Source document. This page maps the official programme the courses were written to — its semesters, elective tracks and course numbers are that programme’s, not this site’s. The courses themselves are studied on their own, in any order.
Prepared by: the prescribing university · Course structure for Semesters I–VI
Sources, all four extracted verbatim. The extracted text is what everything here is checked against, so that is what is named:
| Source document | Covers | Extracted text |
|---|---|---|
| Semester I–II syllabus, 37 pages | Programme structure + Courses 1–5 | docs/syllabus-extracted.md |
| Semester III–IV syllabus, 25 pages | Courses 6–10 | docs/syllabus-extracted-sem3-4.md |
| Semester V syllabus, 24 pages | Course 11 + both Semester V pairs | docs/syllabus-extracted-sem5.md |
| Semester VI syllabus, 17 pages | Both Semester VI pairs | docs/syllabus-extracted-sem6.md |
Every course is a theory paper with its own laboratory.
| Year | Sem | # | Course |
|---|---|---|---|
| I | I | 1 | Computer Fundamentals and Office Automation |
| I | I | 2 | Problem Solving Using C |
| I | II | 3 | Python Programming and Data Structures |
| I | II | 4 | Statistical Foundations for Data Science |
| II | III | 5 | Database Management Systems |
| II | III | 6 | Data Science with R |
| II | III | 7 | Web Technologies |
| II | IV | 8 | Data Mining |
| II | IV | 9 | Python for Data Analysis and Visualization |
| II | IV | 10 | Document Oriented Database |
| III | V | 11 | Business Intelligence Tools |
| III | V | 12 A / B | Elective — Machine Learning or Big Data Technologies |
| III | V | 13 A / B | Elective — Artificial Intelligence or Cloud Computing for Data Science |
| III | VI | 14 A / B | Elective — Neural Networks and Deep Learning or Time Series Analysis and Forecasting |
| III | VI | 15 A / B | Elective — Natural Language Processing or Data Engineering & MLOps |
In Year III you choose a pair of electives from one of two domains, and must stay in the same domain across both Semester V and VI. Choosing the A track means taking 12A, 13A, 14A and 15A.
| Sem V (12) | Sem V (13) | Sem VI (14) | Sem VI (15) | |
|---|---|---|---|---|
| Track A | Machine Learning | Artificial Intelligence | Neural Networks and Deep Learning | Natural Language Processing |
| Track B | Big Data Technologies | Cloud Computing for Data Science | Time Series Analysis and Forecasting | Data Engineering & MLOps |
Read Track A as the modelling / AI path and Track B as the infrastructure /
engineering path. The PDF's sentence naming the two domains is truncated — see
SYLLABUS-REVIEW.md finding D3 — so confirm the official
domain names with your department before choosing.
Nineteen course numbers make up the Major — Courses 1–11, plus both halves of each elective pair in Semesters V and VI: 12 A/B, 13 A/B, 14 A/B and 15 A/B. All four source documents are now published, so every one of them has a full unit-level syllabus and is mapped here.
You take fifteen of them: Courses 1–11, then one track's pair in Semester V and the same track's pair in Semester VI.
Courses 1–13 are mapped in this section; the Semester VI pairs are in §3b.
