measure — Probability Theory (Unit 1)
Distribution of Order Statistics: Unit IV: Estimation Theory — UGC NET Statistics
Distribution of Runs: Non-parametric Tests — Inferential Statistics (Unit 5)
Distribution of s²: Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Disturbance Assumptions: Autocorrelation — Econometrics (Unit 5)
Dockerfile: Containerize an ML model with Docker — Data Engineering and MLOps (Experiment 10)
Document model design patterns: Data Modelling and Aggregation — Document Oriented Database (Unit 4)
DOM: Client-Side Scripting — Web Technologies (Unit 4)
Dominance Property: Game Theory — Optimization Techniques (Unit 4)
Doolittle LU: Practical — Linear Algebra & Linear Models Conventional and using R (STS-106)
Double Counting: National Income and the National Accounts — Economics (Unit 2)
Drawings: Journal and Ledger: the Accounting Process Worked End to End — Financial Accounting (Unit 2)
Drift detection: Monitoring, Feedback Loops and Governance — Data Engineering and MLOps (Unit 5)
Dropping entries: Pandas Basics and Data Structures — Python for Data Analysis and Visualization (Unit 2)
Dual Simplex Method: Duality & Dual Simplex — Operations Research (Unit 5)
Dummy and indicator variables: String Operations and Feature Engineering — Python for Data Analysis and Visualization (Unit 4)
Duncan's Multiple Range: Two-Way ANOVA with Several Observations per Cell, Multiple Comparisons and ANCOVA — Design and Analysis of Experiments (Unit 1)
Duplicate indexes: Pandas Basics and Data Structures — Python for Data Analysis and Visualization (Unit 2)
Durbin–Watson: Autocorrelation — Econometrics (Unit 5)
Durbin–Watson Statistic: Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
Dynamic Itemset Counting (DIC): Association Analysis — Data Mining (Unit 3)
Dynamic memory management: Dynamic Memory, Structures, Unions and Files — Problem Solving Using C (Unit 5)

E

E-governance: Unit VIII: Information and Communication Technology — UGC NET Paper I
E-mail: Unit VIII: Information and Communication Technology — UGC NET Paper I
Econometric Model: Basic Econometrics — Econometrics (Unit 1)
Economic Interpretation: Duality & Dual Simplex — Operations Research (Unit 5)
Education Commissions: Unit X: Higher Education System — UGC NET Paper I
Effective Revenue Deficit: Public Finance, Budgets and Deficits — Economics (Unit 4)
Efficiency: Theory of Estimation — Inferential Statistics (Unit 1)
Efficiency Comparison: Practical — Sampling Theory, Conventional (STS-206 Section B)
Efficiency LSD vs CRD: Missing Values & Efficiency Comparisons — Design & Analysis of Experiments (Unit 5)
Efficiency LSD vs RBD: Missing Values & Efficiency Comparisons — Design & Analysis of Experiments (Unit 5)
Efficiency RBD vs CRD: Missing Values & Efficiency Comparisons — Design & Analysis of Experiments (Unit 5)
Element and evaluation operators: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Elements and Types of Evaluation: Unit I: Teaching Aptitude — UGC NET Paper I
Eliminating Wrong Options: Unit III: Comprehension — UGC NET Paper I
Embedded and normalized models: Data Modelling and Aggregation — Document Oriented Database (Unit 4)
Embedded versus referenced: MongoDB Architecture, Data Modeling and Basics — Document Oriented Database (Unit 2)
Empirical Distribution Function (EDF): Unit IV: Estimation Theory — UGC NET Statistics
Empirical Relation: Measures of Central Tendency — Descriptive Statistics (Unit 3)
Enabling TLS: Install and configure Apache/XAMPP on the VM and host a page — Cloud Computing for Data Science (Experiment 2)
Encryption: Create and manage storage buckets; upload and access datasets — Cloud Computing for Data Science (Experiment 4)
End of Year of Death (discrete): Life Insurance — Actuarial Statistics (Unit 4)
Endowment: Life-Insurance Benefits — Advanced Actuarial Statistics (Unit 2)
Endowment Insurance: Life Insurance — Actuarial Statistics (Unit 4)
Energy Resources: Unit IX: People, Development and Environment — UGC NET Paper I
Engel's Curve / Law: Demand Analysis — Applied Statistics II (Unit 3)
Enhanced ER (EER) model: The Entity-Relationship Model — Database Management Systems (Unit 2)
Enumerating All Samples: Simple Random Sampling — Sampling Techniques (Unit 2)
Environment (Protection) Act 1986: Unit IX: People, Development and Environment — UGC NET Paper I
Environment properties: Introduction to AI and Intelligent Agents — Artificial Intelligence (Unit 1)
Equal Clusters of size M: Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Equal n: Analysis of Variance (ANOVA) — Design & Analysis of Experiments (Unit 1)
Equation of Exchange: Money, Banking and Credit Creation — Economics (Unit 3)
Equilibrium Price: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Equivalence Principle: Life Annuities & Premiums — Actuarial Statistics (Unit 5) · Net Premiums — Advanced Actuarial Statistics (Unit 4)
equivalents, when the exam asks: Create and configure a cloud account (AWS/Azure/GCP free tier) — Cloud Computing for Data Science (Experiment 3)
Ergodic States: Unit IX: Stochastic Processes — UGC NET Statistics
Ergodicity: Unit VII: Time Series — UGC NET Statistics
Error of Commission: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Error of Omission: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Error of Principle: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Error Variance: Test Reliability & Validity — Applied Statistics II (Unit 5)
Errors and Omissions: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Errors of Measurement: Heteroscedasticity — Econometrics (Unit 3)
Estimability: Linear Models: Estimability, Gauss-Markov and Aitken — Linear Algebra & Linear Models (Unit 4)
Estimate of Mean: Systematic, Cluster & Multistage Sampling — Sampling Techniques (Unit 4)
Estimating ρ: Autocorrelation — Econometrics (Unit 5)
Estimation: ARMA and Forecasting — Time Series Analysis and Forecasting (Unit 2) · Statistical Inference, Estimation and Hypothesis Testing — Statistical Foundations for Data Science (Unit 5)
Estimation Theory: Model MCQs — UGC NET Statistics (Code 107)
Estimator of Mean: Stratified Random Sampling — Sampling Techniques (Unit 3)
Estimator of Variance: Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Estimator vs Estimate: Theory of Estimation — Inferential Statistics (Unit 1)
Estimators & Variance: Simple Random Sampling — Sampling Techniques (Unit 2)
Ethical considerations: Information Extraction and Representation — Natural Language Processing (NLP) (Unit 3)
Ethical issues in data science: Applications and Case Studies — Data Science with R (Unit 4)
Ethics of Clinical Trials: Introduction to Clinical Trials — Clinical Trials (Unit 1)
ETL Pipeline: Practical — Data Science using Python (STS-208)
Euler's Summation: The Riemann-Stieltjes Integral — Mathematical Analysis (Unit 2)
Evaluating a clustering: Unsupervised Learning — Machine Learning (Unit 5)
Evaluating models: Model Preparation, Evaluation and Feature Engineering — Machine Learning (Unit 2)
Evaluation: An FAQ chatbot on transformer embeddings — Natural Language Processing (NLP) (Experiment 14) · Extractive and abstractive summarization with Hugging Face — Natural Language Processing (NLP) (Experiment 13)
Evaluation in CBCS: Unit I: Teaching Aptitude — UGC NET Paper I
Event handling: Abstract Data Structures and GUI Programming — Python Programming and Data Structures (Unit 5)
Event-driven architecture: Data Architecture and Distributed Systems — Data Engineering and MLOps (Unit 2)
Events: Client-Side Scripting — Web Technologies (Unit 4) · Elementary Probability — Theory of Probability (Unit 1)
Events & Activities: Network Scheduling (CPM & PERT) — Optimization Techniques (Unit 5)
evolution of cloud computing: Introduction to Cloud Computing — Cloud Computing for Data Science (Unit 1)
Evolution of computers: Number Systems, Evolution, Block Diagram and Generations — Computer Fundamentals and Office Automation (Unit 1)
evolution of the role: Foundations of Data Engineering — Data Engineering and MLOps (Unit 1)
Exact Size: Randomized Tests and the Complete Neyman–Pearson Lemma — Testing of Hypotheses (Unit 1)
Exception handling: JavaScript — Web Technologies (Unit 3) · PL/SQL and Triggers — Database Management Systems (Unit 5)
Existence: Randomized Tests and the Complete Neyman–Pearson Lemma — Testing of Hypotheses (Unit 1)
Existence & Uniqueness: Unit IX: Stochastic Processes — UGC NET Statistics
Existence Conditions: Bounded Variation and Integrals Depending on a Parameter — Mathematical Analysis (Unit 3)
Expectation: Mathematical Expectation — Theory of Probability (Unit 4)
Expectation as an Integral: Expectation, Characteristic Functions and Inequalities — Probability Theory (Unit 2)
Expected Mean Squares: Analysis of Variance (ANOVA) — Design & Analysis of Experiments (Unit 1)
Expected Utility Criterion: Introductory Statistics, Insurance & Utility — Actuarial Statistics (Unit 1)
Expected-value Principle: Premium Principles & Individual Risk Models — Actuarial Statistics (Unit 2)
Expenditure Method: National Income and the National Accounts — Economics (Unit 2)
Expenses: Policy Reserves — Advanced Actuarial Statistics (Unit 5)
Experiment 1: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 10: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 11: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 12: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 2: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 3: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 4: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 5: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 6: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 7: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 8: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experiment 9: Statistical Foundations for Data Science — Lab — Excel / PSPP walkthroughs
Experimentation tracking: MLOps Fundamentals — Data Engineering and MLOps (Unit 3)
experiments: Data Science with R — Lab overview — R and the Python equivalents
Expert systems: Expert Systems, Probabilistic and Emerging AI — Artificial Intelligence (Unit 5)
explain(): Advanced Query Processing and Optimization — Document Oriented Database (Unit 5)
explainability report: Use cloud AutoML services for a dataset prediction task — Cloud Computing for Data Science (Experiment 14)
Explained & Unexplained: Regression — Statistical Methods (Unit 4)
Exploratory Data Analysis: Introduction to the Data Science Process — Data Science with R (Unit 1)
Exponential Curves: Curve Fitting — Statistical Methods (Unit 1)
Exponential distribution: Probability Distributions — Statistical Foundations for Data Science (Unit 3)
Exponential Family: Transformations, Truncated, Mixture and Compound Distributions — Distribution Theory (Unit 2) · Unit IV: Estimation Theory — UGC NET Statistics
Exponential Principle: Premium Principles & Individual Risk Models — Actuarial Statistics (Unit 2)
Exponential Trend: Time Series — Applied Statistics (Unit 1)
Export to Word / PowerPoint: Data Processing in Excel — Computational Statistics & R Programming (Unit 2)
Extremes: Quadratic Forms and Order Statistics — Distribution Theory (Unit 4)

F

F Distribution: Sampling Distributions: Chi-Square, t and F — Distribution Theory (Unit 3)
F-Distribution: Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
F-test: Analysis of Variance (ANOVA) — Design & Analysis of Experiments (Unit 1) · Small Sample Tests — Inferential Statistics (Unit 4)
F-Test: Hypothesis Testing in Excel — MS-Excel (Unit 5)
F-test for overall significance: Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
F-test for two variances: Which Statistical Test Should I Use?
Fact tables and dimension tables: Data Warehousing and OLAP — Data Mining (Unit 1)
Factor Analysis: Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis — Multivariate Analysis (Unit 4)
Factor Model: Practical — Multivariate Analysis, Conventional (STS-205 Section B)
Factorial ANOVA: Practical — Design and Analysis of Experiments, Conventional (STS-206 Section A)
Factorization Theorem: Theory of Estimation — Inferential Statistics (Unit 1)
Factors & Tables: Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5)
Factors Affecting Teaching: Unit I: Teaching Aptitude — UGC NET Paper I
Factors for selecting cloud ML platforms: Training and Deployment of ML on the Cloud — Cloud Computing for Data Science (Unit 5)
Fancy indexing: NumPy Essentials — Python for Data Analysis and Visualization (Unit 1)
Fault tolerance: Hadoop Distributed File System and YARN — Big Data Technologies (Unit 2)
Feature engineering: Model Preparation, Evaluation and Feature Engineering — Machine Learning (Unit 2) · String Operations and Feature Engineering — Python for Data Analysis and Visualization (Unit 4)
Feature engineering and data transformation: Introduction to the Data Science Process — Data Science with R (Unit 1)
Feature subset selection: Data Mining and Preprocessing — Data Mining (Unit 2) · Model Preparation, Evaluation and Feature Engineering — Machine Learning (Unit 2)
Feature transformation: Model Preparation, Evaluation and Feature Engineering — Machine Learning (Unit 2)
Features: R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4)
feedback loop and online evaluation: Monitoring, Feedback Loops and Governance — Data Engineering and MLOps (Unit 5)
Fields & Sigma-Fields: Measure Theory and Probability as a Measure — Probability Theory (Unit 1)
Fieldwork and Data Collection: Questionnaire Design and Fieldwork — Research Methodology (Unit 4)
File handling: Dynamic Memory, Structures, Unions and Files — Problem Solving Using C (Unit 5)
File modes: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
File positions: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
Files & Folders: Computer Basics — Computational Statistics & R Programming (Unit 1)
Filtering & Subsetting: R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4)
Filtering and boolean indexing: Pandas Basics and Data Structures — Python for Data Analysis and Visualization (Unit 2)
Filtering outliers: Data Input, Output and Cleaning — Python for Data Analysis and Visualization (Unit 3)
First Order Logic: Knowledge Representation and Reasoning — Artificial Intelligence (Unit 4)
Fiscal Deficit: Public Finance, Budgets and Deficits — Economics (Unit 4)
Fisher Information: Theory of Estimation — Inferential Statistics (Unit 1) · UMVU Estimation, Cramér-Rao and Rao-Blackwell — Estimation Theory (Unit 1)
Fisher Scoring: Practical — Estimation Theory, Conventional (STS-205 Section A)
Fisher's Exact Test: Reporting and Analysis — Clinical Trials (Unit 4)
Fisher's Ideal: Index Numbers — Applied Statistics (Unit 3)
Fisher's Linear Discriminant (Two Groups): Unit VIII: Multivariate Analysis — UGC NET Statistics
Fisher's LSD: Two-Way ANOVA with Several Observations per Cell, Multiple Comparisons and ANCOVA — Design and Analysis of Experiments (Unit 1)
Fisher's z: Wishart Distribution, Generalized Variance and Correlation Distributions — Multivariate Analysis (Unit 2)
Fisher-Clark Hypothesis: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Fisher–Cochran: Quadratic Forms and Order Statistics — Distribution Theory (Unit 4)
Fisher’s exact test: Which Statistical Test Should I Use?
Fitting Constants: Two-Way ANOVA with Several Observations per Cell, Multiple Comparisons and ANCOVA — Design and Analysis of Experiments (Unit 1)
Fitting Discrete Distributions: Practical — Distribution Theory Conventional and using R (STS-107)
Fitting Straight Line: Statistical Analysis in Excel — Computational Statistics & R Programming (Unit 3)
five dplyr verbs: Data Handling and Visualization in R — Data Science with R (Unit 3)
five Vs: Foundations of Big Data and the Hadoop Ecosystem — Big Data Technologies (Unit 1)
Five Year Plans: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Fixed and Floating Rates: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Fixed-Base: Index Numbers (Advanced) — Applied Statistics II (Unit 2)
Fixed-Effect Model: Surrogate End Points and Meta-Analysis — Clinical Trials (Unit 5)
float: CSS — Web Technologies (Unit 2)
Float / Slack: Network Scheduling (CPM & PERT) — Optimization Techniques (Unit 5)
Flume: Data Ingestion and Serialization — Big Data Technologies (Unit 4)
Force of Mortality: Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1)
Force of Mortality μ(x): Survival Distribution & Life Tables — Actuarial Statistics (Unit 3)
FORECAST & TREND: Statistical Analysis in Excel — Computational Statistics & R Programming (Unit 3)
Forecast accuracy measures: Forecast Evaluation and Comparison — Time Series Analysis and Forecasting (Unit 5)
FORECAST.LINEAR: Correlation, Regression & Forecasting — MS-Excel (Unit 4)
Forecasting: ARMA and Forecasting — Time Series Analysis and Forecasting (Unit 2)
forecasting process: Fundamentals and Stationary Processes — Time Series Analysis and Forecasting (Unit 1)
Forest Plots: Surrogate End Points and Meta-Analysis — Clinical Trials (Unit 5)
Formal and Informal Fallacies: Unit VI: Logical Reasoning — UGC NET Paper I
Formats of Reports: Report Writing and Presentation — Research Methodology (Unit 5)
Forms: HTML — Web Technologies (Unit 1)
Formulation: Assignment Problem — Optimization Techniques (Unit 2) · Transportation Problem — Optimization Techniques (Unit 1)
Forward and backward chaining: Knowledge Representation and Reasoning — Artificial Intelligence (Unit 4)
Forward propagation: Deep Neural Networks — Neural Networks and Deep Learning (Unit 2)
four at a glance: Sequences, Sets and Mapping Types — Python Programming and Data Structures (Unit 3)
four components: Fundamentals and Stationary Processes — Time Series Analysis and Forecasting (Unit 1)
Four Components: Time Series — Applied Statistics (Unit 1)
four deployment models: Virtualization and Deployment Models — Cloud Computing for Data Science (Unit 2)
Four Modes: Convergence of Sequences of Random Variables — Probability Theory (Unit 3)
four tests: Statistical Inference, Estimation and Hypothesis Testing — Statistical Foundations for Data Science (Unit 5)
four types of machine learning: Introduction to Machine Learning — Machine Learning (Unit 1)
four types of NoSQL database: Introduction to NoSQL and the Fundamentals of MongoDB — Document Oriented Database (Unit 1)
FP-Growth algorithm: Association Analysis — Data Mining (Unit 3)
FPT: Policy Reserves — Advanced Actuarial Statistics (Unit 5)
Fraction Defective: Control Charts for Attributes — Statistical Quality Control (Unit 3)
Fractional Age: Survival Distribution & Life Tables — Actuarial Statistics (Unit 3)
Fractional Replication: Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs — Design and Analysis of Experiments (Unit 3) · Practical — Design and Analysis of Experiments, Conventional (STS-206 Section A)
Fractions: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
free tier, honestly: Create and configure a cloud account (AWS/Azure/GCP free tier) — Cloud Computing for Data Science (Experiment 3)
Free Trade: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Free-hand Curve: Time Series — Applied Statistics (Unit 1)
FREQUENCY array: Data Visualization & Frequency Analysis — MS-Excel (Unit 2)
Frequency Distribution: Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
frequency domain: Forecast Evaluation and Comparison — Time Series Analysis and Forecasting (Unit 5)
Frequency Polygon: Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
Frequency Tables: Descriptive Statistics in R — R Programming (Unit 2) · Practical — Statistical Methods using Python (STS-105)
From ggplot2 to interactive, in one line: Advanced Topics — Data Science with R (Unit 5)
Fubini: Bounded Variation and Integrals Depending on a Parameter — Mathematical Analysis (Unit 3)
Fully Continuous Premiums: Net Premiums — Advanced Actuarial Statistics (Unit 4)
Fully Discrete Premiums: Net Premiums — Advanced Actuarial Statistics (Unit 4)
Functional dependencies: The Relational Model and Normalization — Database Management Systems (Unit 3)
Functions: Basics of R Programming — Data Science with R (Unit 2) · Control Flow, Functions and Modules — Python Programming and Data Structures (Unit 2) · JavaScript — Web Technologies (Unit 3) · Pointers, Functions and Storage Classes — Problem Solving Using C (Unit 4) · Statistical Description of Data — Descriptive Statistics (Unit 1)
Functions & Help: R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4)
Functions of Money: Money, Banking and Credit Creation — Economics (Unit 3)
Functions of r.v.: Univariate Random Variables — Theory of Probability (Unit 2)
Fundamentals of Sample Size: Determination of Sample Size — Clinical Trials (Unit 2)
Future Lifetime T(x): Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1) · Survival Distribution & Life Tables — Actuarial Statistics (Unit 3)
Fuzzy logic: Expert Systems, Probabilistic and Emerging AI — Artificial Intelligence (Unit 5)