Notes: notes/sem-1/course-1-computer-fundamentals/
| Unit | Title | Topics |
|---|---|---|
| 1 | Number Systems, Evolution, Block Diagram and Generations | Binary/decimal/octal/hexadecimal and conversions; evolution of computers; block diagram (Input, Output, Memory, CPU = ALU + CU); generations I–V |
| 2 | Basic organization and N/W fundamentals | Functional components, I/O devices, storage types, memory hierarchy; micro/mini/mainframe/super; networks (LAN/WAN/MAN), topologies (star/ring/bus); Internet basics — IP address, domain name, browser, email, WWW |
| 3 | Word Processing and Presentations | MS Word / Google Docs — formatting, styles, tables, mail merge; PowerPoint / Slides — design, animations, transitions; resumes, reports, brochures; keyboard shortcuts |
| 4 | Spreadsheet Basics | Rows/columns/cells, cell referencing; SUM, AVERAGE, IF, COUNT; charts; sorting, filtering, conditional formatting; text functions (LEFT, RIGHT, MID, LEN, TRIM, CONCAT, TEXTJOIN); logical (IF, AND, OR, IFERROR); lookup (VLOOKUP, HLOOKUP, XLOOKUP, INDEX, MATCH) |
| 5 | Data Analysis and Visualization | Conditional formatting (custom rules, colour scales, icon sets, data bars); pivot tables and pivot charts; data validation; what-if analysis (Goal Seek, Scenario Manager, data tables); interactive dashboards, slicers, combo charts, sparklines; named ranges, freeze panes, split view |
Textbooks: Reema Thareja, Fundamentals of Computers (OUP, 2e) · V. Rajaraman, Fundamentals of Computers (PHI) · Peter Norton, Introduction to Computers (McGraw Hill) · Randy Nordell, Microsoft Office 365 In Practice (McGraw Hill) References: Alexander & Kusleika, Excel 2021 Bible (Wiley) · Doug Lowe, Networking All-in-One For Dummies (Wiley) · learn.microsoft.com · Google Workspace Learning Center
Notes: notes/sem-1/course-2-problem-solving-c/
| Unit | Title | Topics |
|---|---|---|
| 1 | Introduction to computer programming | Types of software; compiler vs interpreter; machine/assembly/high-level; flowcharts and algorithms; history and features of C; tokens — variables, keywords, identifiers, constants, data types; rules for variable names; operators; structure of a C program; formatted and unformatted I/O |
| 2 | Control statements | if, if-else, else-if ladder, switch; while, for, do-while; break, continue, goto |
| 3 | Derived data types in C | 1-D arrays — declaration, initialization, memory representation; 2-D arrays — same; strings — declaring and initializing, string handling functions, character handling functions |
| 4 | Functions (opens with pointers — see review D5) | Pointer data type, declaration, initialization, dereferencing; pointer arithmetic; pointers and arrays; function prototype, definition, calling; return; nesting; categories of functions; recursion (basic); parameter passing by value and by address; local vs global variables; storage classes — auto, extern, static, register |
| 5 | Dynamic Memory Management | malloc, calloc, realloc, free; structures — members, access, nested, array of structures, structures with functions and pointers; unions and how they differ from structures; text files — modes, open, read, write, close |
Textbooks: E. Balagurusamy, Programming in ANSI C (TMH, 6e) · Reema Thareja, Computer Fundamentals and Programming in C (OUP) References: Y. Kanetkar, Let Us C (BPB) · Griffiths & Griffiths, Head First C
Notes: notes/sem-2/course-3-python-data-structures/
| Unit | Title | Topics |
|---|---|---|
| 1 | Basics of Python Programming | Introduction and features; interactive vs script mode; identifiers, naming conventions, keywords; built-in data types; literals (int, float, complex, boolean, string); variables, operators, expressions; assignment; I/O statements; lines, comments, indentation; operator classification — arithmetic, relational, logical, bitwise, assignment, augmented assignment, identity; precedence |
| 2 | Control Flow, Functions & Modules | if / if-else / if-elif-else; while, for, nested loops; break, continue, pass, else with loops; defining and invoking functions; return; scope — local, global, nested; arguments — required, positional, default, variable-length; main(); docstrings; recursion; lambda; library functions; modules — import, from..import, creating modules, namespaces |
| 3 | Sequence, Set, Mapping Types | Strings — indexing, slicing, immutability, operators, traversal, accumulation, formatting, methods; lists — indexing, slicing, methods, mutability, add/update/delete/search/copy/traverse, comprehension; tuples — operations, immutability, tuple assignment, arrays; sets — methods, mathematical operations, frozenset, comprehension; dictionaries — methods, operations, traversal, comparison |