G

g-Inverse: Practical — Linear Algebra & Linear Models Conventional and using R (STS-106)
Gamma distribution: Probability Distributions — Statistical Foundations for Data Science (Unit 3)
Gamma Distribution: Gamma & Beta Distributions — Continuous Distributions (Unit 3)
Gamma Function: Gamma & Beta Distributions — Continuous Distributions (Unit 3)
Gauss–Markov: Models and Estimation — Econometrics (Unit 2)
Gauss–Markov Theorem: Linear Models: Estimability, Gauss-Markov and Aitken — Linear Algebra & Linear Models (Unit 4)
GDP: National Income and the National Accounts — Economics (Unit 2)
GDP Deflator: National Income and the National Accounts — Economics (Unit 2)
General Linear Model: Linear Models: Estimability, Gauss-Markov and Aitken — Linear Algebra & Linear Models (Unit 4) · Models and Estimation — Econometrics (Unit 2)
General Linear Process: Unit VII: Time Series — UGC NET Statistics
General LPP: Simplex Method — Operations Research (Unit 3)
general search algorithm: Problem Solving — State Space and Uninformed Search — Artificial Intelligence (Unit 2)
Generalised Interaction: Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs — Design and Analysis of Experiments (Unit 3) · Practical — Design and Analysis of Experiments, Conventional (STS-206 Section A)
Generalized association rules: Association Analysis — Data Mining (Unit 3)
Generalized Inverses: Vector Spaces, Gram-Schmidt and Generalized Inverses — Linear Algebra & Linear Models (Unit 1)
Generalized Variance: Wishart Distribution, Generalized Variance and Correlation Distributions — Multivariate Analysis (Unit 2)
Generating Form of Contrasts: Factorial Experiments Beyond Two Factors: 2^k, 3^2 and Single-Degree Components — Design and Analysis of Experiments (Unit 2)
Generating responsive messages: Client-Side Scripting — Web Technologies (Unit 4)
Generations of computers: Number Systems, Evolution, Block Diagram and Generations — Computer Fundamentals and Office Automation (Unit 1)
Generative models: Advanced and Emerging Topics — Neural Networks and Deep Learning (Unit 5)
Genetic algorithms: Informed and Advanced Search Strategies — Artificial Intelligence (Unit 3)
Geometric distribution: Probability Distributions — Statistical Foundations for Data Science (Unit 3)
Geometric Mean: Measures of Central Tendency — Descriptive Statistics (Unit 3)
Geometry managers: Abstract Data Structures and GUI Programming — Python Programming and Data Structures (Unit 5)
GFR: Life Tables, Fertility & Population Growth — Applied Statistics (Unit 5)
ggplot2 and the grammar of graphics: Data Handling and Visualization in R — Data Science with R (Unit 3)
Gini Coefficient: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Glivenko–Cantelli: Convergence of Sequences of Random Variables — Probability Theory (Unit 3)
GNP: National Income and the National Accounts — Economics (Unit 2)
Going Concern: What Accounting Is: Concepts, Conventions and the Rules of Debit and Credit — Financial Accounting (Unit 1)
Gompertz: Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1)
Gompertz / Makeham: Survival Distribution & Life Tables — Actuarial Statistics (Unit 3)
Gompertz Curve: Growth Curves — Applied Statistics II (Unit 1)
Good Clinical Practice: Introduction to Clinical Trials — Clinical Trials (Unit 1)
Goodness of Fit: Practical — Statistical Methods using Python (STS-105) · Unit V: Testing of Hypotheses — UGC NET Statistics
Governance and regulation: Monitoring, Feedback Loops and Governance — Data Engineering and MLOps (Unit 5)
Governance of Higher Education: Unit X: Higher Education System — UGC NET Paper I
GPT and generative NLP: Transformers and Modern NLP — Natural Language Processing (Unit 5)
Grafana: Logging and monitoring with Prometheus and Grafana — Data Engineering and MLOps (Experiment 15)
Grammar and context-free grammar: Text Preprocessing and Linguistic Analysis — Natural Language Processing (NLP) (Unit 2)
Gram–Schmidt: Vector Spaces, Gram-Schmidt and Generalized Inverses — Linear Algebra & Linear Models (Unit 1)
Graphical AOQL: Single Sampling Plan — Statistical Quality Control (Unit 5)
Graphical Method: Game Theory — Optimization Techniques (Unit 4)
Graphical Method Steps: Graphical Method — Operations Research (Unit 2)
Greedy Best First Search: Informed and Advanced Search Strategies — Artificial Intelligence (Unit 3)
Gross Premium: Policy Reserves — Advanced Actuarial Statistics (Unit 5)
Gross Profit: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Ground truth evaluation: Monitoring, Feedback Loops and Governance — Data Engineering and MLOps (Unit 5)
Group Communication: Unit IV: Communication — UGC NET Paper I
GRR / NRR: Life Tables, Fertility & Population Growth — Applied Statistics (Unit 5)
GRU: Recurrent Neural Networks and NLP — Neural Networks and Deep Learning (Unit 4)
Guaranteed: Life Annuities — Advanced Actuarial Statistics (Unit 3)

H

Hadamard: Quadratic Forms and Matrix Inequalities — Linear Algebra & Linear Models (Unit 3)
Hadoop architecture: Foundations of Big Data and the Hadoop Ecosystem — Big Data Technologies (Unit 1)
Hadoop ecosystem: Foundations of Big Data and the Hadoop Ecosystem — Big Data Technologies (Unit 1)
Hadoop integration with Spark: NoSQL and Ecosystem Enhancements — Big Data Technologies (Unit 5)
Handling missing data: Data Handling and Visualization in R — Data Science with R (Unit 3) · Data Input, Output and Cleaning — Python for Data Analysis and Visualization (Unit 3)
Hansen–Hurwitz: Unequal Probability Sampling: Hansen-Hurwitz, Lahiri, Horvitz-Thompson and Yates-Grundy — Sampling Theory (Unit 1)
Harmonic Mean: Gamma & Beta Distributions — Continuous Distributions (Unit 3) · Measures of Central Tendency — Descriptive Statistics (Unit 3)
HBase data model: NoSQL and Ecosystem Enhancements — Big Data Technologies (Unit 5)
HDFS architecture: Hadoop Distributed File System and YARN — Big Data Technologies (Unit 2)
HDFS file operations: Hadoop Distributed File System and YARN — Big Data Technologies (Unit 2)
Heckscher-Ohlin: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
height, width and overflow: CSS — Web Technologies (Unit 2)
Heine–Borel: Metric Spaces, Compactness and Continuity — Mathematical Analysis (Unit 1)
Helmert Transformation: Sampling Distributions: Chi-Square, t and F — Distribution Theory (Unit 3)
Heterogeneity Assessment: Surrogate End Points and Meta-Analysis — Clinical Trials (Unit 5)
Heteroscedasticity: Heteroscedasticity — Econometrics (Unit 3)
Hetvabhasas: Unit VI: Logical Reasoning — UGC NET Paper I
Heuristics: Informed and Advanced Search Strategies — Artificial Intelligence (Unit 3)
Hierarchical clustering: Clustering Techniques — Data Mining (Unit 5) · Unsupervised Learning — Machine Learning (Unit 5)
Hierarchical indexing: Wrangling, Reshaping and Visualization — Python for Data Analysis and Visualization (Unit 5)
hierarchy: MongoDB Architecture, Data Modeling and Basics — Document Oriented Database (Unit 2)
Higher Education System: Model MCQs — UGC NET Paper I (General Paper)
Hill climbing: Informed and Advanced Search Strategies — Artificial Intelligence (Unit 3)
Histogram: Data Visualization & Frequency Analysis — MS-Excel (Unit 2) · Data Visualization in R — R Programming (Unit 3) · Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
History and features of C: Introduction to Computer Programming — Problem Solving Using C (Unit 1)
Hive: MapReduce and High-Level Tools — Big Data Technologies (Unit 3)
Holder, Minkowski, Liapunov: Expectation, Characteristic Functions and Inequalities — Probability Theory (Unit 2)
Homogeneity: Unit V: Testing of Hypotheses — UGC NET Statistics
Horvitz–Thompson: Unequal Probability Sampling: Hansen-Hurwitz, Lahiri, Horvitz-Thompson and Yates-Grundy — Sampling Theory (Unit 1)
Hotelling's T²: Hotelling's T-squared, Mahalanobis D-squared, Wilks' Lambda and Discriminant Analysis — Multivariate Analysis (Unit 3) · Practical — Multivariate Analysis, Conventional (STS-205 Section B)
HTML basics: HTML — Web Technologies (Unit 1)
Hugging Face ecosystem: Deep Learning for NLP — Natural Language Processing (Unit 4)
Human Capital: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Hungarian Method: Assignment Problem — Optimization Techniques (Unit 2)
Hypothesis testing: Statistical Inference, Estimation and Hypothesis Testing — Statistical Foundations for Data Science (Unit 5)

I

ICT Abbreviations: Unit VIII: Information and Communication Technology — UGC NET Paper I
ICT in Research: Unit II: Research Aptitude — UGC NET Paper I
ID3: Classification — Data Mining (Unit 4)
Idempotent Matrices: Quadratic Forms and Order Statistics — Distribution Theory (Unit 4)
Identification: ARMA and Forecasting — Time Series Analysis and Forecasting (Unit 2)
Identifiers and naming conventions: Basics of Python Programming — Python Programming and Data Structures (Unit 1)
Identifying a seasonal model: Non-Stationary and Seasonal Models — Time Series Analysis and Forecasting (Unit 3)
Identifying Confounding: Practical — Design and Analysis of Experiments, Conventional (STS-206 Section A)
Idle Time: Sequencing Problem — Optimization Techniques (Unit 3)
IIP: Index Numbers (Advanced) — Applied Statistics II (Unit 2)
Images and multimedia: HTML — Web Technologies (Unit 1)
Images and pixels: Convolutional Neural Networks — Neural Networks and Deep Learning (Unit 3)
IMF: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Impact and Incidence: Public Finance, Budgets and Deficits — Economics (Unit 4)
Impact of data science: Introduction to the Data Science Process — Data Science with R (Unit 1)
Implications: Convergence of Sequences of Random Variables — Probability Theory (Unit 3)
Import Substitution: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Importance: Statistical Description of Data — Descriptive Statistics (Unit 1)
Important Acts: National Statistical Office & Commission — Sampling Techniques (Unit 5)
Important Consequences: Unit I: Probability and Distributions — UGC NET Statistics
Important LRTs: Unit V: Testing of Hypotheses — UGC NET Statistics
Imprest System: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
Improper Integrals: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Improving a deployed model: Training and Deployment of ML on the Cloud — Cloud Computing for Data Science (Unit 5)
Inclusion Probabilities: Unequal Probability Sampling: Hansen-Hurwitz, Lahiri, Horvitz-Thompson and Yates-Grundy — Sampling Theory (Unit 1)
Income and Expenditure: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
Income Elasticity: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Income Method: National Income and the National Accounts — Economics (Unit 2)
Independence: Bivariate Random Variables — Theory of Probability (Unit 3) · Elementary Probability — Theory of Probability (Unit 1) · Multinomial and Multivariate Normal Distributions — Multivariate Analysis (Unit 1) · Theory of Attributes — Statistical Methods (Unit 5)
Independence in r× c Contingency Table: Unit V: Testing of Hypotheses — UGC NET Statistics
Independence of Xbar and S²: Sampling Distributions: Chi-Square, t and F — Distribution Theory (Unit 3)
Independent Events: Unit I: Probability and Distributions — UGC NET Statistics
Independent two-sample t-test: Which Statistical Test Should I Use?
Index & Signature: Quadratic Forms and Matrix Inequalities — Linear Algebra & Linear Models (Unit 3)
Index objects: Pandas Basics and Data Structures — Python for Data Analysis and Visualization (Unit 2)
Index of Reliability: Test Reliability & Validity — Applied Statistics II (Unit 5)
Index types: Advanced Query Processing and Optimization — Document Oriented Database (Unit 5)
Indexing and selection: Pandas Basics and Data Structures — Python for Data Analysis and Visualization (Unit 2)
Indexing and slicing: NumPy Essentials — Python for Data Analysis and Visualization (Unit 1)
Individual Differences: Unit I: Teaching Aptitude — UGC NET Paper I
Individual Risk Model: Premium Principles & Individual Risk Models — Actuarial Statistics (Unit 2)
Infant Industry: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Infeasible: Graphical Method — Operations Research (Unit 2)
Inference: Unit III: Comprehension — UGC NET Paper I
Inference and Error in Surveys: Survey Methodology and Data Collection — Research Methodology (Unit 2)
Inferential Statistics Review: Processing, Data Analysis and Interpretation — Research Methodology (Unit 3)
Information and Communication Technology: Model MCQs — UGC NET Paper I (General Paper)
Initial Distribution: Unit IX: Stochastic Processes — UGC NET Statistics
Inner Product Spaces: Vector Spaces, Gram-Schmidt and Generalized Inverses — Linear Algebra & Linear Models (Unit 1)
Input / Output: Computer Basics — Computational Statistics & R Programming (Unit 1)
Input and output: Basics of Python Programming — Python Programming and Data Structures (Unit 1) · Introduction to Computer Programming — Problem Solving Using C (Unit 1)
Input and output devices: Basic Organization and Networking Fundamentals — Computer Fundamentals and Office Automation (Unit 2)
Input-Output Table: National Income and the National Accounts — Economics (Unit 2)
Installation and connecting: Introduction to NoSQL and the Fundamentals of MongoDB — Document Oriented Database (Unit 1)
Insurance Applications: Introductory Statistics, Insurance & Utility — Actuarial Statistics (Unit 1)
Integration by Parts: The Riemann-Stieltjes Integral — Mathematical Analysis (Unit 2)
Intelligent agents: Introduction to AI and Intelligent Agents — Artificial Intelligence (Unit 1)
Inter-cultural Communication: Unit IV: Communication — UGC NET Paper I
Inter-Sectoral Flows: National Income and the National Accounts — Economics (Unit 2)
Interaction: Two-Way ANOVA with Several Observations per Cell, Multiple Comparisons and ANCOVA — Design and Analysis of Experiments (Unit 1)
Interchange with the Integral: Sequences and Series of Functions — Mathematical Analysis (Unit 4)
Interest and Discounting: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
interface: Preparation, Visualization and Storytelling with Tableau — Business Intelligence Tools (Unit 3)
International Environmental Agreements: Unit IX: People, Development and Environment — UGC NET Paper I
Internet basics: Basic Organization and Networking Fundamentals — Computer Fundamentals and Office Automation (Unit 2)
Interpretation: Hypothesis Testing in Excel — MS-Excel (Unit 5)
Interval: Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
Intra-Block Analysis: Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs — Design and Analysis of Experiments (Unit 3)
Intra-class Correlation: Concurrent Deviation, Multiple & Partial Correlation — Statistical Methods (Unit 3)
Intra-Cluster Correlation: Cluster Sampling: the Intra-Cluster Correlation, the Design Effect and Optimum Cluster Size — Sampling Theory (Unit 3)
Intraclass Correlation: Systematic, Cluster & Multistage Sampling — Sampling Techniques (Unit 4)
Intracluster correlation ρ: Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Intranet and Extranet: Unit VIII: Information and Communication Technology — UGC NET Paper I
Introduction and features: Basics of Python Programming — Python Programming and Data Structures (Unit 1)
Introduction of supervised learning: Supervised Learning — Classification — Machine Learning (Unit 4)
Introduction to R: R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4)
Introduction to web design: HTML — Web Technologies (Unit 1)
Invariance: The Likelihood Ratio Test, Wald and Rao Score — Testing of Hypotheses (Unit 3)
Inverse Transform: Practical — Distribution Theory Conventional and using R (STS-107)
Inversion & Continuity: Expectation, Characteristic Functions and Inequalities — Probability Theory (Unit 2)
Invertibility: Unit VII: Time Series — UGC NET Statistics
IQ: Test Reliability & Validity — Applied Statistics II (Unit 5)
Irreducibility: Unit IX: Stochastic Processes — UGC NET Statistics
Issues and challenges: Data Mining and Preprocessing — Data Mining (Unit 2)