| 4 | File Handling, Exception Handling & OOP (overloaded — see review D6) | File types, paths, open/close, read/write, CSV, os/pathlib; syntax errors, built-in exceptions, try-except, raise, user-defined exceptions, assertions; classes, objects, attributes, methods, constructors, destructors; encapsulation — private/public members; inheritance — single, multilevel, multiple; method overriding |
| 5 | Abstract Data Structures and GUI Programming (two subjects fused — see review D7) | ADT concepts; linked lists — singly, doubly, circular; node structure, insertion, deletion, traversal (singly implemented); stacks — LIFO, list implementation, applications; queues — FIFO, list implementation, priority queues; Tkinter — Label, Button, Entry, Menu, Listbox, Canvas; event handling; simple GUI apps |
Textbooks: Anita Goel, Python Programming — An Object Oriented Approach · Reema Thareja, Python Programming using Problem Solving Approach (OUP, 2020) · Budd T. A., Exploring Python (McGraw-Hill, 1e, 2011) References: Martin C. Brown, Python: The Complete Reference (McGraw-Hill, 2018) · Kenneth A. Lambert, Fundamentals of Python: First Programs (Cengage, 2e, 2019)
Notes: notes/sem-2/course-4-statistical-foundations/
| Unit | Title | Topics |
|---|---|---|
| 1 | Fundamentals of Probability & Basic Statistics | Concept of uncertainty; axioms and rules of probability; conditional probability; central tendency — mean, median, mode; dispersion — range, IQR, variance, standard deviation; correlation and covariance (introduction); data representation — histograms, bar charts, scatter plots |
| 2 | Random Variables, Expectation, and Variance | Random variables — definition, discrete vs continuous, properties; PMF and PDF; CDF; mathematical expectation, variance, standard deviation; moments and moment-generating functions |
| 3 | Probability Distributions | Discrete — Binomial, Poisson, Geometric, Negative Binomial; continuous — Uniform, Normal, Exponential, Gamma; joint, marginal and conditional distributions; Central Limit Theorem (introduction) |
| 4 | Correlation and Regression | Bivariate data and scatter plots; Pearson and Spearman coefficients and interpretation; simple linear regression — model, estimation, properties, ANOVA; multiple linear regression (conceptual); residuals and goodness of fit |
| 5 | Statistical Inference, Estimation, and Hypothesis Testing | Population vs sample, parameters vs statistics; sampling distributions; point and interval estimation (confidence intervals); z-test, t-test, chi-square test, F-test; p-values; Type I and Type II errors; power of a test |
Also examinable but missing from the units: Bayes' theorem — see review D1.
Textbooks: Walpole, Probability and Statistics for Engineers and Scientists (Wiley) · Sheldon M. Ross, Introduction to Probability and Statistics for Engineers and Scientists · Montgomery & Runger, Applied Statistics and Probability for Engineers References: D. C. Agarwal, Statistics for Data Science and AI · Larry J. Stephens, Excel Data Analysis
Notes: notes/sem-3/course-5-dbms/
| Unit | Title | Topics |
|---|---|---|
| 1 | Overview of Database Management System | Data, information, database, DBMS; file-based systems and their drawbacks; database approach; classification of DBMS; advantages; data models; components of a DBMS; three-schema architecture; costs and risks of the database approach |
| 2 | Entity-Relationship Model | Building blocks of an ER diagram; classification of entity sets; attribute classification; relationship degree and classification; reducing ER diagrams to tables; EER model; generalization and specialization; IS-A and attribute inheritance; multiple inheritance; constraints on specialization/generalization; advantages of ER modelling |
| 3 | Relational Model | CODD rules; relational data model; concept of key; relational integrity; relational algebra and its operations, advantages and limitations; functional dependencies and normal forms |
| 4 | Structured Query Language | Commands and data types; DDL; selection and projection; aggregate functions; DML; table modification commands; joins; set operations; views; subqueries |
| 5 | PL/SQL | Shortcomings of SQL; structure of PL/SQL; language elements; data types; operator precedence; control structures; steps to create a PL/SQL program; iterative control; procedures; functions |
Also examinable but missing from the units: database triggers — see review D2.
Textbooks: Silberschatz, Korth & Sudarshan, Database System Concepts (McGraw-Hill, 7e) · Raghu Ramakrishnan, Database Management Systems (McGraw-Hill) References: Elmasri & Navathe, Fundamentals of Database Systems (Pearson) · C. J. Date, An Introduction to Database Systems (Pearson)
Notes: notes/sem-3/course-6-data-science-r/
| Unit | Title | Topics |
|---|---|---|
| 1 | Introduction to the Data Science Process | Definition; data science in various fields; impact; Data Analytics Life Cycle; data science toolkit; the data scientist and the data science team; Exploratory Data Analysis; feature engineering and data transformation |
| 2 | Basics of R Programming | R and RStudio; data types, variables, operators; control structures (if, loops, apply); functions and packages; data input/output (CSV, Excel, XML, JSON) |
| 3 | Data Handling and Visualization in R | Data frames, lists, matrices; wrangling with dplyr and tidyr; missing data; date/time; ggplot2 — grammar of graphics, aesthetics, geometries, scales, faceting, layering; customising and exporting plots |
| 4 | Applications and Case Studies | Simple and multiple linear regression; model evaluation — accuracy, confusion matrix, ROC; K-Means clustering; text mining and word clouds; recommender system basics; ethical issues |
| 5 | Advanced Topics (overloaded — see review D12) | Time series in R — trend, seasonality, noise, ts/zoo/xts, decomposition, stationarity and differencing, ACF/PACF, AR/MA/ARIMA, forecasting; interactive plots with plotly; R Shiny — UI and server functions, reactivity, widgets, dashboard layout |
Textbook: James, Witten, Hastie & Tibshirani, An Introduction to Statistical Learning with Applications in R (Springer, 2e, 2021) References: Matloff, The Art of R Programming (No Starch, 2011) · Venables & Ripley, Modern Applied Statistics with S (Springer, 2002) · Irizarry, Introduction to Data Science (CRC, 2020) · Grus, Data Science from Scratch
Notes: notes/sem-3/course-7-web-technologies/
| Unit | Title | Topics |
|---|---|---|
| 1 | HTML | Web design principles; web vs desktop applications; HTML structure, elements, attributes; headings, paragraphs, images, tables, lists, blocks, symbols; embedding multimedia; HTML forms |
| 2 | CSS | Syntax and combinators; colours, background, borders, margins, padding, height/width; text, fonts, tables, lists; position, overflow, float; pseudo-classes and pseudo-elements; opacity, tooltips, image gallery; CSS forms and counters |
| 3 | JavaScript | DHTML; basics, variables, operators, statements; string manipulation; mathematical functions; arrays, functions, objects; regular expressions; exception handling |
| 4 | Client-Side Scripting | Accessing form elements through the JavaScript object model; basic and format validation; responsive messages; opening windows; dialog boxes; the status bar; animation with keyboard and mouse events |
| 5 | JSON and jQuery | Need for data exchange formats; JSON syntax; JSON vs XML; parsing, creating and accessing nested JSON; reading/writing JSON in JavaScript. jQuery — selectors, filters, DOM manipulation, event handling, animations, effects, chaining |
Textbooks: Chris Bates, Web Programming: Building Internet Applications (Wiley, 2e) · Wang & Katila, An Introduction to Web Design plus Programming (Thomson) · Chaffer & Swedberg, Learning jQuery (Packt) · JSON at Work Reference: David R. Brooks, An Introduction to HTML and JavaScript (Springer)
Notes: notes/sem-4/course-8-data-mining/
| Unit | Title | Topics |
|---|---|---|
| 1 | Data Warehousing | Introduction; database systems vs data warehouse; characteristics; architecture and components; data modelling; schema design — star, snowflake, fact constellation; fact table; OLAP cube and OLAP operations |
| 2 | Data Mining | Definitions; KDD vs data mining; data mining tasks; preprocessing — cleaning, missing data, dimensionality reduction, feature subset selection, discretization and binarization, transformation; similarity and dissimilarity measures; issues, challenges and applications |
| 3 | Association Analysis | What an association rule is; methods to discover rules; A Priori; Partition; Pincer-Search; Dynamic Itemset Counting; FP-Tree Growth; generalized association rules; rules with item constraints |
| 4 | Classification | Decision trees — construction principle, best split, splitting indices and criteria; CART, ID3, C4.5; comparing classifiers; rule-based classifiers; nearest neighbour; Bayesian classifiers |
| 5 | Clustering Techniques | Clustering paradigms; partitioning (K-Means); k-Medoid; hierarchical — DBSCAN, BIRCH; categorical clustering — STIRR, ROCK, CACTUS |
Textbooks: Arun K. Pujari, Data Mining Techniques (3e) · Han, Kamber & Pei, Data Mining: Concepts and Techniques (Morgan Kaufmann, 3e) References: Soman, Diwakar & Ajay, Insight into Data Mining Theory and Practice (PHI, 2006) · Tan, Steinbach, Karpatne & Kumar, Introduction to Data Mining (2e)
Notes: notes/sem-4/course-9-python-data-analysis/
| Unit | Title | Topics |
|---|---|---|
| 1 | NumPy Essentials | The ndarray; creating arrays; data types; arithmetic; basic indexing and slicing; boolean and fancy indexing; transposing and swapping axes; universal functions; mathematical and statistical functions; random number generation |