J

J-Curve: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Jackknife: Completeness, Lehmann-Scheffé, CAN and BAN, Jackknife and Bootstrap — Estimation Theory (Unit 2) · Practical — Estimation Theory, Conventional (STS-205 Section A)
Jacobian Transformations: Transformations, Truncated, Mixture and Compound Distributions — Distribution Theory (Unit 2)
Jensen: Expectation, Characteristic Functions and Inequalities — Probability Theory (Unit 2)
Johnson's Algorithm: Sequencing Problem — Optimization Techniques (Unit 3)
Joins: Structured Query Language — Database Management Systems (Unit 4)
Joins and blending: Preparation, Visualization and Storytelling with Tableau — Business Intelligence Tools (Unit 3)
Joint & Last Survivor: Life Annuities — Advanced Actuarial Statistics (Unit 3)
Joint CDF: Bivariate Random Variables — Theory of Probability (Unit 3)
Joint Distribution Function: Bivariate Random Variables — Theory of Probability (Unit 3)
Joint Life: Life-Insurance Benefits — Advanced Actuarial Statistics (Unit 2)
Joint PDF (jpdf): Bivariate Random Variables — Theory of Probability (Unit 3)
Joint PMF (jpmf): Bivariate Random Variables — Theory of Probability (Unit 3)
Joint, marginal and conditional distributions: Probability Distributions — Statistical Foundations for Data Science (Unit 3)
Jordan Decomposition: Bounded Variation and Integrals Depending on a Parameter — Mathematical Analysis (Unit 3)
Journal: Journal and Ledger: the Accounting Process Worked End to End — Financial Accounting (Unit 2)
Journal Proper: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
jQuery: JSON and jQuery — Web Technologies (Unit 5)
jQuery DOM manipulation: JSON and jQuery — Web Technologies (Unit 5)
jQuery effects and animations: JSON and jQuery — Web Technologies (Unit 5)
jQuery event handling: JSON and jQuery — Web Technologies (Unit 5)
jQuery selectors and filters: JSON and jQuery — Web Technologies (Unit 5)
JSON: JSON and jQuery — Web Technologies (Unit 5)
JSON and BSON: Introduction to NoSQL and the Fundamentals of MongoDB — Document Oriented Database (Unit 1)
Jump statements: Control Statements — Problem Solving Using C (Unit 2)

K

K-Means: Clustering Techniques — Data Mining (Unit 5) · Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis — Multivariate Analysis (Unit 4) · Unsupervised Learning — Machine Learning (Unit 5)
K-Means clustering: Applications and Case Studies — Data Science with R (Unit 4)
K-Medoids: Clustering Techniques — Data Mining (Unit 5)
k-Medoids (PAM): Unsupervised Learning — Machine Learning (Unit 5)
k-Nearest Neighbours: Supervised Learning — Classification — Machine Learning (Unit 4)
k-th Degree Polynomial: Curve Fitting — Statistical Methods (Unit 1)
Kafka: Batch against event-driven ingestion with Kafka or RabbitMQ — Data Engineering and MLOps (Experiment 4)
Kaplan-Meier Estimator: Reporting and Analysis — Clinical Trials (Unit 4)
Karl Pearson Skewness: Moments, Skewness & Kurtosis — Descriptive Statistics (Unit 5)
Karl Pearson's r: Correlation — Statistical Methods (Unit 2)
Karlin–Rubin: UMP Tests, Monotone Likelihood Ratio and Similar Regions — Testing of Hypotheses (Unit 2)
Karlin–Rubin Theorem: Unit V: Testing of Hypotheses — UGC NET Statistics
KDD versus data mining: Data Mining and Preprocessing — Data Mining (Unit 2)
Kendall's τ: Unit IV: Estimation Theory — UGC NET Statistics
Keras and TensorFlow: Deep Neural Networks — Neural Networks and Deep Learning (Unit 2)
Kernel Density Estimation: Decision Theory, Bayes and Minimax, and Density Estimation — Estimation Theory (Unit 4)
Kernels: U-Statistics, Interval Estimation and Tolerance Limits — Estimation Theory (Unit 3)
key MLOps features, in order: MLOps Fundamentals — Data Engineering and MLOps (Unit 3)
Key Properties: Unit I: Probability and Distributions — UGC NET Statistics
Key Publications & Outputs: Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Key-value databases: Cloud Storage and Data Management — Cloud Computing for Data Science (Unit 3)
Keyboard events: Client-Side Scripting — Web Technologies (Unit 4)
Keyboard shortcuts: Word Processing and Presentations — Computer Fundamentals and Office Automation (Unit 3)
Khintchine: Laws of Large Numbers and Central Limit Theorems — Probability Theory (Unit 4)
Kinds of Comprehension Question: Unit III: Comprehension — UGC NET Paper I
Kinked Demand Curve: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Knowledge representation: Knowledge Representation and Reasoning — Artificial Intelligence (Unit 4)
Knowledge-based agents: Knowledge Representation and Reasoning — Artificial Intelligence (Unit 4)
Kolmogorov SLLN: Laws of Large Numbers and Central Limit Theorems — Probability Theory (Unit 4)
Kolmogorov's Inequality: Laws of Large Numbers and Central Limit Theorems — Probability Theory (Unit 4)
Kolmogorov–Smirnov: Unit IV: Estimation Theory — UGC NET Statistics
Kruskal–Wallis test: Which Statistical Test Should I Use?
Kuder-Richardson: Test Reliability & Validity — Applied Statistics II (Unit 5)
Kurtosis: Continuous Uniform Distribution — Continuous Distributions (Unit 1) · Descriptive Statistics in Excel — MS-Excel (Unit 3) · Moments, Skewness & Kurtosis — Descriptive Statistics (Unit 5) · Univariate Random Variables — Theory of Probability (Unit 2)

L

L'Hôpital's Rule: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Lack of Fit: Practical — Linear Algebra & Linear Models Conventional and using R (STS-106)
Lack of Memory: Geometric Distribution — Discrete Distributions (Unit 4)
Lagrange's Mean Value Theorem: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Lahiri's Method: Practical — Sampling Theory, Conventional (STS-206 Section B) · Unequal Probability Sampling: Hansen-Hurwitz, Lahiri, Horvitz-Thompson and Yates-Grundy — Sampling Theory (Unit 1)
Language elements: PL/SQL and Triggers — Database Management Systems (Unit 5)
Laplace: Lognormal, Weibull, Pareto, Laplace and Cauchy — Distribution Theory (Unit 1)
Large Sample Surveys: Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Large-Sample Limits: Practical — Estimation Theory, Conventional (STS-205 Section A)
Laspeyres: Index Numbers — Applied Statistics (Unit 3)
Launch, attach, format, mount: Launch an instance and configure block storage (EBS) — Cloud Computing for Data Science (Experiment 5)
Layout: Completely Randomised Design (CRD) — Design & Analysis of Experiments (Unit 2) · Latin Square Design (LSD) — Design & Analysis of Experiments (Unit 4) · Randomised Block Design (RBD) — Design & Analysis of Experiments (Unit 3)
Layout, alignment and design: Dashboard Design and Business Insights — Business Intelligence Tools (Unit 5)
Learning in Ancient India: Unit X: Higher Education System — UGC NET Paper I
learning rate: Deep Neural Networks — Neural Networks and Deep Learning (Unit 2)
Least Squares: Curve Fitting — Statistical Methods (Unit 1) · Time Series — Applied Statistics (Unit 1)
Least-Cost Entry: Transportation Problem — Optimization Techniques (Unit 1)
Ledger Folio: Journal and Ledger: the Accounting Process Worked End to End — Financial Accounting (Unit 2)
Ledger Posting: Journal and Ledger: the Accounting Process Worked End to End — Financial Accounting (Unit 2)
Lehmann–Scheffé: Completeness, Lehmann-Scheffé, CAN and BAN, Jackknife and Bootstrap — Estimation Theory (Unit 2)
Lehmann–Scheffé Method: Unit IV: Estimation Theory — UGC NET Statistics
Leibniz's Rule: Bounded Variation and Integrals Depending on a Parameter — Mathematical Analysis (Unit 3)
Leontief: Demand Analysis — Applied Statistics II (Unit 3)
Leontief Paradox: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Letter Series: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
Levels of programming language: Introduction to Computer Programming — Problem Solving Using C (Unit 1)
Levels of Teaching: Unit I: Teaching Aptitude — UGC NET Paper I
Liapounoff CLT: Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
Liapunov: Laws of Large Numbers and Central Limit Theorems — Probability Theory (Unit 4)
Life Table: Survival Distribution & Life Tables — Actuarial Statistics (Unit 3)
Life Table Columns: Life Tables, Fertility & Population Growth — Applied Statistics (Unit 5)
Likelihood Ratio: The Likelihood Ratio Test, Wald and Rao Score — Testing of Hypotheses (Unit 3)
Limit Algebra: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Limit of Binomial: Poisson Distribution — Discrete Distributions (Unit 2)
Limit to Binomial: Hypergeometric Distribution — Discrete Distributions (Unit 5)
Limitations: Cloud Platforms for Data Science and ML — Cloud Computing for Data Science (Unit 4) · Introduction & LPP Formulation — Operations Research (Unit 1) · Statistical Description of Data — Descriptive Statistics (Unit 1)
Limiting Form: Gamma & Beta Distributions — Continuous Distributions (Unit 3)
Limiting Form to Normal: Uniform, Bernoulli & Binomial — Discrete Distributions (Unit 1)
Limiting Forms: Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Limits and Continuity: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Lindeberg–Feller: Laws of Large Numbers and Central Limit Theorems — Probability Theory (Unit 4)
Lindeberg–Lévy: Laws of Large Numbers and Central Limit Theorems — Probability Theory (Unit 4)
Lindeberg–Lévy CLT: Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
Line Charts: Unit VII: Data Interpretation — UGC NET Paper I
Linear & Quadratic Components: Factorial Experiments Beyond Two Factors: 2^k, 3^2 and Single-Degree Components — Design and Analysis of Experiments (Unit 2)
Linear Combination: Normal Distribution — Continuous Distributions (Unit 4)
Linear Discriminant Analysis: Hotelling's T-squared, Mahalanobis D-squared, Wilks' Lambda and Discriminant Analysis — Multivariate Analysis (Unit 3)
Linear Estimation, Regression & Econometrics: Model MCQs — UGC NET Statistics (Code 107)
Linear Properties: The Riemann-Stieltjes Integral — Mathematical Analysis (Unit 2)
Linear Regression: Correlation, Regression & Forecasting — MS-Excel (Unit 4)
Linear Systems: Vector Spaces, Gram-Schmidt and Generalized Inverses — Linear Algebra & Linear Models (Unit 1)
Linear vs Non-linear: Regression — Statistical Methods (Unit 4)
Linear Zero Functions: Linear Models: Estimability, Gauss-Markov and Aitken — Linear Algebra & Linear Models (Unit 4)
Link Relatives: Seasonal Component — Applied Statistics (Unit 2)
Linkages: Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis — Multivariate Analysis (Unit 4)
Linked lists: Abstract Data Structures and GUI Programming — Python Programming and Data Structures (Unit 5)
Linux (Apache directly): Install and configure Apache/XAMPP on the VM and host a page — Cloud Computing for Data Science (Experiment 2)
Linux and macOS basics: Basic Organization and Networking Fundamentals — Computer Fundamentals and Office Automation (Unit 2)
Liquidity Preference: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Liquidity Trap: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Lists: HTML — Web Technologies (Unit 1) · Sequences, Sets and Mapping Types — Python Programming and Data Structures (Unit 3)
lm(): Regression Modeling in R — R Programming (Unit 5)
Loading BigQuery: A simple ETL job: extract, transform, load into a cloud warehouse — Cloud Computing for Data Science (Experiment 12)
Loading Redshift: A simple ETL job: extract, transform, load into a cloud warehouse — Cloud Computing for Data Science (Experiment 12)
Loadings: Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis — Multivariate Analysis (Unit 4)
Local and global variables: Pointers, Functions and Storage Classes — Problem Solving Using C (Unit 4)
Local search: Informed and Advanced Search Strategies — Artificial Intelligence (Unit 3)
Log-rank Test: Reporting and Analysis — Clinical Trials (Unit 4)
Logging and monitoring frameworks: Monitoring, Feedback Loops and Governance — Data Engineering and MLOps (Unit 5)
Logical functions: Spreadsheet Basics — Computer Fundamentals and Office Automation (Unit 4)
Logical Functions: Excel Basics for Data Analysis — MS-Excel (Unit 1)
Logical operators: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Logical Reasoning: Model MCQs — UGC NET Paper I (General Paper)
Logistic & Probit: Practical — Statistical Analysis using SPSS (STS-207)
Logistic Curve: Growth Curves — Applied Statistics II (Unit 1)
Logistic regression: Supervised Learning — Regression — Machine Learning (Unit 3)
Logistic Regression: Reporting and Analysis — Clinical Trials (Unit 4)
Lognormal: Lognormal, Weibull, Pareto, Laplace and Cauchy — Distribution Theory (Unit 1)
Lognormal MLE: Practical — Distribution Theory Conventional and using R (STS-107)
Lookup functions: Spreadsheet Basics — Computer Fundamentals and Office Automation (Unit 4)
Lookup Functions: Excel Basics for Data Analysis — MS-Excel (Unit 1)
Loops: Control Flow, Functions and Modules — Python Programming and Data Structures (Unit 2) · Control Statements — Problem Solving Using C (Unit 2)
Lorenz Curve: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Loss & Risk: Decision Theory, Bayes and Minimax, and Density Estimation — Estimation Theory (Unit 4)
Loss and Risk: Sequential Analysis and Decision Theory — Testing of Hypotheses (Unit 4)
Loss functions in detail: Deep Neural Networks — Neural Networks and Deep Learning (Unit 2)
Loss functions, intuitively: Foundations of Deep Learning — Neural Networks and Deep Learning (Unit 1)
Loss-at-Issue r.v.: Net Premiums — Advanced Actuarial Statistics (Unit 4)
Lot-Quality Approach: Single Sampling Plan — Statistical Quality Control (Unit 5)
Lottery Method: Simple Random Sampling — Sampling Techniques (Unit 2)
LPP Formulation: Introduction & LPP Formulation — Operations Research (Unit 1)
LSD Concept: Latin Square Design (LSD) — Design & Analysis of Experiments (Unit 4)
LSTM: Recurrent Neural Networks and NLP — Neural Networks and Deep Learning (Unit 4)
LSTM and GRU for sequence modeling: Deep Learning for NLP — Natural Language Processing (Unit 4)
LTPD: Acceptance Sampling for Attributes — Statistical Quality Control (Unit 4)