| 2 | Pandas Basics and Data Structures | Series, DataFrame, Index objects; indexing and selection; filtering and boolean indexing; arithmetic and data alignment; sorting and ranking; dropping entries; duplicate indexes |
| 3 | Data Input, Output and Cleaning | Reading and writing text formats (CSV, TXT); JSON; Excel; handling missing data — dropping, filling, replacing; renaming axis indexes; removing duplicates; filtering outliers; transforming with mapping or functions |
| 4 | String Operations and Feature Engineering | Pandas string methods; basic regular expressions; vectorized string functions; dummy/indicator variables; permutation and random sampling |
| 5 | Data Wrangling, Reshaping and Visualization | Merging and joining; concatenating along an axis; combining with overlap; pivot, stack and unstack; hierarchical indexing; summary statistics by group or level; matplotlib; seaborn; plotly |
Textbooks: Wes McKinney, Python for Data Analysis · Anita Goel, Python Programming — An Object Oriented Approach · Vasiliev, Python for Data Science For Dummies (Wiley, 2e, 2022) Reference: Jake VanderPlas, Python Data Science Handbook (2023)
Notes: notes/sem-4/course-10-document-database/
| Unit | Title | Topics |
|---|---|---|
| 1 | Introduction to NoSQL and MongoDB Fundamentals | What NoSQL is; history and evolution; features; CAP theorem and BASE; types (key-value, document, column, graph); RDBMS vs NoSQL; when to use NoSQL; misconceptions; benefits and use cases; comparison of Redis, Cassandra, CouchDB, Neo4j; JSON and BSON; installation and setup |
| 2 | MongoDB Architecture and Data Modeling | Database, collection and document concepts; BSON; advantages over RDBMS; datatypes; schema design strategies; embedded vs referenced documents; creating and dropping databases and collections |
| 3 | CRUD Operations and Querying | insertOne/insertMany; find with operators and conditions; updateOne/updateMany/replaceOne; deleteOne/deleteMany; query operators ($gt, $lt, $in, $nin, $and, $or, $not); regular expression queries; bulk operations; working with arrays |
| 4 | Data Modelling and Aggregation | Embedded vs normalized models — use cases, benefits, limitations, trade-offs; references between documents; relationships; data models using embedded documents and document references; the aggregation framework — simple pipelines and operators |
| 5 | Advanced Query Processing and Optimization | Projection; limiting and skipping; sorting; indexing (single field, compound, multikey, text); aggregation pipelines, stages and operators; replication — replica sets, failover, consistency |
Semester V is Course 11 plus one elective pair. Course 11 is compulsory; you then take either 12 A + 13 A or 12 B + 13 B, and the choice binds you for Semester VI too. All five are mapped below, because you cannot make that choice well without seeing what is in both.
Notes: notes/sem-5/course-11-business-intelligence/
| Unit | Title | Topics |
|---|---|---|
| 1 | Introduction to BI and Decision Support Systems | BI definition, scope and evolution; BI vs Data Analytics vs Data Science; the BI lifecycle; applications in finance, HR, marketing, retail, education, healthcare; BI maturity models and organizational readiness; DSS concepts, components and architecture; Power BI, Tableau and other tools compared; case study — a retail chain's BI strategy for inventory |
| 2 | Data Preparation and Visualization with Power BI | The Power BI ecosystem — Desktop, Service, Mobile; the interface; data sources (Excel, CSV, SQL Server, Web APIs); Power Query for preparation, cleaning and transformation; basic DAX — SUM, COUNT, AVERAGE, CALCULATE, IF; charts, tables and cards; sharing via Power BI Service; case studies — student performance, finance dataset |
| 3 | Data Preparation, Visualization and Storytelling with Tableau | Tableau characteristics; architecture and components — Public, Desktop, Reader, Online, Server; the interface — shelves, marks card, views; extensions; data connection and preparation — cleaning, pivoting, filtering; calculated fields and LOD expressions; bar, line, tree, geo map and scatter visualizations; storytelling and creating a Tableau story; case study — HR analytics |
| 4 | Data Modeling and Relationships in BI Tools | Dimensional modeling — dimension, dimension table, fact, fact table, schema; star and snowflake schemas; Power BI relationships, cardinality and cross-filtering; Tableau joins (inner, left, full) and blending; data governance — metadata, hierarchies, quality; data model design best practices; case study — retail BI for sales optimization |
| 5 | Dashboard Design and Business Insights | When to use a dashboard; dashboard components; principles of effective visualization and dashboarding; advanced visuals — parameters, slicers, filters, drilldowns, graphs and maps; layout, alignment and accessibility; publishing to Power BI Service and Tableau Public; storytelling and insight communication; case study — sales forecasting and budgeting |
Notes: notes/sem-5/course-12a-machine-learning/
| Unit | Title | Topics |
|---|---|---|
| 1 | Introduction to Machine Learning | Types of human learning; what machine learning is; supervised, unsupervised, semi-supervised and reinforcement learning; machine learning activities; applications; types of data in ML; structure of data |
| 2 | Model Preparation, Evaluation and Feature Engineering | Data pre-processing; model selection and training for supervised learning; model representation and interpretability; evaluating algorithms and enhancing performance; feature engineering; feature transformation; feature subset selection; principal component analysis |
| 3 | Supervised Learning — Regression | Introduction to regression; simple linear regression; multiple linear regression; polynomial regression; logistic regression; maximum likelihood estimation |
| 4 | Supervised Learning — Classification | Introduction to supervised learning; the classification model and its learning steps; Naïve Bayes; k-Nearest Neighbour; decision trees; support vector machines; random forest |
| 5 | Unsupervised Learning | Introduction; unsupervised vs supervised; applications; clustering and its types; partitioning methods — k-Means and k-Medoids; hierarchical clustering; density-based methods — DBSCAN; case studies — image recognition, speech recognition, email spam filtering, online fraud detection |
Notes: notes/sem-5/course-13a-artificial-intelligence/
| Unit | Title | Topics |
|---|---|---|
| 1 | Introduction to AI and Intelligent Agents | Definition and scope of AI; history and evolution; the Turing Test; real-world applications; Weak vs Strong AI, Narrow vs General AI; intelligent agents — structure, rationality, agent types; environments — deterministic vs stochastic, static vs dynamic, discrete vs continuous; PEAS representation |
| 2 | Problem Solving — State Space and Uninformed Search | State space representation — state, actions, goal test, path cost; problem formulation with the 8-puzzle, water jug and vacuum cleaner world; breadth first search; depth first search; uniform cost search; properties — completeness, optimality, time and space complexity |
| 3 | Informed and Advanced Search Strategies | Heuristics — concept, admissibility, consistency; greedy best first search; A*; local search — hill climbing, simulated annealing; genetic algorithms; constraint satisfaction problems and backtracking search |
| 4 | Knowledge Representation and Reasoning | Representation issues and approaches; propositional logic — syntax, semantics, truth tables, inference rules; first order logic — syntax, semantics, quantifiers, substitution, unification; forward chaining, backward chaining, resolution; knowledge-based agents |
| 5 | Expert Systems, Probabilistic and Emerging AI | Expert system architecture — knowledge base, inference engine, explanation facility; probabilistic reasoning — Bayes' theorem, Bayesian belief networks; fuzzy logic and uncertainty handling; NLP basics; robotics; AI ethics and societal impact |
Notes: notes/sem-5/course-12b-big-data/
| Unit | Title | Topics |
|---|---|---|
| 1 | Foundations of Big Data and the Hadoop Ecosystem | Big Data characteristics — volume, variety, velocity, veracity, value; ecosystem overview — HDFS, MapReduce, YARN, Hadoop Common; Hadoop architecture and use cases |
| 2 | HDFS and YARN | HDFS architecture — blocks, NameNode, DataNodes; file operations; fault tolerance; replication; YARN architecture — ResourceManager, NodeManager, application scheduling |
| 3 | MapReduce and High-Level Tools | The MapReduce programming model — map, shuffle, reduce phases; writing MapReduce applications; high-level abstractions — Hive, Pig, Crunch; introduction to Spark integration |
| 4 | Data Ingestion and Serialization | Ingestion pipelines — Sqoop for RDBMS, Flume for streaming; data formats and serialization — Avro, Parquet, SequenceFile; batch and streaming ingestion workflows |
| 5 | NoSQL and Ecosystem Enhancements | NoSQL within the Hadoop ecosystem — HBase; configuring and using ZooKeeper for coordination; Hadoop integration with Spark for data processing |
Notes: notes/sem-5/course-13b-cloud-computing/
| Unit | Title | Topics |
|---|---|---|
| 1 | Introduction to Cloud Computing | Definition and evolution; service-oriented architecture and web services; utility and grid computing; characteristics of cloud computing; architecture — front-end, back-end, networking, delivery models; service models — SaaS, PaaS, IaaS; continuous delivery using PaaS |
| 2 | Virtualization and Deployment Models | Concept and importance of virtualization; types — application, network, desktop, storage, server, data; deployment models — public, private, community, hybrid; the role of cloud computing in data science; advantages of cloud in machine learning |
| 3 | Cloud Storage and Data Management | Cloud storage — introduction, benefits, use cases (backup, archiving, disaster recovery, content delivery); storage systems — block-based, file-based, object-based; key-value databases — features and limitations; batch vs streaming data for ML pipelines; cloud data warehouses — AWS Redshift, Google BigQuery |
| 4 | Cloud Platforms for Data Science and ML | Machine learning in the cloud — benefits and limitations; cloud-based ML services — AIaaS, GPUaaS; managed ML platforms; AWS SageMaker, Azure ML Studio, Google Cloud AutoML |
| 5 | Training and Deployment of ML on the Cloud | Factors for selecting a platform — ETL/ELT pipeline support, scale-up/scale-out training, ML frameworks, pre-tuned services; steps for training in the cloud — data source identification, feature engineering, training, validation, deployment, monitoring; improving cloud-deployed models; case studies and industry applications |
Source: the Semester VI syllabus, 17 pages, extracted to
docs/syllabus-extracted-sem6.md.