M

m-thly Annuity: Life Annuities & Premiums — Actuarial Statistics (Unit 5)
M1 M2 M3 M4: Money, Banking and Credit Creation — Economics (Unit 3)
MA(∞) Representation: Unit VII: Time Series — UGC NET Statistics
Machine learning activities: Introduction to Machine Learning — Machine Learning (Unit 1)
Machine learning in the cloud: Cloud Platforms for Data Science and ML — Cloud Computing for Data Science (Unit 4)
Mahalanobis Distance: Unit VIII: Multivariate Analysis — UGC NET Statistics
Mahalanobis D²: Hotelling's T-squared, Mahalanobis D-squared, Wilks' Lambda and Discriminant Analysis — Multivariate Analysis (Unit 3) · Practical — Multivariate Analysis, Conventional (STS-205 Section B)
Mahalanobis Strategy: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Main Idea and Title: Unit III: Comprehension — UGC NET Paper I
Major Surveys: Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Makeham: Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1)
Making a chart honest: Wrangling, Reshaping and Visualization — Python for Data Analysis and Visualization (Unit 5)
Managed against self-hosted: Connect to cloud-hosted database services (RDS, BigQuery, Cosmos DB) — Cloud Computing for Data Science (Experiment 8)
Managed ML platforms: Cloud Platforms for Data Science and ML — Cloud Computing for Data Science (Unit 4)
Mann–Whitney U: Non-parametric Tests — Inferential Statistics (Unit 5) · Which Statistical Test Should I Use?
MapReduce programming model: MapReduce and High-Level Tools — Big Data Technologies (Unit 3)
Margin Requirements: Money, Banking and Credit Creation — Economics (Unit 3)
Marginal & Conditional: Multinomial and Multivariate Normal Distributions — Multivariate Analysis (Unit 1)
Marginal Distributions: Bivariate Random Variables — Theory of Probability (Unit 3)
Markov & Chebyshev: Expectation, Characteristic Functions and Inequalities — Probability Theory (Unit 2)
Marshall-Edgeworth: Index Numbers — Applied Statistics (Unit 3)
Marshall-Lerner Condition: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Mass Media and Society: Unit IV: Communication — UGC NET Paper I
Matching: What Accounting Is: Concepts, Conventions and the Rules of Debit and Credit — Financial Accounting (Unit 1)
Materiality: What Accounting Is: Concepts, Conventions and the Rules of Debit and Credit — Financial Accounting (Unit 1)
Mathematical & Statistical Functions: Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Mathematical and statistical functions: NumPy Essentials — Python for Data Analysis and Visualization (Unit 1)
Mathematical expectation: Random Variables, Expectation and Variance — Statistical Foundations for Data Science (Unit 2)
Mathematical functions: JavaScript — Web Technologies (Unit 3)
Mathematical Reasoning and Aptitude: Model MCQs — UGC NET Paper I (General Paper)
matplotlib: Wrangling, Reshaping and Visualization — Python for Data Analysis and Visualization (Unit 5)
Matrices: Basics of R for Statistical Data Handling — R Programming (Unit 1) · Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5) · Practical — Statistical Methods using Python (STS-105)
Matrix Creation: Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Matrix Form: Simplex Method — Operations Research (Unit 3)
Matrix Operations: Data Processing in Excel — Computational Statistics & R Programming (Unit 2) · Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Matrix Square Root: Characteristic Roots, Cayley-Hamilton and Spectral Decomposition — Linear Algebra & Linear Models (Unit 2)
Max & Min: Simplex Method — Operations Research (Unit 3)
Maximisation: Graphical Method — Operations Research (Unit 2)
Maximization AP: Assignment Problem — Optimization Techniques (Unit 2)
Maximization TP: Transportation Problem — Optimization Techniques (Unit 1)
Maximum likelihood estimation: Supervised Learning — Regression — Machine Learning (Unit 3)
Maxmin / Minimax: Game Theory — Optimization Techniques (Unit 4)
MCT, Fatou, DCT: Measure Theory and Probability as a Measure — Probability Theory (Unit 1)
MDGs and SDGs: Unit IX: People, Development and Environment — UGC NET Paper I
Mean & SD functions: Data Processing in Excel — Computational Statistics & R Programming (Unit 2)
Mean & Variance: Geometric Distribution — Discrete Distributions (Unit 4) · Hypergeometric Distribution — Discrete Distributions (Unit 5) · Negative Binomial Distribution — Discrete Distributions (Unit 3) · Poisson Distribution — Discrete Distributions (Unit 2)
Mean = Median = Mode: Normal Distribution — Continuous Distributions (Unit 4)
Mean Deviation: Continuous Uniform Distribution — Continuous Distributions (Unit 1) · Measures of Dispersion — Descriptive Statistics (Unit 4) · Univariate Random Variables — Theory of Probability (Unit 2)
Mean Value Theorems: Metric Spaces, Compactness and Continuity — Mathematical Analysis (Unit 1)
Meaning: Correlation — Statistical Methods (Unit 2)
Meaning and Process of Communication: Unit IV: Communication — UGC NET Paper I
Meaning of Research: Introduction to Research — Research Methodology (Unit 1)
Measurable Functions: Measure Theory and Probability as a Measure — Probability Theory (Unit 1)
Measure: Measure Theory and Probability as a Measure — Probability Theory (Unit 1)
Measurement: Basic Econometrics — Econometrics (Unit 1)
Measurement Scales: Practical — Data Handling using R (STS-108)
measurement: RNN vs LSTM vs GRU, twice: Recurrent Neural Networks and NLP — Neural Networks and Deep Learning (Unit 4)
Measures of central tendency: Fundamentals of Probability and Basic Statistics — Statistical Foundations for Data Science (Unit 1)
Measures of dispersion: Fundamentals of Probability and Basic Statistics — Statistical Foundations for Data Science (Unit 1)
Measures of similarity and dissimilarity: Data Mining and Preprocessing — Data Mining (Unit 2)
Measuring a search strategy: Problem Solving — State Space and Uninformed Search — Artificial Intelligence (Unit 2)
Median: Measures of Central Tendency — Descriptive Statistics (Unit 3)
Median & Mode by Graph: Measures of Central Tendency — Descriptive Statistics (Unit 3)
Median Test: Non-parametric Tests — Inferential Statistics (Unit 5)
Memory & Storage: Computer Basics — Computational Statistics & R Programming (Unit 1)
Memory and storage: Basic Organization and Networking Fundamentals — Computer Fundamentals and Office Automation (Unit 2)
Memoryless Property: Exponential Distribution — Continuous Distributions (Unit 2)
Memoryless Property of Geometric: Unit I: Probability and Distributions — UGC NET Statistics
Merging: Basics of R for Statistical Data Handling — R Programming (Unit 1) · Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5)
Merging and joining: Wrangling, Reshaping and Visualization — Python for Data Analysis and Visualization (Unit 5)
Meta-Analysis Overview: Surrogate End Points and Meta-Analysis — Clinical Trials (Unit 5)
Method of Moments: Theory of Estimation — Inferential Statistics (Unit 1)
Methods of Data Collection: Survey Methodology and Data Collection — Research Methodology (Unit 2)
Metric Spaces: Metric Spaces, Compactness and Continuity — Mathematical Analysis (Unit 1)
MGF: Generating Functions, LLN & CLT — Theory of Probability (Unit 5) · Poisson Distribution — Discrete Distributions (Unit 2)
MGF / CF / CGF: Continuous Uniform Distribution — Continuous Distributions (Unit 1) · Exponential Distribution — Continuous Distributions (Unit 2) · Normal Distribution — Continuous Distributions (Unit 4)
MGF / CF / CGF / PGF: Negative Binomial Distribution — Discrete Distributions (Unit 3)
MGF, CF, CGF, PGF: Uniform, Bernoulli & Binomial — Discrete Distributions (Unit 1)
MGF/CF/CGF/PGF: Geometric Distribution — Discrete Distributions (Unit 4)
minimal program: Abstract Data Structures and GUI Programming — Python Programming and Data Structures (Unit 5)
Minimax Rules: Decision Theory, Bayes and Minimax, and Density Estimation — Estimation Theory (Unit 4)
Minimisation: Graphical Method — Operations Research (Unit 2)
Misclassification: Hotelling's T-squared, Mahalanobis D-squared, Wilks' Lambda and Discriminant Analysis — Multivariate Analysis (Unit 3)
Missing Value: Randomised Block Design (RBD) — Design & Analysis of Experiments (Unit 3)
Missing Value in LSD: Missing Values & Efficiency Comparisons — Design & Analysis of Experiments (Unit 5)
Missing Values: Basics of R for Statistical Data Handling — R Programming (Unit 1)
Missing Values & Outliers: Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5)
Mixed Distributions: Introductory Statistics, Insurance & Utility — Actuarial Statistics (Unit 1)
Mixed Strategy: Game Theory — Optimization Techniques (Unit 4)
Mixtures: Transformations, Truncated, Mixture and Compound Distributions — Distribution Theory (Unit 2)
MLE: Theory of Estimation — Inferential Statistics (Unit 1)
MLE of μ and Σ: Multinomial and Multivariate Normal Distributions — Multivariate Analysis (Unit 1) · Practical — Multivariate Analysis, Conventional (STS-205 Section B)
MLE Properties: Completeness, Lehmann-Scheffé, CAN and BAN, Jackknife and Bootstrap — Estimation Theory (Unit 2)
Mode: Measures of Central Tendency — Descriptive Statistics (Unit 3)
Model evaluation: Applications and Case Studies — Data Science with R (Unit 4)
Model evaluation metrics: Fundamentals and Stationary Processes — Time Series Analysis and Forecasting (Unit 1)
Model representation and interpretability: Model Preparation, Evaluation and Feature Engineering — Machine Learning (Unit 2)
Model risk management: Monitoring, Feedback Loops and Governance — Data Engineering and MLOps (Unit 5)
Model Selection: Practical — Statistical Analysis using SPSS (STS-207)
Model selection and training: Model Preparation, Evaluation and Feature Engineering — Machine Learning (Unit 2)
Model selection by information criterion: ARMA and Forecasting — Time Series Analysis and Forecasting (Unit 2)
Model versioning: MLOps Fundamentals — Data Engineering and MLOps (Unit 3)
Modelling: Introduction & LPP Formulation — Operations Research (Unit 1)
Models of Communication: Unit IV: Communication — UGC NET Paper I
MODI Method: Transportation Problem — Optimization Techniques (Unit 1)
Modified Exponential: Growth Curves — Applied Statistics II (Unit 1)
Modules: Control Flow, Functions and Modules — Python Programming and Data Structures (Unit 2)
MOLS: Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs — Design and Analysis of Experiments (Unit 3)
Moment of Death (continuous): Life Insurance — Actuarial Statistics (Unit 4)
Moment-generating function (MGF): Random Variables, Expectation and Variance — Statistical Foundations for Data Science (Unit 2)
Moments: Continuous Uniform Distribution — Continuous Distributions (Unit 1) · Exponential Distribution — Continuous Distributions (Unit 2) · Normal Distribution — Continuous Distributions (Unit 4) · Practical — Statistical Methods using Python (STS-105) · Random Variables, Expectation and Variance — Statistical Foundations for Data Science (Unit 2) · Unit I: Probability and Distributions — UGC NET Statistics · Univariate Random Variables — Theory of Probability (Unit 2)
Moments & Mode: Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Moments via E: Mathematical Expectation — Theory of Probability (Unit 4)
MongoDB: Practical — Data Science using Python (STS-208)
MongoDB data types: MongoDB Architecture, Data Modeling and Basics — Document Oriented Database (Unit 2)
Monitoring a forecast in production: Forecast Evaluation and Comparison — Time Series Analysis and Forecasting (Unit 5)
Monitoring the deployed model: Deploy a trained ML model as a REST API endpoint — Cloud Computing for Data Science (Experiment 15)
Monitoring, alarms and autoscaling: Training and Deployment of ML on the Cloud — Cloud Computing for Data Science (Unit 5)
Monopolistic Competition: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Monopoly: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Monotone Functions: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Monotone Likelihood Ratio: UMP Tests, Monotone Likelihood Ratio and Similar Regions — Testing of Hypotheses (Unit 2)
Monotonic Functions: Metric Spaces, Compactness and Continuity — Mathematical Analysis (Unit 1)
Mood and Figure: Unit VI: Logical Reasoning — UGC NET Paper I
Moore–Penrose: Practical — Linear Algebra & Linear Models Conventional and using R (STS-106)
Moore–Penrose Inverse: Vector Spaces, Gram-Schmidt and Generalized Inverses — Linear Algebra & Linear Models (Unit 1)
Moral Suasion: Money, Banking and Credit Creation — Economics (Unit 3)
Most Powerful Test: Testing of Hypothesis — Inferential Statistics (Unit 2)
Motivation: Introduction to Research — Research Methodology (Unit 1)
Moving Averages: Time Series — Applied Statistics (Unit 1)
MR = MC: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
mthly Payments: Life Annuities — Advanced Actuarial Statistics (Unit 3)
Multi-center Trials: Introduction to Clinical Trials — Clinical Trials (Unit 1)
Multi-panel: Data Visualization in R — R Programming (Unit 3)
Multi-stage, if the model needs building: Containerize an ML model with Docker — Data Engineering and MLOps (Experiment 10)
Multicollinearity & VIF: Linear Models: Estimability, Gauss-Markov and Aitken — Linear Algebra & Linear Models (Unit 4)
Multidimensional Scaling: Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis — Multivariate Analysis (Unit 4)
Multinomial Distribution: Multinomial and Multivariate Normal Distributions — Multivariate Analysis (Unit 1)
Multiple & Partial Correlation: Wishart Distribution, Generalized Variance and Correlation Distributions — Multivariate Analysis (Unit 2)
Multiple Bar: Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
Multiple Correlation: Concurrent Deviation, Multiple & Partial Correlation — Statistical Methods (Unit 3) · Models and Estimation — Econometrics (Unit 2) · Unit VIII: Multivariate Analysis — UGC NET Statistics
Multiple Groups: Unit VIII: Multivariate Analysis — UGC NET Statistics
Multiple linear regression: Supervised Learning — Regression — Machine Learning (Unit 3)
Multiple linear regression (conceptual): Correlation and Regression — Statistical Foundations for Data Science (Unit 4)
Multiple regression: Applications and Case Studies — Data Science with R (Unit 4)
Multiplication Rule: Unit I: Probability and Distributions — UGC NET Statistics
Multiplication Theorem: Elementary Probability — Theory of Probability (Unit 1) · Mathematical Expectation — Theory of Probability (Unit 4)
Multiplicative Model: Time Series — Applied Statistics (Unit 1)
Multistage Sampling: Systematic, Cluster & Multistage Sampling — Sampling Techniques (Unit 4)
Multivariate: Practical — Statistical Analysis using SPSS (STS-207)
Multivariate Analysis: Model MCQs — UGC NET Statistics (Code 107)
Multivariate Normal: Multinomial and Multivariate Normal Distributions — Multivariate Analysis (Unit 1)

N

n × 2 machines: Sequencing Problem — Optimization Techniques (Unit 3)
n × 3 machines: Sequencing Problem — Optimization Techniques (Unit 3)
n × m machines: Sequencing Problem — Optimization Techniques (Unit 3)
n-step Probabilities: Unit IX: Stochastic Processes — UGC NET Statistics
Naive Bayes: Supervised Learning — Classification — Machine Learning (Unit 4)
Nalanda and Takshashila: Unit X: Higher Education System — UGC NET Paper I
Named Entity Recognition: Information Extraction and Representation — Natural Language Processing (NLP) (Unit 3)
NAPCC: Unit IX: People, Development and Environment — UGC NET Paper I
Narration: Journal and Ledger: the Accounting Process Worked End to End — Financial Accounting (Unit 2)
Narrow and Broad Money: Money, Banking and Credit Creation — Economics (Unit 3)
National Income: National Income and the National Accounts — Economics (Unit 2)
National Policies on Education: Unit X: Higher Education System — UGC NET Paper I
National Statistical Commission: National Statistical Office & Commission — Sampling Techniques (Unit 5)
National Statistical Office (NSO): National Statistical Office & Commission — Sampling Techniques (Unit 5)
Native plotly: Advanced Topics — Data Science with R (Unit 5)
Nature & Features: Introduction & LPP Formulation — Operations Research (Unit 1)
Nature of Econometrics: Basic Econometrics — Econometrics (Unit 1)
ndarray: NumPy Essentials — Python for Data Analysis and Visualization (Unit 1)
Near Money: Money, Banking and Credit Creation — Economics (Unit 3)
Nearest neighbour classifiers: Classification — Data Mining (Unit 4)
Necessity: Randomized Tests and the Complete Neyman–Pearson Lemma — Testing of Hypotheses (Unit 1)
Need for Clinical Trials: Introduction to Clinical Trials — Clinical Trials (Unit 1)
Negative binomial distribution: Probability Distributions — Statistical Foundations for Data Science (Unit 3)
NEP 2020: Unit X: Higher Education System — UGC NET Paper I
Net / Pure Premium: Premium Principles & Individual Risk Models — Actuarial Statistics (Unit 2)
Net Annual Premium: Life Annuities & Premiums — Actuarial Statistics (Unit 5)
Net Factor Income from Abroad: National Income and the National Accounts — Economics (Unit 2)
Net Indirect Taxes: National Income and the National Accounts — Economics (Unit 2)
Net Profit: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Net Single Premium: Life Insurance — Actuarial Statistics (Unit 4)
Networking fundamentals: Basic Organization and Networking Fundamentals — Computer Fundamentals and Office Automation (Unit 2)
Newey–West SE: Autocorrelation — Econometrics (Unit 5)
Neyman Allocation Derived: Stratified Random Sampling — Sampling Techniques (Unit 3)
Neyman Structure: UMP Tests, Monotone Likelihood Ratio and Similar Regions — Testing of Hypotheses (Unit 2)
Neyman–Pearson: Randomized Tests and the Complete Neyman–Pearson Lemma — Testing of Hypotheses (Unit 1) · Testing of Hypothesis — Inferential Statistics (Unit 2)
NITI Aayog: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
NLP basics: Expert Systems, Probabilistic and Emerging AI — Artificial Intelligence (Unit 5)
NNP at Factor Cost: National Income and the National Accounts — Economics (Unit 2)
Nodes & Arcs: Network Scheduling (CPM & PERT) — Optimization Techniques (Unit 5)
Nominal: Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
Non-Central Forms: Sampling Distributions: Chi-Square, t and F — Distribution Theory (Unit 3)
Non-Parametric Tests: Practical — Data Handling using R (STS-108) · Practical — Statistical Analysis using SPSS (STS-207)
Non-Response: Two-Stage Sampling, Non-Sampling Errors, Randomized Response and Small Area Estimation — Sampling Theory (Unit 4)
Non-response: Survey Methodology and Data Collection — Research Methodology (Unit 2)
Normal (Gaussian) distribution: Probability Distributions — Statistical Foundations for Data Science (Unit 3)
Normal Approximation: Premium Principles & Individual Risk Models — Actuarial Statistics (Unit 2)
Normal Confidence Limits: Practical — Estimation Theory, Conventional (STS-205 Section A)
Normal Equations Derived: Curve Fitting — Statistical Methods (Unit 1)
Normal Limit: Negative Binomial Distribution — Discrete Distributions (Unit 3) · Poisson Distribution — Discrete Distributions (Unit 2)
Normalization: Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5) · The Relational Model and Normalization — Database Management Systems (Unit 3)
Normalized Scores: Psychological & Educational Statistics — Applied Statistics II (Unit 4)
North-West Corner: Transportation Problem — Optimization Techniques (Unit 1)
Notation: Simple Random Sampling — Sampling Techniques (Unit 2)
Notation (Σ, μ, σ): Statistical Description of Data — Descriptive Statistics (Unit 1)
Notations: Theory of Attributes — Statistical Methods (Unit 5)
np Chart: Control Charts for Attributes — Statistical Quality Control (Unit 3)
NSSO: National Income and the National Accounts — Economics (Unit 2) · National Statistical Office & Commission — Sampling Techniques (Unit 5)
Null & Alternative: Testing of Hypothesis — Inferential Statistics (Unit 2)
Number Series: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
Number systems: Number Systems, Evolution, Block Diagram and Generations — Computer Fundamentals and Office Automation (Unit 1)
NumPy: Practical — Data Science using Python (STS-208)

O

Object storage, in detail: Cloud Storage and Data Management — Cloud Computing for Data Science (Unit 3)
Objectives: Introduction to Research — Research Methodology (Unit 1)
Objectives and Endpoints: Design of Clinical Trials — Clinical Trials (Unit 3)
Objects: JavaScript — Web Technologies (Unit 3)
Obsolescence: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
OC Curve: Acceptance Sampling for Attributes — Statistical Quality Control (Unit 4)
OC Function: Sequential Analysis and Decision Theory — Testing of Hypotheses (Unit 4)
Odds Ratio and Relative Risk: Reporting and Analysis — Clinical Trials (Unit 4)
Ogives: Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
OLAP cube: Data Warehousing and OLAP — Data Mining (Unit 1)
OLAP operations: Data Warehousing and OLAP — Data Mining (Unit 1)
Oligopoly: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
OLS: Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
OLS Estimation: Models and Estimation — Econometrics (Unit 2)
OLS Estimators: Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
On the VM: Set up Jupyter Notebook / Colab on a cloud VM — Cloud Computing for Data Science (Experiment 7)
One- vs Two-tailed: Testing of Hypothesis — Inferential Statistics (Unit 2)
One-dimensional arrays: Derived Data Types: Arrays and Strings — Problem Solving Using C (Unit 3)
One-sample t-test: Which Statistical Test Should I Use?
One-way ANOVA: Analysis of Variance (ANOVA) — Design & Analysis of Experiments (Unit 1) · Statistical Analysis in Excel — Computational Statistics & R Programming (Unit 3) · Which Statistical Test Should I Use?
Open and Distance Learning: Unit X: Higher Education System — UGC NET Paper I
Open Market Operations: Money, Banking and Credit Creation — Economics (Unit 3)
Opening and closing: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
Opening windows: Client-Side Scripting — Web Technologies (Unit 4)
Operating Characteristic (OC): Unit V: Testing of Hypotheses — UGC NET Statistics
Operating Systems: Computer Basics — Computational Statistics & R Programming (Unit 1)
Operators: Basics of Python Programming — Python Programming and Data Structures (Unit 1) · Basics of R Programming — Data Science with R (Unit 2) · Introduction to Computer Programming — Problem Solving Using C (Unit 1) · JavaScript — Web Technologies (Unit 3) · R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4)
Optimisers: Deep Neural Networks — Neural Networks and Deep Learning (Unit 2)
Optimum Allocation: Stratified Random Sampling — Sampling Techniques (Unit 3)
Optimum Cluster Size: Cluster Sampling: the Intra-Cluster Correlation, the Design Effect and Optimum Cluster Size — Sampling Theory (Unit 3)
Optimum Sub-Sample: Two-Stage Sampling, Non-Sampling Errors, Randomized Response and Small Area Estimation — Sampling Theory (Unit 4)
Orchestration: A simple ETL job: extract, transform, load into a cloud warehouse — Cloud Computing for Data Science (Experiment 12)
Order of Class: Theory of Attributes — Statistical Methods (Unit 5)
Order Statistics: Quadratic Forms and Order Statistics — Distribution Theory (Unit 4)
Ordinal: Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
Origin & History: Statistical Description of Data — Descriptive Statistics (Unit 1)
Origin of OR: Introduction & LPP Formulation — Operations Research (Unit 1)
Orthogonal Projection: Vector Spaces, Gram-Schmidt and Generalized Inverses — Linear Algebra & Linear Models (Unit 1)
os and pathlib: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
Outstanding and Prepaid: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Over-fitting: Practical — Data Handling using R (STS-108)
Overdraft: Bank Reconciliation Statement — Financial Accounting (Unit 4)
Overfitting and pruning: Classification — Data Mining (Unit 4)