The Semester VI document confirms the track pairing that §1 inferred and §3a partly confirmed: 14 A pairs with 15 A (Deep Learning → NLP) and 14 B with 15 B (Time Series → Data Engineering & MLOps). You stay in the same track across both semesters of Year III.
| Unit | Title | Topics |
|---|---|---|
| 1 | Foundations of Deep Learning | What AI, ML and deep learning are; history and applications; biological vs. artificial neurons; neural networks; perceptron and activation functions (linear, ReLU, sigmoid, tanh, softmax); shallow vs. deep and feedforward vs. recurrent; gradient descent and backpropagation (conceptual); loss functions (MSE, cross-entropy) intuitively |
| 2 | Deep Neural Networks | Forward and backward propagation; weight initialization, learning rate, and optimization (SGD, Adam, RMSProp); overfitting and underfitting; regularization, dropout, batch normalization; activation functions in deep networks; loss functions in detail (MSE, cross-entropy, hinge); introduction to Keras/TensorFlow |
| 3 | Convolutional Neural Networks | Images and pixels; filters/kernels, padding and pooling; CNN layers (Conv, Pooling, Fully Connected, Softmax); LeNet-5, AlexNet, VGG; applications in image classification, object detection, facial recognition |
| 4 | Recurrent Neural Networks and NLP | Sequences and time series data; RNNs and the vanishing/exploding gradient; LSTM and GRU; word embeddings — Word2Vec, GloVe, contextual embeddings and BERT at a high level; sentiment analysis, text generation, simple time-series forecasting |
| 5 | Advanced and Emerging Topics | Generative models — GANs (generator and discriminator intuition), VAEs (introduction only); transformers and the attention mechanism (intuitive); BERT and the GPT family; transfer learning and fine-tuning pre-trained models; AI ethics — bias, fairness, privacy, safety, explainability |
Lab: 12 practicals. Ten run here against real MNIST,
Fashion-MNIST, IMDb and real MobileNetV2/VGG16 ImageNet weights. Two are
documented rather than demonstrated — experiment 2 is two interactive web
applications, and experiment 12 needs huggingface.co, which this
environment refuses with a 403 at the gateway.
| Unit | Title | Topics |
|---|---|---|
| 1 | Fundamentals and Stationary Processes | Time series types, components and the forecasting process; stationary processes; autocovariance and the ACF/PACF; model evaluation metrics; ACF/PACF example analyses |
| 2 | ARMA and Forecasting with ARMA | ARMA(p,q) — definition, estimation, forecasting; model identification via AIC and the ACF/PACF; diagnostic checks; practical fitting and forecast generation |
| 3 | Non-Stationary and Seasonal Models | Differencing; the Augmented Dickey-Fuller and KPSS tests; ARIMA and SARIMA for seasonal data; prediction intervals |
| 4 | State-Space and Multivariate Models | Vector autoregression; Granger causality; state-space representation and the Kalman filter |
| 5 | Advanced Topics and Forecast Evaluation | Spectral analysis and the periodogram; exponential smoothing and Holt-Winters; comparing ARIMA against exponential smoothing against machine learning; RMSE, MAE, MAPE and MASE |
Lab: 13 practicals. All thirteen run — statsmodels implements
every technique the syllabus names, so this course has no NOT EXECUTED file
anywhere.