P

p Chart: Control Charts for Attributes — Statistical Quality Control (Unit 3)
p-series: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
p-value: Statistical Analysis in Excel — Computational Statistics & R Programming (Unit 3) · Testing of Hypothesis — Inferential Statistics (Unit 2)
p-values & CIs: Inferential Statistics & Hypothesis Testing — R Programming (Unit 4)
Paasche: Index Numbers — Applied Statistics (Unit 3)
Packages: Basics of R Programming — Data Science with R (Unit 2)
Padding: Convolutional Neural Networks — Neural Networks and Deep Learning (Unit 3)
Paired Data: Basic Econometrics — Econometrics (Unit 1)
Paired Sign Test: Non-parametric Tests — Inferential Statistics (Unit 5)
Paired t-test: Small Sample Tests — Inferential Statistics (Unit 4) · Which Statistical Test Should I Use?
Pandas: Practical — Data Science using Python (STS-208)
Parabola: Curve Fitting — Statistical Methods (Unit 1)
Parallel Designs: Design of Clinical Trials — Clinical Trials (Unit 3)
Parallel Economy: Public Finance, Budgets and Deficits — Economics (Unit 4)
Parallel Tests: Test Reliability & Validity — Applied Statistics II (Unit 5)
Parameter / Statistic: Sample Survey Concepts — Sampling Techniques (Unit 1) · Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Parameter passing: Pointers, Functions and Storage Classes — Problem Solving Using C (Unit 4)
Parametric Tests: Practical — Data Handling using R (STS-108) · Practical — Statistical Analysis using SPSS (STS-207)
Pareto: Lognormal, Weibull, Pareto, Laplace and Cauchy — Distribution Theory (Unit 1)
Pareto MLE: Practical — Distribution Theory Conventional and using R (STS-107)
Pareto's Law: Demand Analysis — Applied Statistics II (Unit 3)
Park, Glejser, White: Heteroscedasticity — Econometrics (Unit 3)
Parsing: Text Preprocessing and Linguistic Analysis — Natural Language Processing (NLP) (Unit 2)
Parsing and stringifying: JSON and jQuery — Web Technologies (Unit 5)
Partial ACF (PACF): Unit VII: Time Series — UGC NET Statistics
Partial Confounding: Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs — Design and Analysis of Experiments (Unit 3)
Partial Correlation: Concurrent Deviation, Multiple & Partial Correlation — Statistical Methods (Unit 3) · Models and Estimation — Econometrics (Unit 2) · Practical — Linear Algebra & Linear Models Conventional and using R (STS-106) · Unit VIII: Multivariate Analysis — UGC NET Statistics
Partial Elasticities: Demand Analysis — Applied Statistics II (Unit 3)
Partial Sums: Growth Curves — Applied Statistics II (Unit 1)
Partition algorithm: Association Analysis — Data Mining (Unit 3)
Partition Method: Practical — Linear Algebra & Linear Models Conventional and using R (STS-106)
Partitioning and skew: MapReduce and High-Level Tools — Big Data Technologies (Unit 3)
Parts of a Report: Report Writing and Presentation — Research Methodology (Unit 5)
Pass Book: Bank Reconciliation Statement — Financial Accounting (Unit 4)
Path Analysis: Practical — Multivariate Analysis, Conventional (STS-205 Section B) · Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis — Multivariate Analysis (Unit 4)
PBIBD: Practical — Design and Analysis of Experiments, Conventional (STS-206 Section A)
PBIBD(2): PBIBD(2), Lattice and Youden Designs, and Response Surface Methodology — Design and Analysis of Experiments (Unit 4)
PDF: Continuous Uniform Distribution — Continuous Distributions (Unit 1) · Exponential Distribution — Continuous Distributions (Unit 2) · Normal Distribution — Continuous Distributions (Unit 4) · Univariate Random Variables — Theory of Probability (Unit 2)
Pearl's Vital Index: Life Tables, Fertility & Population Growth — Applied Statistics (Unit 5)
Pearson Correlation: Correlation, Regression & Forecasting — MS-Excel (Unit 4)
Pearson r: Regression Modeling in R — R Programming (Unit 5)
Pearson's correlation coefficient: Correlation and Regression — Statistical Foundations for Data Science (Unit 4)
Pearson’s r: Which Statistical Test Should I Use?
PEAS: Introduction to AI and Intelligent Agents — Artificial Intelligence (Unit 1)
People, Development and Environment: Model MCQs — UGC NET Paper I (General Paper)
Per Capita Income: National Income and the National Accounts — Economics (Unit 2)
Percentile Scores: Psychological & Educational Statistics — Applied Statistics II (Unit 4)
Perfect Competition: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Perfect Sets: Metric Spaces, Compactness and Continuity — Mathematical Analysis (Unit 1)
Perfect vs Imperfect: Multicollinearity — Econometrics (Unit 4)
Performance enhancement: Model Preparation, Evaluation and Feature Engineering — Machine Learning (Unit 2)
Periodicity: Unit IX: Stochastic Processes — UGC NET Statistics
Permutation and random sampling: String Operations and Feature Engineering — Python for Data Analysis and Visualization (Unit 4)
Personal, Real and Nominal Accounts: What Accounting Is: Concepts, Conventions and the Rules of Debit and Credit — Financial Accounting (Unit 1)
PERT: Network Scheduling (CPM & PERT) — Optimization Techniques (Unit 5)
Petty Cash: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
PGF: Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
Phase I Trial Design: Design of Clinical Trials — Clinical Trials (Unit 3)
Phase II Trial Design: Design of Clinical Trials — Clinical Trials (Unit 3)
Phase III with Sequential Stopping: Design of Clinical Trials — Clinical Trials (Unit 3)
Phase I–IV Trials: Introduction to Clinical Trials — Clinical Trials (Unit 1)
Phases of Census: Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Pie: Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
Pie Chart: Data Visualization in R — R Programming (Unit 3)
Pie Charts: Unit VII: Data Interpretation — UGC NET Paper I
Pig: MapReduce and High-Level Tools — Big Data Technologies (Unit 3)
Pigou: Demand Analysis — Applied Statistics II (Unit 3)
Pilot Testing: Questionnaire Design and Fieldwork — Research Methodology (Unit 4)
Pincer-Search algorithm: Association Analysis — Data Mining (Unit 3)
pipe: Data Handling and Visualization in R — Data Science with R (Unit 3)
Pivot tables: Data Analysis and Visualization — Computer Fundamentals and Office Automation (Unit 5)
PivotChart: Data Visualization & Frequency Analysis — MS-Excel (Unit 2)
Pivots: U-Statistics, Interval Estimation and Tolerance Limits — Estimation Theory (Unit 3)
PivotTable: Data Visualization & Frequency Analysis — MS-Excel (Unit 2)
Planning Commission: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
plot(): Data Visualization in R — R Programming (Unit 3)
Plotly: Wrangling, Reshaping and Visualization — Python for Data Analysis and Visualization (Unit 5)
PMF: Geometric Distribution — Discrete Distributions (Unit 4) · Hypergeometric Distribution — Discrete Distributions (Unit 5) · Negative Binomial Distribution — Discrete Distributions (Unit 3) · Poisson Distribution — Discrete Distributions (Unit 2) · Univariate Random Variables — Theory of Probability (Unit 2)
PMF, PDF and CDF: Random Variables, Expectation and Variance — Statistical Foundations for Data Science (Unit 2)
Pointers: Pointers, Functions and Storage Classes — Problem Solving Using C (Unit 4)
Points of Inflexion: Normal Distribution — Continuous Distributions (Unit 4)
Pointwise vs Uniform: Sequences and Series of Functions — Mathematical Analysis (Unit 4)
Poisson Approximation: Single Sampling Plan — Statistical Quality Control (Unit 5)
Poisson distribution: Probability Distributions — Statistical Foundations for Data Science (Unit 3)
Pollutants and Health: Unit IX: People, Development and Environment — UGC NET Paper I
Polynomial & Power Curves: Statistical Analysis in Excel — Computational Statistics & R Programming (Unit 3)
Polynomial regression: Supervised Learning — Regression — Machine Learning (Unit 3)
Pooled Cross-Section: Basic Econometrics — Econometrics (Unit 1)
Pooled Dispersion: Hotelling's T-squared, Mahalanobis D-squared, Wilks' Lambda and Discriminant Analysis — Multivariate Analysis (Unit 3)
Pooled Variance: Unit IV: Estimation Theory — UGC NET Statistics
Pooling: Convolutional Neural Networks — Neural Networks and Deep Learning (Unit 3)
Popular systems compared: Introduction to NoSQL and the Fundamentals of MongoDB — Document Oriented Database (Unit 1)
Population & Sample: Sample Survey Concepts — Sampling Techniques (Unit 1) · Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Population PCA: Unit VIII: Multivariate Analysis — UGC NET Statistics
Population, sample, parameter, statistic: Statistical Inference, Estimation and Hypothesis Testing — Statistical Foundations for Data Science (Unit 5)
Portfolio-Percentile: Net Premiums — Advanced Actuarial Statistics (Unit 4)
Position & Ranking: Descriptive Statistics in Excel — MS-Excel (Unit 3)
Positioning: CSS — Web Technologies (Unit 2)
Positivism and Post-positivism: Unit II: Research Aptitude — UGC NET Paper I
Poverty Gap: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Poverty Line: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Power and Significance Level: Determination of Sample Size — Clinical Trials (Unit 2)
Power BI ecosystem: Data Preparation and Visualization with Power BI — Business Intelligence Tools (Unit 2)
Power BI relationships and cardinality: Data Modeling and Relationships in BI Tools — Business Intelligence Tools (Unit 4)
Power Curve: Curve Fitting — Statistical Methods (Unit 1)
Power Functions: UMP Tests, Monotone Likelihood Ratio and Similar Regions — Testing of Hypotheses (Unit 2)
Power of a Test: Testing of Hypothesis — Inferential Statistics (Unit 2)
Power Query: Data Preparation and Visualization with Power BI — Business Intelligence Tools (Unit 2)
Power Series Family: Transformations, Truncated, Mixture and Compound Distributions — Distribution Theory (Unit 2)
PPS Selection: Practical — Sampling Theory, Conventional (STS-206 Section B)
PPSWOR: Unequal Probability Sampling: Hansen-Hurwitz, Lahiri, Horvitz-Thompson and Yates-Grundy — Sampling Theory (Unit 1)
PPSWOR — Horvitz–Thompson: Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
PPSWR: Unequal Probability Sampling: Hansen-Hurwitz, Lahiri, Horvitz-Thompson and Yates-Grundy — Sampling Theory (Unit 1)
PPSWR — Hansen–Hurwitz Estimator: Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Practical Considerations: Determination of Sample Size — Clinical Trials (Unit 2)
Pramanas: Unit VI: Logical Reasoning — UGC NET Paper I
Pre-assigned Slope: Ratio and Regression Estimators: Exact Bias, the Difference Estimator, Separate and Combined — Sampling Theory (Unit 2)
Pre-processing: Practical — Data Handling using R (STS-108)
Precautions in Interpretation: Processing, Data Analysis and Interpretation — Research Methodology (Unit 3)
predict(): Regression Modeling in R — R Programming (Unit 5)
Premium Principles: Premium Principles & Individual Risk Models — Actuarial Statistics (Unit 2)
Preparing a model for production: Model Deployment and CI/CD Pipelines — Data Engineering and MLOps (Unit 4)
Present Value Z: Life Insurance — Actuarial Statistics (Unit 4)
Present-Value r.v.: Life-Insurance Benefits — Advanced Actuarial Statistics (Unit 2)
Presentation: Statistical Description of Data — Descriptive Statistics (Unit 1)
Presentation of a Report: Report Writing and Presentation — Research Methodology (Unit 5)
Presentation tools: Word Processing and Presentations — Computer Fundamentals and Office Automation (Unit 3)
Presenter's Poise: Report Writing and Presentation — Research Methodology (Unit 5)
Pretrained models: Deep Learning for NLP — Natural Language Processing (Unit 4)
Price Elasticity: Demand Analysis — Applied Statistics II (Unit 3) · Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Primal & Dual: Duality & Dual Simplex — Operations Research (Unit 5)
Primal-Dual Relations: Duality & Dual Simplex — Operations Research (Unit 5)
Primary Data: Statistical Description of Data — Descriptive Statistics (Unit 1)
Primary Deficit: Public Finance, Budgets and Deficits — Economics (Unit 4)
Principal Component Analysis: Model Preparation, Evaluation and Feature Engineering — Machine Learning (Unit 2)
Principal Components: Practical — Multivariate Analysis, Conventional (STS-205 Section B) · Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis — Multivariate Analysis (Unit 4)
Principles: Sample Survey Concepts — Sampling Techniques (Unit 1)
Principles of effective visualization: Dashboard Design and Business Insights — Business Intelligence Tools (Unit 5)
principles of good data architecture: Data Architecture and Distributed Systems — Data Engineering and MLOps (Unit 2)
Probabilistic reasoning: Expert Systems, Probabilistic and Emerging AI — Artificial Intelligence (Unit 5)
Probability: Fundamentals of Probability and Basic Statistics — Statistical Foundations for Data Science (Unit 1)
Probability and Distributions: Model MCQs — UGC NET Statistics (Code 107)
Probability Distributions: Inferential Statistics & Hypothesis Testing — R Programming (Unit 4)
Probability Distributions (prefixes d/p/q/r ): Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Probability of Misclassification: Unit VIII: Multivariate Analysis — UGC NET Statistics
Probability of Reaching N: Unit IX: Stochastic Processes — UGC NET Statistics
Probability of Ruin (Reaching 0): Unit IX: Stochastic Processes — UGC NET Statistics
Probability Space: Elementary Probability — Theory of Probability (Unit 1) · Univariate Random Variables — Theory of Probability (Unit 2)
Probable Error: Concurrent Deviation, Multiple & Partial Correlation — Statistical Methods (Unit 3)
Procedures and functions: PL/SQL and Triggers — Database Management Systems (Unit 5)
Process Control: Introduction to SQC — Statistical Quality Control (Unit 1)
Producer's Risk: Acceptance Sampling for Attributes — Statistical Quality Control (Unit 4) · Testing of Hypothesis — Inferential Statistics (Unit 2)
Product Control: Introduction to SQC — Statistical Quality Control (Unit 1)
Productivity features: Data Analysis and Visualization — Computer Fundamentals and Office Automation (Unit 5)
Professional and Skill Education: Unit X: Higher Education System — UGC NET Paper I
Profit and Loss: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
Profit and Loss Account: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Programming Languages: Computer Basics — Computational Statistics & R Programming (Unit 1)
Programming modes: Basics of Python Programming — Python Programming and Data Structures (Unit 1)
Project Network: Network Scheduling (CPM & PERT) — Optimization Techniques (Unit 5)
Projection, sorting, limiting and skipping: Advanced Query Processing and Optimization — Document Oriented Database (Unit 5)
Prometheus configuration: Logging and monitoring with Prometheus and Grafana — Data Engineering and MLOps (Experiment 15)
proper client library: Logging and monitoring with Prometheus and Grafana — Data Engineering and MLOps (Experiment 15)
Properties: Correlation — Statistical Methods (Unit 2) · Normal Distribution — Continuous Distributions (Unit 4)
Properties of AM: Measures of Central Tendency — Descriptive Statistics (Unit 3)
Properties of CDF: Unit I: Probability and Distributions — UGC NET Statistics
Properties of E, Var, Cov: Mathematical Expectation — Theory of Probability (Unit 4)
Proportional Allocation: Stratified Random Sampling — Sampling Techniques (Unit 3)
Proportional Frequencies: Two-Way ANOVA with Several Observations per Cell, Multiple Comparisons and ANCOVA — Design and Analysis of Experiments (Unit 1)
Propositional logic: Knowledge Representation and Reasoning — Artificial Intelligence (Unit 4)
Prospective Formula: Policy Reserves — Advanced Actuarial Statistics (Unit 5)
Protection: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Provision: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
Provision for Depreciation: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
Public Debt: Public Finance, Budgets and Deficits — Economics (Unit 4)
Public Revenue: Public Finance, Budgets and Deficits — Economics (Unit 4)
Publication Bias: Surrogate End Points and Meta-Analysis — Clinical Trials (Unit 5)
Publishing: Dashboard Design and Business Insights — Business Intelligence Tools (Unit 5)
Purchase Returns: Journal and Ledger: the Accounting Process Worked End to End — Financial Accounting (Unit 2)
Purchases Book: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
Purchasing Power Parity: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Pure Endowment: Life Insurance — Actuarial Statistics (Unit 4) · Life-Insurance Benefits — Advanced Actuarial Statistics (Unit 2)
Pure Strategy: Game Theory — Optimization Techniques (Unit 4)

Q

Q-Q Plot: Data Visualization in R — R Programming (Unit 3)
Quadratic Forms: Quadratic Forms and Matrix Inequalities — Linear Algebra & Linear Models (Unit 3) · Quadratic Forms and Order Statistics — Distribution Theory (Unit 4)
Qualitative and Quantitative Methods: Unit II: Research Aptitude — UGC NET Paper I
Quality Concept: Introduction to SQC — Statistical Quality Control (Unit 1)
Quantile Intervals: U-Statistics, Interval Estimation and Tolerance Limits — Estimation Theory (Unit 3)
Quantiles: Descriptive Statistics in R — R Programming (Unit 2)
Quantitative and Qualitative Data: Unit VII: Data Interpretation — UGC NET Paper I
Quantity Theory: Money, Banking and Credit Creation — Economics (Unit 3)
Quartile Deviation: Measures of Dispersion — Descriptive Statistics (Unit 4)
Query optimization in practice: Advanced Query Processing and Optimization — Document Oriented Database (Unit 5)
Question Construction: Questionnaire Design and Fieldwork — Research Methodology (Unit 4)
Questionnaire Development: Questionnaire Design and Fieldwork — Research Methodology (Unit 4)
Questions and Answers in Surveys: Survey Methodology and Data Collection — Research Methodology (Unit 2)
Queues: Abstract Data Structures and GUI Programming — Python Programming and Data Structures (Unit 5)
Quota: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Quota Sampling: Systematic, Cluster & Multistage Sampling — Sampling Techniques (Unit 4)

R

R and RStudio: Basics of R Programming — Data Science with R (Unit 2)
R Chart: Control Charts for Variables — Statistical Quality Control (Unit 2)
R Installation: Basics of R for Statistical Data Handling — R Programming (Unit 1)
raise: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
Random Experiment: Elementary Probability — Theory of Probability (Unit 1)
Random forest: Supervised Learning — Classification — Machine Learning (Unit 4)
Random Generation: Practical — Statistical Methods using Python (STS-105)
Random number generation: NumPy Essentials — Python for Data Analysis and Visualization (Unit 1)
Random Number Generation: Practical — Distribution Theory Conventional and using R (STS-107)
Random Number Tables: Simple Random Sampling — Sampling Techniques (Unit 2)
Random Sampling: Inferential Statistics & Hypothesis Testing — R Programming (Unit 4)
Random Variable: Univariate Random Variables — Theory of Probability (Unit 2)
Random variables: Random Variables, Expectation and Variance — Statistical Foundations for Data Science (Unit 2)
Random Vectors: Multinomial and Multivariate Normal Distributions — Multivariate Analysis (Unit 1)
Random-Effects Model: Surrogate End Points and Meta-Analysis — Clinical Trials (Unit 5)
Randomized Response: Two-Stage Sampling, Non-Sampling Errors, Randomized Response and Small Area Estimation — Sampling Theory (Unit 4)
Randomized Tests: Randomized Tests and the Complete Neyman–Pearson Lemma — Testing of Hypotheses (Unit 1)
Range: Measures of Dispersion — Descriptive Statistics (Unit 4)
Rank Correlation: Correlation — Statistical Methods (Unit 2)
Rao Score Test: The Likelihood Ratio Test, Wald and Rao Score — Testing of Hypotheses (Unit 3)
Rao-Cramer: Theory of Estimation — Inferential Statistics (Unit 1)
Rao–Blackwell: UMVU Estimation, Cramér-Rao and Rao-Blackwell — Estimation Theory (Unit 1)
Ratio: Measurement Scales & Data Presentation — Descriptive Statistics (Unit 2)
Ratio Estimator: Practical — Sampling Theory, Conventional (STS-206 Section B) · Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Ratio to Moving Average: Seasonal Component — Applied Statistics (Unit 2)
Ratio to Trend: Seasonal Component — Applied Statistics (Unit 2)
Ratio, Proportion and Percentage: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
Raw Moments: Moments, Skewness & Kurtosis — Descriptive Statistics (Unit 5)
Rayleigh Quotient: Quadratic Forms and Matrix Inequalities — Linear Algebra & Linear Models (Unit 3)
RBD Concept: Randomised Block Design (RBD) — Design & Analysis of Experiments (Unit 3)
RDBMS versus NoSQL: Introduction to NoSQL and the Fundamentals of MongoDB — Document Oriented Database (Unit 1)
RDS (managed PostgreSQL/MySQL): Connect to cloud-hosted database services (RDS, BigQuery, Cosmos DB) — Cloud Computing for Data Science (Experiment 8)
Reactivity: Advanced Topics — Data Science with R (Unit 5)
Read: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Reading a Passage: Unit III: Comprehension — UGC NET Paper I
Reading a Table: Unit VII: Data Interpretation — UGC NET Paper I
Reading and changing elements: Client-Side Scripting — Web Technologies (Unit 4)
Reading and writing: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
Reading and writing JSON in JavaScript: JSON and jQuery — Web Technologies (Unit 5)
Reading and writing text data: Data Input, Output and Cleaning — Python for Data Analysis and Visualization (Unit 3)
Reading and writing widgets: Abstract Data Structures and GUI Programming — Python Programming and Data Structures (Unit 5)
Real Analysis & Matrix Algebra: Model MCQs — UGC NET Statistics (Code 107)
Real-time, serverless or batch: Deploy a trained ML model as a REST API endpoint — Cloud Computing for Data Science (Experiment 15)
Real-world use cases: Introduction to NoSQL and the Fundamentals of MongoDB — Document Oriented Database (Unit 1)
Realisation: What Accounting Is: Concepts, Conventions and the Rules of Debit and Credit — Financial Accounting (Unit 1)
Receipts and Payments: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
Recommender systems: Applications and Case Studies — Data Science with R (Unit 4)
Rectification: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Recurrence: Geometric Distribution — Discrete Distributions (Unit 4) · Negative Binomial Distribution — Discrete Distributions (Unit 3) · Poisson Distribution — Discrete Distributions (Unit 2)
Recurrence Relation: Hypergeometric Distribution — Discrete Distributions (Unit 5) · Uniform, Bernoulli & Binomial — Discrete Distributions (Unit 1)
Recurrence vs Transience: Unit IX: Stochastic Processes — UGC NET Statistics
Recursions: Life-Insurance Benefits — Advanced Actuarial Statistics (Unit 2)
Recycling: R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4)
Reducing an ER diagram to tables: The Entity-Relationship Model — Database Management Systems (Unit 2)
Reference Section: Report Writing and Presentation — Research Methodology (Unit 5)
Regional Disparity: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Regression: Practical — Statistical Methods using Python (STS-105) · Which Statistical Test Should I Use?
Regression and Correlation: Processing, Data Analysis and Interpretation — Research Methodology (Unit 3)
Regression Coefficients: Regression — Statistical Methods (Unit 4) · Wishart Distribution, Generalized Variance and Correlation Distributions — Multivariate Analysis (Unit 2)
Regression Estimator: Practical — Sampling Theory, Conventional (STS-206 Section B) · Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Regression Lines: Regression — Statistical Methods (Unit 4)
Regular expression queries: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Regular expressions: Introduction to NLP and Language Fundamentals — Natural Language Processing (Unit 1) · JavaScript — Web Technologies (Unit 3)
Regular Expressions: Practical — Data Science using Python (STS-208)
Regular expressions in Pandas: String Operations and Feature Engineering — Python for Data Analysis and Visualization (Unit 4)
Regularisation: Supervised Learning — Regression — Machine Learning (Unit 3)
Regularity Conditions: The Likelihood Ratio Test, Wald and Rao Score — Testing of Hypotheses (Unit 3) · Theory of Estimation — Inferential Statistics (Unit 1) · UMVU Estimation, Cramér-Rao and Rao-Blackwell — Estimation Theory (Unit 1)
Relational algebra: The Relational Model and Normalization — Database Management Systems (Unit 3)
relational data model: The Relational Model and Normalization — Database Management Systems (Unit 3)
Relational integrity constraints: The Relational Model and Normalization — Database Management Systems (Unit 3)
Relations: Moments, Skewness & Kurtosis — Descriptive Statistics (Unit 5)
Relationships: The Entity-Relationship Model — Database Management Systems (Unit 2)
Relative Poverty: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Reliability Concept: Test Reliability & Validity — Applied Statistics II (Unit 5)
Remedies: Multicollinearity — Econometrics (Unit 4) · Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
Removing duplicates: Data Input, Output and Cleaning — Python for Data Analysis and Visualization (Unit 3)
Renaming axis indexes: Data Input, Output and Cleaning — Python for Data Analysis and Visualization (Unit 3)
Repeated Measures Variables: Determination of Sample Size — Clinical Trials (Unit 2)
Replacing values: Data Input, Output and Cleaning — Python for Data Analysis and Visualization (Unit 3)
Replication: Advanced Query Processing and Optimization — Document Oriented Database (Unit 5)
Replication and rack awareness: Hadoop Distributed File System and YARN — Big Data Technologies (Unit 2)
Repo Rate: Money, Banking and Credit Creation — Economics (Unit 3)
Reporting Standards: Reporting and Analysis — Clinical Trials (Unit 4)
Reproducibility: MLOps Fundamentals — Data Engineering and MLOps (Unit 3)
Research Approach: Introduction to Research — Research Methodology (Unit 1)
Research Aptitude: Model MCQs — UGC NET Paper I (General Paper)
Research Ethics: Unit II: Research Aptitude — UGC NET Paper I
Research Problems: Introduction to Research — Research Methodology (Unit 1)
Reserve: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
Reshaping, transposing and swapping axes: NumPy Essentials — Python for Data Analysis and Visualization (Unit 1)
Reshaping: pivot, stack, unstack: Wrangling, Reshaping and Visualization — Python for Data Analysis and Visualization (Unit 5)
Residual Diagnostics: Regression Modeling in R — R Programming (Unit 5)
Residual Plots: Data Visualization in R — R Programming (Unit 3)
Residuals and goodness of fit: Correlation and Regression — Statistical Foundations for Data Science (Unit 4)
Resolution: Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs — Design and Analysis of Experiments (Unit 3) · Knowledge Representation and Reasoning — Artificial Intelligence (Unit 4)
Response Bias: Two-Stage Sampling, Non-Sampling Errors, Randomized Response and Small Area Estimation — Sampling Theory (Unit 4)
Response Surface: PBIBD(2), Lattice and Youden Designs, and Response Surface Methodology — Design and Analysis of Experiments (Unit 4)
Responsible AI and scaling: MLOps Fundamentals — Data Engineering and MLOps (Unit 3)
Responsible AI: fairness, measured: Monitoring, Feedback Loops and Governance — Data Engineering and MLOps (Unit 5)
Restricted AP: Assignment Problem — Optimization Techniques (Unit 2)
Retrospective Formula: Policy Reserves — Advanced Actuarial Statistics (Unit 5)
Returns Books: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
Revenue Account: Public Finance, Budgets and Deficits — Economics (Unit 4)
Revenue Deficit: Public Finance, Budgets and Deficits — Economics (Unit 4)
Reversal Tests: Index Numbers — Applied Statistics (Unit 3)
Review of Basic Distributions: Lognormal, Weibull, Pareto, Laplace and Cauchy — Distribution Theory (Unit 1)
Riemann's Condition: The Riemann-Stieltjes Integral — Mathematical Analysis (Unit 2)
Robotics: Expert Systems, Probabilistic and Emerging AI — Artificial Intelligence (Unit 5)
ROC & AUC: Practical — Data Handling using R (STS-108)
role first, then the notebook: Launch a SageMaker notebook, attach an IAM role and an S3 bucket — Cloud Computing for Data Science (Experiment 10)
role of cloud computing in data science: Virtualization and Deployment Models — Cloud Computing for Data Science (Unit 2)
Rolle's Theorem: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Root against IAM user: Create and configure a cloud account (AWS/Azure/GCP free tier) — Cloud Computing for Data Science (Experiment 3)
Rosenblatt's Estimator: Decision Theory, Bayes and Minimax, and Density Estimation — Estimation Theory (Unit 4)
Rotatability: PBIBD(2), Lattice and Youden Designs, and Response Surface Methodology — Design and Analysis of Experiments (Unit 4)
Row / Column Reduction: Assignment Problem — Optimization Techniques (Unit 2)
Row key design: NoSQL and Ecosystem Enhancements — Big Data Technologies (Unit 5)
RStudio: R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4)
Rule-based classifiers: Classification — Data Mining (Unit 4)
Rules of Debit and Credit: What Accounting Is: Concepts, Conventions and the Rules of Debit and Credit — Financial Accounting (Unit 1)
Rulon Method: Test Reliability & Validity — Applied Statistics II (Unit 5)
Run test: Which Statistical Test Should I Use?
Run Test: Non-parametric Tests — Inferential Statistics (Unit 5)
Runtime environments: Model Deployment and CI/CD Pipelines — Data Engineering and MLOps (Unit 4)
Rural-Urban Disparity: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
R²: Heteroscedasticity — Econometrics (Unit 3)
R² & Equation: Statistical Analysis in Excel — Computational Statistics & R Programming (Unit 3)
R–S Integral: The Riemann-Stieltjes Integral — Mathematical Analysis (Unit 2)