| Unit | Title | Topics |
|---|---|---|
| 1 | Introduction to NLP and Language Fundamentals | Definition, goals and scope of NLP; real-world applications (assistants, chatbots, translation, summarization, QA, spam detection); fundamentals of language processing; ambiguities — lexical, structural, contextual; installations — Python, NLTK, spaCy basics; regular expressions (findall, split, sub, matching tokens) |
| 2 | Text Preprocessing and Linguistic Analysis | Morphology, lexicon, orthographic rules; finite state transducers; tokenization, stopword removal, stemming, lemmatization; grammar and context-free grammar; parsing — top-down, bottom-up, the CYK algorithm; semantic analysis — elements and meaning representation |
| 3 | Information Extraction and Representation | Named entity recognition with spaCy and NLTK; word embeddings — Word2Vec (skip-gram, CBOW), comparison and implementations; bag of words and n-grams; the text classification pipeline; sentiment analysis; ethical considerations in preprocessing and classification |
| 4 | Deep Learning for NLP | Recurrent neural networks; RNN against CNN and feedforward networks; LSTM and GRU for sequence modeling; transformer models; pretrained models (BERT, GPT); the Hugging Face ecosystem |
| 5 | Transformers and Modern NLP | Transformer architecture basics — self-attention, encoder-decoder; BERT pretraining and fine-tuning; GPT and generative NLP; using pre-trained models; text summarization — extractive, abstractive, hybrid; applications — document classification, chatbots, virtual assistants |
Lab: 14 practicals. Eleven run against real NLTK corpora
(Brown, Reuters, the Penn Treebank, movie_reviews, Gutenberg, WordNet), real
spaCy models, scikit-learn and PyTorch. The three Hugging Face experiments
(12–14) are marked NOT EXECUTED — huggingface.co is refused at the gateway
with a 403 — and each has a runnable half that builds the same mechanism.
| Unit | Title | Topics |
|---|---|---|
| 1 | Foundations of Data Engineering | Data engineering — definition, lifecycle, skills, activities; the evolution and roles of data engineers, technical against business responsibilities, internal against external; the relationship between data engineering and data science; the data lifecycle against the data engineering lifecycle |
| 2 | Data Architecture and Distributed Systems | Enterprise and data architecture; principles of good data architecture; scalability, failure design, tiers, microservices, monolith against modular; event-driven architecture, hybrid cloud, multicloud, edge computing; technology selection — team size, interoperability, cost, TCO |
| 3 | MLOps Fundamentals | MLOps challenges and risk mitigation; Responsible AI and scaling ML solutions; EDA, feature engineering, model training and evaluation, reproducibility; deployment requirements and monitoring basics; model versioning and experimentation tracking |
| 4 | Model Deployment and CI/CD Pipelines | Preparing models for production; runtime environments from dev to production; CI/CD pipelines — building ML artifacts, testing pipelines; deployment strategies — batch, online, A/B testing, canary releases; containerization and scaling with Docker and Kubernetes |
| 5 | Monitoring, Feedback Loops and Governance | Monitoring in production — drift detection, ground truth evaluation; feedback loops — retraining workflows, online evaluation; logging and monitoring frameworks; governance — GDPR, CCPA, GxP, Responsible AI principles; templates for governance, compliance and model risk management |
Lab: 16 practicals. Eleven run against the real tools — MLflow 3 on a SQLite backend, git and DVC with a genuine data rollback, a Flask server on a real socket, SQLite constraints that reject bad rows, and scipy's statistical tests scored against injected drift. Five are marked NOT EXECUTED: Kafka/RabbitMQ and Hadoop need broker and JVM processes, the Docker daemon is not running, GitHub Actions needs a runner, and Prometheus and Grafana are servers. Each names a runnable half.
All nineteen course numbers have unit-level notes, laboratory material and practice questions here. A student takes fifteen of them — Courses 1–11, then one track's pair in Semester V and the same track's pair in Semester VI — but both halves of every elective pair are written out, so you can read each before choosing.
| Semester | Courses | Status |
|---|---|---|
| I–II | 1–5 | complete |
| III–IV | 6–10 | complete |
| V | 11, 12 A/B, 13 A/B | complete, both tracks |
| VI | 14 A/B, 15 A/B | complete, both tracks |
Both elective tracks are covered in full, because the choice made at the start of Semester V binds you through Semester VI and you cannot choose well without seeing what is in each.