S

S Chart: Control Charts for Variables — Statistical Quality Control (Unit 2)
Saddle Point: Game Theory — Optimization Techniques (Unit 4)
SageMaker Autopilot: Use cloud AutoML services for a dataset prediction task — Cloud Computing for Data Science (Experiment 14)
SageMaker Studio / Vertex Workbench instead: Set up Jupyter Notebook / Colab on a cloud VM — Cloud Computing for Data Science (Experiment 7)
Sales Book: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
Sales Returns: Journal and Ledger: the Accounting Process Worked End to End — Financial Accounting (Unit 2)
sample data: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Sample Mean & Variance: Sampling Distributions: Chi-Square, t and F — Distribution Theory (Unit 3)
Sample Mean as Estimator of Population Mean: Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Sample Range: Quadratic Forms and Order Statistics — Distribution Theory (Unit 4)
Sample Size: Simple Random Sampling — Sampling Techniques (Unit 2)
Sample Space: Elementary Probability — Theory of Probability (Unit 1)
Sampling Distribution: Large Sample Tests — Inferential Statistics (Unit 3) · Sample Survey Concepts — Sampling Techniques (Unit 1) · Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Sampling distributions: Statistical Inference, Estimation and Hypothesis Testing — Statistical Foundations for Data Science (Unit 5)
Sampling Frames and Coverage Error: Survey Methodology and Data Collection — Research Methodology (Unit 2)
Sampling Inspection: Acceptance Sampling for Attributes — Statistical Quality Control (Unit 4)
Sampling Methods & Design of Experiments: Model MCQs — UGC NET Statistics (Code 107)
Sampling vs Non-sampling Errors: Sample Survey Concepts — Sampling Techniques (Unit 1)
Sampling without replacement: Hypergeometric Distribution — Discrete Distributions (Unit 5)
SARIMA: Non-Stationary and Seasonal Models — Time Series Analysis and Forecasting (Unit 3)
Scales of Measurement: Unit VII: Data Interpretation — UGC NET Paper I
Scaling: Practical — Multivariate Analysis, Conventional (STS-205 Section B)
Scaling of Rankings: Psychological & Educational Statistics — Applied Statistics II (Unit 4)
Scaling of Ratings: Psychological & Educational Statistics — Applied Statistics II (Unit 4)
Scaling Techniques: Questionnaire Design and Fieldwork — Research Methodology (Unit 4)
Scaling with Kubernetes: Model Deployment and CI/CD Pipelines — Data Engineering and MLOps (Unit 4)
Scatter Diagram: Correlation — Statistical Methods (Unit 2) · Statistical Analysis in Excel — Computational Statistics & R Programming (Unit 3)
Scatter Plot: Data Visualization in R — R Programming (Unit 3)
Schema design strategies: MongoDB Architecture, Data Modeling and Basics — Document Oriented Database (Unit 2)
Schema validation: MongoDB Architecture, Data Modeling and Basics — Document Oriented Database (Unit 2)
Scientific Method: Introduction & LPP Formulation — Operations Research (Unit 1)
Scope: Statistical Description of Data — Descriptive Statistics (Unit 1)
SD Principle: Premium Principles & Individual Risk Models — Actuarial Statistics (Unit 2)
SD Tests: Large Sample Tests — Inferential Statistics (Unit 3)
SDK call: Build a classification/regression model on a managed ML platform — Cloud Computing for Data Science (Experiment 11)
Seaborn: Wrangling, Reshaping and Visualization — Python for Data Analysis and Visualization (Unit 5)
Seasonal Unemployment: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Second-Order Designs: PBIBD(2), Lattice and Youden Designs, and Response Surface Methodology — Design and Analysis of Experiments (Unit 4)
Secondary Data: Statistical Description of Data — Descriptive Statistics (Unit 1)
Secret Reserve: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
Sectoral Composition: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
security group rule everyone forgets: Create and configure file storage on a cloud VM (EFS) — Cloud Computing for Data Science (Experiment 6)
SELECT: Structured Query Language — Database Management Systems (Unit 4)
Select Tables: Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1)
Selecting elements: Client-Side Scripting — Web Technologies (Unit 4)
Selectors: CSS — Web Technologies (Unit 2)
Semantic analysis: Text Preprocessing and Linguistic Analysis — Natural Language Processing (NLP) (Unit 2)
Semantic structure: HTML — Web Technologies (Unit 1)
Semi-Averages: Time Series — Applied Statistics (Unit 1)
Semi-Continuous: Net Premiums — Advanced Actuarial Statistics (Unit 4)
Separate Ratio Estimator: Ratio and Regression Estimators: Exact Bias, the Difference Estimator, Separate and Combined — Sampling Theory (Unit 2)
Separate vs Combined: Practical — Sampling Theory, Conventional (STS-206 Section B)
Sequencing: Sequencing Problem — Optimization Techniques (Unit 3)
Serialization formats: Data Ingestion and Serialization — Big Data Technologies (Unit 4)
Series: Pandas Basics and Data Structures — Python for Data Analysis and Visualization (Unit 2)
Set operations: Structured Query Language — Database Management Systems (Unit 4)
Sets: Sequences, Sets and Mapping Types — Python Programming and Data Structures (Unit 3)
seven traps, in the order people hit them: Containerize an ML model with Docker — Data Engineering and MLOps (Experiment 10)
Shapiro–Wilk test: Which Statistical Test Should I Use?
Sharding, briefly: Advanced Query Processing and Optimization — Document Oriented Database (Unit 5)
Sharing via the Power BI Service: Data Preparation and Visualization with Power BI — Business Intelligence Tools (Unit 2)
Sheppard's Correction: Moments, Skewness & Kurtosis — Descriptive Statistics (Unit 5)
Shewhart Charts: Introduction to SQC — Statistical Quality Control (Unit 1)
Shortcomings of SQL: PL/SQL and Triggers — Database Management Systems (Unit 5)
Shortest-Length Intervals: U-Statistics, Interval Estimation and Tolerance Limits — Estimation Theory (Unit 3)
Sigma Scaling (Difficulty): Psychological & Educational Statistics — Applied Statistics II (Unit 4)
Sign test: Which Statistical Test Should I Use?
Sign Test: Non-parametric Tests — Inferential Statistics (Unit 5)
Significance: Introduction to Research — Research Methodology (Unit 1)
Significance Level: Testing of Hypothesis — Inferential Statistics (Unit 2)
Significance Tests: Heteroscedasticity — Econometrics (Unit 3)
Similar Regions: UMP Tests, Monotone Likelihood Ratio and Similar Regions — Testing of Hypotheses (Unit 2)
Simple (Pearson) Correlation: Unit VIII: Multivariate Analysis — UGC NET Statistics
Simple Averages: Seasonal Component — Applied Statistics (Unit 2)
Simple Index: Index Numbers — Applied Statistics (Unit 3)
Simple Lattice: PBIBD(2), Lattice and Youden Designs, and Response Surface Methodology — Design and Analysis of Experiments (Unit 4)
Simple linear regression: Correlation and Regression — Statistical Foundations for Data Science (Unit 4) · Supervised Learning — Regression — Machine Learning (Unit 3)
Simple linear regression in R: Applications and Case Studies — Data Science with R (Unit 4)
Simple time series forecasting: Recurrent Neural Networks and NLP — Neural Networks and Deep Learning (Unit 4)
Simple visualizations: Data Preparation and Visualization with Power BI — Business Intelligence Tools (Unit 2)
Simple vs Composite: Randomized Tests and the Complete Neyman–Pearson Lemma — Testing of Hypotheses (Unit 1) · Unit V: Testing of Hypotheses — UGC NET Statistics
Simplex Algorithm: Simplex Method — Operations Research (Unit 3)
Simulated annealing: Informed and Advanced Search Strategies — Artificial Intelligence (Unit 3)
Simultaneous Equations: Big-M & Two-Phase Methods — Operations Research (Unit 4)
Simultaneous Reduction: Quadratic Forms and Matrix Inequalities — Linear Algebra & Linear Models (Unit 3)
Single Entry: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
Single Mean: Large Sample Tests — Inferential Statistics (Unit 3)
Single Proportion: Large Sample Tests — Inferential Statistics (Unit 3)
Single Sampling Plan: Single Sampling Plan — Statistical Quality Control (Unit 5)
Single Variance: Large Sample Tests — Inferential Statistics (Unit 3)
six metrics worth alarming on: Use CloudWatch/Stackdriver to monitor endpoints, set alarms and auto-scale — Cloud Computing for Data Science (Experiment 13)
six steps: Training and Deployment of ML on the Cloud — Cloud Computing for Data Science (Unit 5)
six types of virtualization: Virtualization and Deployment Models — Cloud Computing for Data Science (Unit 2)
Skewness: Continuous Uniform Distribution — Continuous Distributions (Unit 1) · Descriptive Statistics in Excel — MS-Excel (Unit 3) · Univariate Random Variables — Theory of Probability (Unit 2)
Skewness & Kurtosis: Exponential Distribution — Continuous Distributions (Unit 2) · Geometric Distribution — Discrete Distributions (Unit 4) · Negative Binomial Distribution — Discrete Distributions (Unit 3) · Normal Distribution — Continuous Distributions (Unit 4) · Poisson Distribution — Discrete Distributions (Unit 2) · Uniform, Bernoulli & Binomial — Discrete Distributions (Unit 1)
Skewness / Kurtosis: Descriptive Statistics in R — R Programming (Unit 2)
skills: Foundations of Data Engineering — Data Engineering and MLOps (Unit 1)
Slack & Surplus: Simplex Method — Operations Research (Unit 3)
Slicers: Data Analysis and Visualization — Computer Fundamentals and Office Automation (Unit 5)
SLLN: Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
SLOPE / INTERCEPT: Correlation, Regression & Forecasting — MS-Excel (Unit 4)
Slutzky's Theorem: Convergence of Sequences of Random Variables — Probability Theory (Unit 3)
Small Area Estimation: Two-Stage Sampling, Non-Sampling Errors, Randomized Response and Small Area Estimation — Sampling Theory (Unit 4)
Snapshots: Launch an instance and configure block storage (EBS) — Cloud Computing for Data Science (Experiment 5)
SOA and web services: Introduction to Cloud Computing — Cloud Computing for Data Science (Unit 1)
Software Types: Computer Basics — Computational Statistics & R Programming (Unit 1)
Solver: Excel Basics for Data Analysis — MS-Excel (Unit 1)
Sort & Filter: Data Processing in Excel — Computational Statistics & R Programming (Unit 2)
Sort / Filter / Conditional Format: Excel Basics for Data Analysis — MS-Excel (Unit 1)
Sorting & Searching: Practical — Statistical Methods using Python (STS-105)
Sorting and ranking: Pandas Basics and Data Structures — Python for Data Analysis and Visualization (Unit 2)
Sources: Vital Statistics — Applied Statistics (Unit 4)
Sources of Data: Unit VII: Data Interpretation — UGC NET Paper I
Sources of Growth: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Spark, introduced: MapReduce and High-Level Tools — Big Data Technologies (Unit 3)
Spearman Rank: Correlation, Regression & Forecasting — MS-Excel (Unit 4)
Spearman ρ: Regression Modeling in R — R Programming (Unit 5)
Spearman's rank correlation: Correlation and Regression — Statistical Foundations for Data Science (Unit 4)
Spearman's ρₛ: Unit IV: Estimation Theory — UGC NET Statistics
Spearman’s rank correlation: Which Statistical Test Should I Use?
Special Cases: Big-M & Two-Phase Methods — Operations Research (Unit 4)
Special Drawing Rights: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Special Numbers: R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4)
Specification Error: Heteroscedasticity — Econometrics (Unit 3)
Specificity and the cascade: CSS — Web Technologies (Unit 2)
Spectral Decomposition: Characteristic Roots, Cayley-Hamilton and Spectral Decomposition — Linear Algebra & Linear Models (Unit 2)
Splicing: Index Numbers (Advanced) — Applied Statistics II (Unit 2)
Split-Plot: Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs — Design and Analysis of Experiments (Unit 3) · Practical — Design and Analysis of Experiments, Conventional (STS-206 Section A)
Splitting indices: Classification — Data Mining (Unit 4)
Spot training: Build a classification/regression model on a managed ML platform — Cloud Computing for Data Science (Experiment 11)
Spreadsheet structure: Spreadsheet Basics — Computer Fundamentals and Office Automation (Unit 4)
SPRT: Sequential Analysis and Decision Theory — Testing of Hypotheses (Unit 4)
SQL & CRUD: Practical — Data Science using Python (STS-208)
SQL command categories: Structured Query Language — Database Management Systems (Unit 4)
Square of Opposition: Unit VI: Logical Reasoning — UGC NET Paper I
SRS for Attributes: Simple Random Sampling — Sampling Techniques (Unit 2)
SRSWOR: Simple Random Sampling — Sampling Techniques (Unit 2)
SRSWR: Simple Random Sampling — Sampling Techniques (Unit 2)
Stacks: Abstract Data Structures and GUI Programming — Python Programming and Data Structures (Unit 5)
Stagflation: Public Finance, Budgets and Deficits — Economics (Unit 4)
Standard Deviation: Measures of Dispersion — Descriptive Statistics (Unit 4)
Standard Error: Large Sample Tests — Inferential Statistics (Unit 3) · Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Standard Form: Simplex Method — Operations Research (Unit 3)
Standard Normal: Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Standard Scores: Psychological & Educational Statistics — Applied Statistics II (Unit 4)
Standardisation: Unit VIII: Multivariate Analysis — UGC NET Statistics
Standardised Death Rate: Vital Statistics — Applied Statistics (Unit 4)
Standards Specified: Control Charts for Variables — Statistical Quality Control (Unit 2)
Standards Unspecified: Control Charts for Variables — Statistical Quality Control (Unit 2)
Standing Order: Bank Reconciliation Statement — Financial Accounting (Unit 4)
Star and snowflake schemas: Data Modeling and Relationships in BI Tools — Business Intelligence Tools (Unit 4)
Star, snowflake and fact constellation: Data Warehousing and OLAP — Data Mining (Unit 1)
State space representation: Problem Solving — State Space and Uninformed Search — Artificial Intelligence (Unit 2)
State-space models: Multivariate and State-Space Models — Time Series Analysis and Forecasting (Unit 4)
Statement of Affairs: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
Statements: JavaScript — Web Technologies (Unit 3)
Stationarity: Advanced Topics — Data Science with R (Unit 5) · Fundamentals and Stationary Processes — Time Series Analysis and Forecasting (Unit 1) · Unit VII: Time Series — UGC NET Statistics
Statistical Analysis: Completely Randomised Design (CRD) — Design & Analysis of Experiments (Unit 2) · Latin Square Design (LSD) — Design & Analysis of Experiments (Unit 4) · Randomised Block Design (RBD) — Design & Analysis of Experiments (Unit 3)
Statistical Definition: Elementary Probability — Theory of Probability (Unit 1)
Statistical Report: Practical — Data Handling using R (STS-108)
status bar: Client-Side Scripting — Web Technologies (Unit 4)
Statutory Liquidity Ratio: Money, Banking and Credit Creation — Economics (Unit 3)
Steepest Ascent: PBIBD(2), Lattice and Youden Designs, and Response Surface Methodology — Design and Analysis of Experiments (Unit 4)
Stemming and lemmatization: Text Preprocessing and Linguistic Analysis — Natural Language Processing (NLP) (Unit 2)
Step 1: Deploy a sentiment analysis app for Swiggy reviews with Hugging Face — Neural Networks and Deep Learning (Experiment 12)
Step 2: Deploy a sentiment analysis app for Swiggy reviews with Hugging Face — Neural Networks and Deep Learning (Experiment 12)
Step 3: Deploy a sentiment analysis app for Swiggy reviews with Hugging Face — Neural Networks and Deep Learning (Experiment 12)
Step Integrators: The Riemann-Stieltjes Integral — Mathematical Analysis (Unit 2)
Steps in a Survey: Sample Survey Concepts — Sampling Techniques (Unit 1)
Steps in Empirical Analysis: Basic Econometrics — Econometrics (Unit 1)
Steps of Research: Unit II: Research Aptitude — UGC NET Paper I
Steps to create and run a PL/SQL program: PL/SQL and Triggers — Database Management Systems (Unit 5)
Stochastic Independence: Bivariate Random Variables — Theory of Probability (Unit 3)
Stochastic Processes: Model MCQs — UGC NET Statistics (Code 107)
Stone–Weierstrass: Sequences and Series of Functions — Mathematical Analysis (Unit 4)
Stopword removal: Text Preprocessing and Linguistic Analysis — Natural Language Processing (NLP) (Unit 2)
Storage classes: Pointers, Functions and Storage Classes — Problem Solving Using C (Unit 4)
Storytelling and creating a Tableau story: Preparation, Visualization and Storytelling with Tableau — Business Intelligence Tools (Unit 3)
Storytelling and insight communication: Dashboard Design and Business Insights — Business Intelligence Tools (Unit 5)
Straight Line: Curve Fitting — Statistical Methods (Unit 1)
Straight Line Method: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
Stratification: Stratified Random Sampling — Sampling Techniques (Unit 3)
Stratified Mean and Variance: Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Stratified Regression: Ratio and Regression Estimators: Exact Bias, the Difference Estimator, Separate and Combined — Sampling Theory (Unit 2)
String manipulation: JavaScript — Web Technologies (Unit 3)
Strings: Derived Data Types: Arrays and Strings — Problem Solving Using C (Unit 3) · Sequences, Sets and Mapping Types — Python Programming and Data Structures (Unit 3)
Structural Unemployment: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Structure of a C program: Introduction to Computer Programming — Problem Solving Using C (Unit 1)
Structure of a PL/SQL block: PL/SQL and Triggers — Database Management Systems (Unit 5)
Structure of Arguments: Unit VI: Logical Reasoning — UGC NET Paper I
Structure of data: Introduction to Machine Learning — Machine Learning (Unit 1)
Structures: Dynamic Memory, Structures, Unions and Files — Problem Solving Using C (Unit 5)
Student's t: Sampling Distributions: Chi-Square, t and F — Distribution Theory (Unit 3) · Standard Normal & Sampling Distributions — Continuous Distributions (Unit 5)
Styles of Referencing: Unit II: Research Aptitude — UGC NET Paper I
Subgroup Size: Control Charts for Variables — Statistical Quality Control (Unit 2)
Subqueries: Structured Query Language — Database Management Systems (Unit 4)
Subsetting: Basics of R for Statistical Data Handling — R Programming (Unit 1)
Subsidiary Books: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
Sufficiency: Theory of Estimation — Inferential Statistics (Unit 1) · UMVU Estimation, Cramér-Rao and Rao-Blackwell — Estimation Theory (Unit 1)
Sum of Random Variables: Introductory Statistics, Insurance & Utility — Actuarial Statistics (Unit 1)
Summary statistics by group: Wrangling, Reshaping and Visualization — Python for Data Analysis and Visualization (Unit 5)
summary(): Descriptive Statistics in R — R Programming (Unit 2) · Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5)
summary(lm): Regression Modeling in R — R Programming (Unit 5)
Support Vector Machines: Supervised Learning — Classification — Machine Learning (Unit 4)
Support, confidence and lift: Association Analysis — Data Mining (Unit 3)
Supremum Test: Sequences and Series of Functions — Mathematical Analysis (Unit 4)
Surrogate Endpoints: Surrogate End Points and Meta-Analysis — Clinical Trials (Unit 5)
Survival Data Concepts: Reporting and Analysis — Clinical Trials (Unit 4)
Survival Function: Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1)
Survival Function s(x): Survival Distribution & Life Tables — Actuarial Statistics (Unit 3)
Suspense Account: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
SWAYAM, SWAYAM Prabha and MOOCs: Unit I: Teaching Aptitude — UGC NET Paper I
Sweep-Out: Practical — Linear Algebra & Linear Models Conventional and using R (STS-106)
Symbols: HTML — Web Technologies (Unit 1)
Symptoms: Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
Syntax: CSS — Web Technologies (Unit 2) · Practical — Statistical Analysis using SPSS (STS-207)
Syntax errors vs exceptions: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
Syntax: lines, comments and indentation: Basics of Python Programming — Python Programming and Data Structures (Unit 1)
Systematic Sampling: Systematic, Cluster & Multistage Sampling — Sampling Techniques (Unit 4)

T

t Test Derived: The Likelihood Ratio Test, Wald and Rao Score — Testing of Hypotheses (Unit 3)
T-Scores: Psychological & Educational Statistics — Applied Statistics II (Unit 4)
t-test: Statistical Analysis in Excel — Computational Statistics & R Programming (Unit 3)
t-test (correlation): Small Sample Tests — Inferential Statistics (Unit 4)
t-test (difference): Small Sample Tests — Inferential Statistics (Unit 4)
t-Test (Paired): Hypothesis Testing in Excel — MS-Excel (Unit 5)
t-test (single mean): Small Sample Tests — Inferential Statistics (Unit 4)
t-Test (Two-Sample): Hypothesis Testing in Excel — MS-Excel (Unit 5)
t-test for individual βⱼ: Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
t-tests: Inferential Statistics & Hypothesis Testing — R Programming (Unit 4)
Tableau: Data Modeling and Relationships in BI Tools — Business Intelligence Tools (Unit 4)
Tables: HTML — Web Technologies (Unit 1)
Target Populations: Survey Methodology and Data Collection — Research Methodology (Unit 2)
Tariff: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Tax Incidence: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Taylor's Theorem: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Teacher- and Learner-centred Methods: Unit I: Teaching Aptitude — UGC NET Paper I
Teaching Aptitude: Model MCQs — UGC NET Paper I (General Paper)
Teaching Support Systems: Unit I: Teaching Aptitude — UGC NET Paper I
Techniques of Interpretation: Processing, Data Analysis and Interpretation — Research Methodology (Unit 3)
Technology selection: Data Architecture and Distributed Systems — Data Engineering and MLOps (Unit 2)
Temporary: Life Annuities — Advanced Actuarial Statistics (Unit 3)
Temporary Annuity: Life Annuities & Premiums — Actuarial Statistics (Unit 5)
Term Insurance: Life Insurance — Actuarial Statistics (Unit 4) · Life-Insurance Benefits — Advanced Actuarial Statistics (Unit 2)
terminology: Text Preprocessing and Linguistic Analysis — Natural Language Processing (NLP) (Unit 2)
Test Function: Randomized Tests and the Complete Neyman–Pearson Lemma — Testing of Hypotheses (Unit 1) · Unit V: Testing of Hypotheses — UGC NET Statistics
Test-Retest: Test Reliability & Validity — Applied Statistics II (Unit 5)
Testing of Hypotheses: Model MCQs — UGC NET Statistics (Code 107)
Testing Procedure: Large Sample Tests — Inferential Statistics (Unit 3)
Tests of Convergence: Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Tests of Hypotheses: Practical — Statistical Methods using Python (STS-105)
Text and fonts: CSS — Web Technologies (Unit 2)
text classification pipeline: Information Extraction and Representation — Natural Language Processing (NLP) (Unit 3)
Text elements: HTML — Web Technologies (Unit 1)
Text functions: Spreadsheet Basics — Computer Fundamentals and Office Automation (Unit 4)
Text generation: Recurrent Neural Networks and NLP — Neural Networks and Deep Learning (Unit 4)
Text generation with an RNN: Deep Learning for NLP — Natural Language Processing (Unit 4)
Text mining and word clouds: Applications and Case Studies — Data Science with R (Unit 4)
Text of the Report: Report Writing and Presentation — Research Methodology (Unit 5)
Text summarization: Transformers and Modern NLP — Natural Language Processing (Unit 5)
TFR: Life Tables, Fertility & Population Growth — Applied Statistics (Unit 5)
Then clean up: Create and configure a cloud account (AWS/Azure/GCP free tier) — Cloud Computing for Data Science (Experiment 3)
Then the notebook: Launch a SageMaker notebook, attach an IAM role and an S3 bucket — Cloud Computing for Data Science (Experiment 10)
Theories of Profit: Price Determination, Market Structures and Factor Incomes — Economics (Unit 1)
Thesis and Article Writing: Unit II: Research Aptitude — UGC NET Paper I
Thiele's Recursion: Policy Reserves — Advanced Actuarial Statistics (Unit 5)
three kinds of drift: Monitoring, Feedback Loops and Governance — Data Engineering and MLOps (Unit 5)
three models, in one place: ARMA and Forecasting — Time Series Analysis and Forecasting (Unit 2)
Three Principles: Completely Randomised Design (CRD) — Design & Analysis of Experiments (Unit 2)
three schedulers: Hadoop Distributed File System and YARN — Big Data Technologies (Unit 2)
Three Selected Points: Growth Curves — Applied Statistics II (Unit 1)
three service models: Introduction to Cloud Computing — Cloud Computing for Data Science (Unit 1)
three storage types: Cloud Storage and Data Management — Cloud Computing for Data Science (Unit 3)
three strategies compared: Problem Solving — State Space and Uninformed Search — Artificial Intelligence (Unit 2)
Three-Column Cash Book: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
Three-Factor Interaction: Factorial Experiments Beyond Two Factors: 2^k, 3^2 and Single-Degree Components — Design and Analysis of Experiments (Unit 2)
three-schema architecture: Overview of Database Management Systems — Database Management Systems (Unit 1)
tidyr: Data Handling and Visualization in R — Data Science with R (Unit 3)
Tied Ranks: Correlation — Statistical Methods (Unit 2)
Time and Distance: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
Time Calculations: Network Scheduling (CPM & PERT) — Optimization Techniques (Unit 5)
Time Series: Model MCQs — UGC NET Statistics (Code 107) · Time Series — Applied Statistics (Unit 1)
Time series objects in R: Advanced Topics — Data Science with R (Unit 5)
Time-Series: Basic Econometrics — Econometrics (Unit 1)
Tokenization: Text Preprocessing and Linguistic Analysis — Natural Language Processing (NLP) (Unit 2)
Tolerance: Multicollinearity — Econometrics (Unit 4)
Tolerance Limits: U-Statistics, Interval Estimation and Tolerance Limits — Estimation Theory (Unit 3)
Tone and Purpose: Unit III: Comprehension — UGC NET Paper I
Tonelli: Bounded Variation and Integrals Depending on a Parameter — Mathematical Analysis (Unit 3)
toolchain: Introduction to NLP and Language Fundamentals — Natural Language Processing (Unit 1)
ToolPak Report: Descriptive Statistics in Excel — MS-Excel (Unit 3)
Total Confounding: Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs — Design and Analysis of Experiments (Unit 3)
Total Elapsed Time: Sequencing Problem — Optimization Techniques (Unit 3)
Total Factor Productivity: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Total Probability: Elementary Probability — Theory of Probability (Unit 1)
Total Probability Theorem: Unit I: Probability and Distributions — UGC NET Statistics
Trace Method: Characteristic Roots, Cayley-Hamilton and Spectral Decomposition — Linear Algebra & Linear Models (Unit 2) · Practical — Linear Algebra & Linear Models Conventional and using R (STS-106)
Trade Discount: Subsidiary Books and the Cash Book — Financial Accounting (Unit 3)
Trading Account: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
train.py contract: Build a classification/regression model on a managed ML platform — Cloud Computing for Data Science (Experiment 11)
Transactions: Practical — Data Science using Python (STS-208)
Transactions and ACID: Overview of Database Management Systems — Database Management Systems (Unit 1)
Transactions and GridFS: Advanced Query Processing and Optimization — Document Oriented Database (Unit 5)
Transfer learning and fine-tuning: Advanced and Emerging Topics — Neural Networks and Deep Learning (Unit 5)
Transfer Payments: National Income and the National Accounts — Economics (Unit 2)
Transformations: Practical — Data Handling using R (STS-108)
Transformers: Deep Learning for NLP — Natural Language Processing (Unit 4)
Transforming with mapping and functions: Data Input, Output and Cleaning — Python for Data Analysis and Visualization (Unit 3)
Tree construction and the best split: Classification — Data Mining (Unit 4)
TREND: Correlation, Regression & Forecasting — MS-Excel (Unit 4)
Trial Balance: Trial Balance, Errors and Final Accounts — Financial Accounting (Unit 5)
Triggers: PL/SQL and Triggers — Database Management Systems (Unit 5)
Truncated Distributions: Transformations, Truncated, Mixture and Compound Distributions — Distribution Theory (Unit 2)
Truncation: Sequential Analysis and Decision Theory — Testing of Hypotheses (Unit 4)
try / except / else / finally: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
Tukey's HSD: Two-Way ANOVA with Several Observations per Cell, Multiple Comparisons and ANCOVA — Design and Analysis of Experiments (Unit 1)
Tuples: Sequences, Sets and Mapping Types — Python Programming and Data Structures (Unit 3)
Two Correlation Coefficients: Large Sample Tests — Inferential Statistics (Unit 3)
two halves of every Shiny app: Advanced Topics — Data Science with R (Unit 5)
Two versions of every experiment: Data Science with R — Lab overview — R and the Python equivalents
Two-dimensional arrays: Derived Data Types: Arrays and Strings — Problem Solving Using C (Unit 3)
Two-person Zero-sum: Game Theory — Optimization Techniques (Unit 4)
Two-Phase Method: Big-M & Two-Phase Methods — Operations Research (Unit 4)
Two-Sample T^2: Unit VIII: Multivariate Analysis — UGC NET Statistics
Two-Stage Sampling: Practical — Sampling Theory, Conventional (STS-206 Section B) · Two-Stage Sampling, Non-Sampling Errors, Randomized Response and Small Area Estimation — Sampling Theory (Unit 4)
Two-Variable Model: Models and Estimation — Econometrics (Unit 2)
Two-way ANOVA: Analysis of Variance (ANOVA) — Design & Analysis of Experiments (Unit 1) · Which Statistical Test Should I Use?
Two-Way ANOVA: Practical — Design and Analysis of Experiments, Conventional (STS-206 Section A) · Two-Way ANOVA with Several Observations per Cell, Multiple Comparisons and ANCOVA — Design and Analysis of Experiments (Unit 1)
Type 1 and type 2 hypervisors: Virtualization and Deployment Models — Cloud Computing for Data Science (Unit 2)
Type I & II Errors: Testing of Hypothesis — Inferential Statistics (Unit 2)
Type I and Type II errors: Statistical Inference, Estimation and Hypothesis Testing — Statistical Foundations for Data Science (Unit 5)
Types: Correlation — Statistical Methods (Unit 2)
Types and Characteristics: Unit IV: Communication — UGC NET Paper I
Types of AI: Introduction to AI and Intelligent Agents — Artificial Intelligence (Unit 1)
Types of computers: Basic Organization and Networking Fundamentals — Computer Fundamentals and Office Automation (Unit 2)
Types of data in machine learning: Introduction to Machine Learning — Machine Learning (Unit 1)
Types of human learning: Introduction to Machine Learning — Machine Learning (Unit 1)
Types of neural network: Foundations of Deep Learning — Neural Networks and Deep Learning (Unit 1)
Types of Reasoning: Unit V: Mathematical Reasoning and Aptitude — UGC NET Paper I
Types of Research: Introduction to Research — Research Methodology (Unit 1)
Types of Sampling: Sample Survey Concepts — Sampling Techniques (Unit 1)
Types of software: Introduction to Computer Programming — Problem Solving Using C (Unit 1)
Types of Utility Function: Introductory Statistics, Insurance & Utility — Actuarial Statistics (Unit 1)
Typing Instructions: Report Writing and Presentation — Research Methodology (Unit 5)

U

u Chart: Control Charts for Attributes — Statistical Quality Control (Unit 3)
U-Statistics: U-Statistics, Interval Estimation and Tolerance Limits — Estimation Theory (Unit 3)
UDD & Balducci: Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1)
Ultimate Frequencies: Theory of Attributes — Statistical Methods (Unit 5)
UMP Tests: UMP Tests, Monotone Likelihood Ratio and Similar Regions — Testing of Hypotheses (Unit 2)
UMPU: UMP Tests, Monotone Likelihood Ratio and Similar Regions — Testing of Hypotheses (Unit 2)
UMVU Estimation: UMVU Estimation, Cramér-Rao and Rao-Blackwell — Estimation Theory (Unit 1)
Unbalanced AP: Assignment Problem — Optimization Techniques (Unit 2)
Unbalanced TP: Transportation Problem — Optimization Techniques (Unit 1)
Unbiased Tests: UMP Tests, Monotone Likelihood Ratio and Similar Regions — Testing of Hypotheses (Unit 2)
Unbiasedness: Theory of Estimation — Inferential Statistics (Unit 1)
Unbiasedness of s²: Simple Random Sampling — Sampling Techniques (Unit 2)
Unbounded: Graphical Method — Operations Research (Unit 2)
Uncorrelated vs Independent: Mathematical Expectation — Theory of Probability (Unit 4)
Uncredited Cheques: Bank Reconciliation Statement — Financial Accounting (Unit 4)
Unequal Cluster Sizes: Cluster Sampling: the Intra-Cluster Correlation, the Design Effect and Optimum Cluster Size — Sampling Theory (Unit 3)
Unequal Clusters: Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Unequal n: Analysis of Variance (ANOVA) — Design & Analysis of Experiments (Unit 1)
UNIANOVA: Practical — Statistical Analysis using SPSS (STS-207)
Uniform Continuity: Metric Spaces, Compactness and Continuity — Mathematical Analysis (Unit 1) · Unit II: Real Analysis & Matrix Algebra — UGC NET Statistics
Uniform Cost Search: Problem Solving — State Space and Uninformed Search — Artificial Intelligence (Unit 2)
Uniform distribution: Probability Distributions — Statistical Foundations for Data Science (Unit 3)
Uniform Limit of Continuous Functions: Sequences and Series of Functions — Mathematical Analysis (Unit 4)
Unions: Dynamic Memory, Structures, Unions and Files — Problem Solving Using C (Unit 5)
Unit roots: Non-Stationary and Seasonal Models — Time Series Analysis and Forecasting (Unit 3)
Universal functions: NumPy Essentials — Python for Data Analysis and Visualization (Unit 1)
Unpresented Cheques: Bank Reconciliation Statement — Financial Accounting (Unit 4)
Unrelated Question: Two-Stage Sampling, Non-Sampling Errors, Randomized Response and Small Area Estimation — Sampling Theory (Unit 4)
Unsupervised versus supervised learning: Unsupervised Learning — Machine Learning (Unit 5)
Update: CRUD Operations and Querying — Document Oriented Database (Unit 3)
Use cases: Cloud Storage and Data Management — Cloud Computing for Data Science (Unit 3) · Foundations of Big Data and the Hadoop Ecosystem — Big Data Technologies (Unit 1)
User-defined exceptions: File Handling, Exception Handling and OOP — Python Programming and Data Structures (Unit 4)
Uses: Life Tables, Fertility & Population Growth — Applied Statistics (Unit 5) · Vital Statistics — Applied Statistics (Unit 4)
Usual and Current Status: The Indian Economy: Poverty, Unemployment, Inequality and Planning — Economics (Unit 6)
Utility Theory: Introductory Statistics, Insurance & Utility — Actuarial Statistics (Unit 1)

V

Validation of Surrogates: Surrogate End Points and Meta-Analysis — Clinical Trials (Unit 5)
Validity: Test Reliability & Validity — Applied Statistics II (Unit 5)
Value Added: National Income and the National Accounts — Economics (Unit 2)
Value and Environmental Education: Unit X: Higher Education System — UGC NET Paper I
VAM: Transportation Problem — Optimization Techniques (Unit 1)
vanishing and exploding gradient: Recurrent Neural Networks and NLP — Neural Networks and Deep Learning (Unit 4)
Variability: Descriptive Statistics in R — R Programming (Unit 2) · Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5)
Variable View: Practical — Statistical Analysis using SPSS (STS-207)
Variables: Basics of Python Programming — Python Programming and Data Structures (Unit 1) · JavaScript — Web Technologies (Unit 3)
Variance: Life-Insurance Benefits — Advanced Actuarial Statistics (Unit 2) · Measures of Dispersion — Descriptive Statistics (Unit 4) · Stratified Random Sampling — Sampling Techniques (Unit 3)
Variance and standard deviation: Random Variables, Expectation and Variance — Statistical Foundations for Data Science (Unit 2)
Variance Decomposition: Expectation, Characteristic Functions and Inequalities — Probability Theory (Unit 2)
Variance for Linear Trend: Systematic, Cluster & Multistage Sampling — Sampling Techniques (Unit 4)
Variance of an Effect: Factorial Experiments Beyond Two Factors: 2^k, 3^2 and Single-Degree Components — Design and Analysis of Experiments (Unit 2)
Variance of Loss: Net Premiums — Advanced Actuarial Statistics (Unit 4)
Variance Principle: Premium Principles & Individual Risk Models — Actuarial Statistics (Unit 2)
Variance Properties: Unit I: Probability and Distributions — UGC NET Statistics
Varying Benefits: Life-Insurance Benefits — Advanced Actuarial Statistics (Unit 2)
Vector autoregression: Multivariate and State-Space Models — Time Series Analysis and Forecasting (Unit 4)
Vectors: Basics of R for Statistical Data Handling — R Programming (Unit 1) · R Programming Basics & Vectors — Computational Statistics & R Programming (Unit 4) · Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Velocity of Circulation: Money, Banking and Credit Creation — Economics (Unit 3)
Venn Diagrams: Unit VI: Logical Reasoning — UGC NET Paper I
Verbal and Non-verbal Communication: Unit IV: Communication — UGC NET Paper I
Versioning and lifecycle: Create and manage storage buckets; upload and access datasets — Cloud Computing for Data Science (Experiment 4)
Vertex AI AutoML: Use cloud AutoML services for a dataset prediction task — Cloud Computing for Data Science (Experiment 14)
Views: Structured Query Language — Database Management Systems (Unit 4)
VIF: Multicollinearity — Econometrics (Unit 4) · Practical — Linear Algebra & Linear Models Conventional and using R (STS-106)
Visualization: Matrices, Data Frames & EDA — Computational Statistics & R Programming (Unit 5) · Practical — Data Handling using R (STS-108)
Vocabulary in Context: Unit III: Comprehension — UGC NET Paper I

W

Wald Test: The Likelihood Ratio Test, Wald and Rao Score — Testing of Hypotheses (Unit 3)
Wald's Boundaries: Sequential Analysis and Decision Theory — Testing of Hypotheses (Unit 4)
Wald's Identity: Sequential Analysis and Decision Theory — Testing of Hypotheses (Unit 4) · Unit V: Testing of Hypotheses — UGC NET Statistics
Wald–Wolfowitz: Non-parametric Tests — Inferential Statistics (Unit 5)
Warner's Model: Two-Stage Sampling, Non-Sampling Errors, Randomized Response and Small Area Estimation — Sampling Theory (Unit 4)
Waste Management: Unit IX: People, Development and Environment — UGC NET Paper I
Web applications vs desktop applications: HTML — Web Technologies (Unit 1)
Weibull: Future Lifetime & Mortality Laws — Advanced Actuarial Statistics (Unit 1)
Weibull & Hazard Rate: Lognormal, Weibull, Pareto, Laplace and Cauchy — Distribution Theory (Unit 1)
Weibull Plot: Practical — Distribution Theory Conventional and using R (STS-107)
Weierstrass M-Test: Sequences and Series of Functions — Mathematical Analysis (Unit 4)
Weight initialisation: Deep Neural Networks — Neural Networks and Deep Learning (Unit 2)
What-if analysis: Data Analysis and Visualization — Computer Fundamentals and Office Automation (Unit 5)
Whole-life Annuity: Life Annuities & Premiums — Actuarial Statistics (Unit 5)
Whole-Life Insurance: Life Insurance — Actuarial Statistics (Unit 4) · Life-Insurance Benefits — Advanced Actuarial Statistics (Unit 2)
Widgets: Abstract Data Structures and GUI Programming — Python Programming and Data Structures (Unit 5)
Wilcoxon: Inferential Statistics & Hypothesis Testing — R Programming (Unit 4)
Wilcoxon Signed-rank: Non-parametric Tests — Inferential Statistics (Unit 5)
Wilcoxon signed-rank test: Which Statistical Test Should I Use?
Wilks' Lambda: Unit VIII: Multivariate Analysis — UGC NET Statistics
Wilks' Theorem: The Likelihood Ratio Test, Wald and Rao Score — Testing of Hypotheses (Unit 3) · Unit V: Testing of Hypotheses — UGC NET Statistics
Wilks' Λ: Hotelling's T-squared, Mahalanobis D-squared, Wilks' Lambda and Discriminant Analysis — Multivariate Analysis (Unit 3)
Windows (XAMPP/WAMP): Install and configure Apache/XAMPP on the VM and host a page — Cloud Computing for Data Science (Experiment 2)
Wings of NSO: Unit X: Indian Statistical System & Research Methodology — UGC NET Statistics
Wishart Distribution: Wishart Distribution, Generalized Variance and Correlation Distributions — Multivariate Analysis (Unit 2)
Wishart Matrix: Wishart Distribution, Generalized Variance and Correlation Distributions — Multivariate Analysis (Unit 2)
WLLN: Generating Functions, LLN & CLT — Theory of Probability (Unit 5)
WLS: Heteroscedasticity — Econometrics (Unit 3)
WLS Special Case: Unit VI: Linear Estimation, Regression & Econometrics — UGC NET Statistics
Wold Decomposition: Unit VII: Time Series — UGC NET Statistics
Word embeddings: Information Extraction and Representation — Natural Language Processing (NLP) (Unit 3) · Recurrent Neural Networks and NLP — Neural Networks and Deep Learning (Unit 4)
Word processing basics: Word Processing and Presentations — Computer Fundamentals and Office Automation (Unit 3)
Worked example: Control Statements — Problem Solving Using C (Unit 2) · The Entity-Relationship Model — Database Management Systems (Unit 2) · The Relational Model and Normalization — Database Management Systems (Unit 3)
Worked Problems: Curve Fitting — Statistical Methods (Unit 1) · Theory of Attributes — Statistical Methods (Unit 5)
workflow: Automate training and deployment with GitHub Actions — Data Engineering and MLOps (Experiment 11)
Working with JSON: Data Input, Output and Cleaning — Python for Data Analysis and Visualization (Unit 3)
Worksheet Management: Excel Basics for Data Analysis — MS-Excel (Unit 1)
World Bank: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)
Writing MapReduce applications in Hadoop: MapReduce and High-Level Tools — Big Data Technologies (Unit 3)
Writing the Density: Practical — Multivariate Analysis, Conventional (STS-205 Section B)
Written Down Value: Depreciation, Reserves, Single Entry and Non-Trading Concerns — Financial Accounting (Unit 6)
WTO: International Economics: Trade, Balance of Payments and the Institutions — Economics (Unit 5)

X

X̄ Chart: Control Charts for Variables — Statistical Quality Control (Unit 2)

Y

YARN architecture: Hadoop Distributed File System and YARN — Big Data Technologies (Unit 2)
Yates' Continuity Correction: Unit V: Testing of Hypotheses — UGC NET Statistics
Yates's Algorithm for 2³: Factorial Experiments Beyond Two Factors: 2^k, 3^2 and Single-Degree Components — Design and Analysis of Experiments (Unit 2)
Yates–Grundy: Unequal Probability Sampling: Hansen-Hurwitz, Lahiri, Horvitz-Thompson and Yates-Grundy — Sampling Theory (Unit 1)
Yates–Grundy Variance Form: Unit III: Sampling Methods & Design of Experiments — UGC NET Statistics
Youden Square: PBIBD(2), Lattice and Youden Designs, and Response Surface Methodology — Design and Analysis of Experiments (Unit 4) · Practical — Design and Analysis of Experiments, Conventional (STS-206 Section A)
Yule's Q: Theory of Attributes — Statistical Methods (Unit 5)

Z

Z Scaling: Psychological & Educational Statistics — Applied Statistics II (Unit 4)
z-test: Which Statistical Test Should I Use?
Z-Test: Hypothesis Testing in Excel — MS-Excel (Unit 5)
Zero-One Law: Convergence of Sequences of Random Variables — Probability Theory (Unit 3)
Zillmer: Policy Reserves — Advanced Actuarial Statistics (Unit 5)
ZooKeeper: NoSQL and Ecosystem Enhancements — Big Data Technologies (Unit 5)

Symbols

.str accessor: String Operations and Feature Engineering — Python for Data Analysis and Visualization (Unit 4)
β₁, β₂: Moments, Skewness & Kurtosis — Descriptive Statistics (Unit 5)
β₂ ≥ β₁ + 1: Mathematical Expectation — Theory of Probability (Unit 4)
χ² for Variance: Small Sample Tests — Inferential Statistics (Unit 4)
χ² Goodness of Fit: Small Sample Tests — Inferential Statistics (Unit 4)
χ² Independence: Small Sample Tests — Inferential Statistics (Unit 4)
χ² test: Inferential Statistics & Hypothesis Testing — R Programming (Unit 4)