Source document. This page reproduces the official programme syllabus the courses were written to — its semesters, elective tracks and course numbers are that document’s, not this site’s. The courses themselves are studied on their own, in any order.
Source: docs/Data-Science-Major-Sem1-2.pdf — 37 pages.
Extracted verbatim so every claim in the notes is traceable to a page.
Regenerate with python3 tools/extract_syllabus.py <pdf>.
Model Syllabus for 4-Year UG Honours in B.Sc. (Data Science) as Major in consonance with Curriculum framework, Prepared by the prescribing university COURSE STRUCTURE (for Semester I to VI) Year Semester Course Title of the Course No. of Hrs /Week No. of Credits I I 1 Computer Fundamentals and Office Automation 3 3 Computer Fundamentals and Office Automation Lab 2 1 2 Problem Solving Using C 3 3 Problem Solving Using C Lab 2 1 II 3 Python Programming and Data Structures 3 3 Python programming and Data Structures lab 2 1 4 Statistical Foundations for Data Science 3 3 Statistical Foundations for Data Science lab 2 1 II III 5 Database Management Systems 3 3 Database Management Systems Lab 2 1 6 Data Science with R 3 3 Data Science With R lab 2 1 7 Web Technologies 3 3 Web Technologies Lab 2 1 IV 8 Data Mining 3 3 Data Mining Lab 2 1 9 Python for Data Analysis and Visualization 3 3 Python for Data Analysis and Visualization lab 2 1 10 Document Oriented Database 3 3 Document oriented Database lab 2 1 III V 11 Business Intelligence Tools 3 3 Business Intelligence Tools Lab 2 1
Year Semester Course Title of the Course No. of Hrs /Week No. of Credits 12 A Machine Learning 3 3 Machine Learning Lab 2 1 OR 12 B Big Data Technologies 3 3 Big Data Technologies Lab 2 1 13 A Artificial Intelligence 3 3 Artificial Intelligence Lab 2 1 OR 13 B Cloud computing for Data Science 3 3 Cloud computing for Data Science Lab 2 1 VI 14 A Neural networks and Deep Learning 3 3 Neural networks and Deep Learning lab 2 1 OR 14 B Time Series Analysis and Forecasting 3 3 Time Series Analysis and Forecasting Lab 2 1 15 A Natural Language Processing 3 3 Natural Language Processing Lab 2 1 OR 15 B Data Engineering & MLOps 3 3 Data Engineering & MLOps 2 1 Note: In the III Year (during the V and VI Semesters), students are required to select a pair of electives from one of the TWO specified domains. is chosen, courses 12 to 15 to be chosen as 12 A, 13 A, 14 A and 15 A. To ensure in-depth understanding and skill development in the chosen domain, students must continue with the same domain electives in both the V and VI Semesters.
SEMESTER-I COURSE 1: COMPUTER FUNDAMENTALS AND OFFICE AUTOMATION Theory Credits: 3 3 hrs/week Course Objectives 1. Understand foundational computing concepts, including number systems, the evolution of computers, block diagrams, and generational progress. 2. Develop knowledge of computer architecture, focusing on system organization and networking fundamentals. 3. Acquire practical skills in document creation, formatting, and digital presentations using word processing tools. 4. Gain proficiency in spreadsheet operations, such as data entry, formulas, functions, and charting techniques. 5. Introduce data visualization and basic modelling principles, fostering analytical thinking in structuring and interpreting data sets. Course Outcomes 1. At the End of the Course, The Students will be able to explain different number systems, the historical evolution of computers, and identify key components in a block diagram. 2. Learners will demonstrate basic blocks of a computer and fundamental networking knowledge. 3. Learners will create professional-level documents and design visually appealing presentations using word processing software and presentation software. 4. Learners will manipulate data within spreadsheets, apply formulas, and generate accurate summaries and visualizations. 5. Learners will apply data modelling techniques to analyze, organize, and represent data effectively in various scenarios. Unit 1. Number Systems, Evolution , Block Diagram and Generations: Number Systems: Binary, Decimal, Octal, Hexadecimal; conversions between number systems. Evolution of Computers: History from early mechanical devices to modern-day systems. Block Diagram of a Computer: Components like Input Unit, Output Unit, Memory, CPU (ALU + CU).
Generations of Computers: First to Fifth Generation technologies, characteristics, examples. Unit 2. Basic organization and N/W fundamentals: Computer Organization: Functional components Input/Output devices, Storage types, Memory hierarchy. Types of Computers: Micro, Mini, Mainframe, and Supercomputers. Networking Fundamentals: Definition, need for networks, types (LAN, WAN, MAN), topology (Star, Ring, Bus). Internet Basics: IP Address, Domain Name, Web Browser, Email, WWW. Unit 3. Word Processing and presentations: Word Processing Basics: Using MS Word/Google Docs formatting, styles, tables, mail merge. Presentation Tools: Using PowerPoint/Google Slides slide design, animations, transitions. Applications: Creating resumes, reports, brochures, and presentations. Keyboard Shortcuts Unit 4. Spreadsheet Basics: Spreadsheet Concepts: Understanding rows, columns, cells in tools like MS Excel/Google Sheets, cell referencing. Functions and Formulae: SUM, AVERAGE, IF, COUNT. Charts and Graphs: Creating visual representations Data Handling: Sorting, filtering, conditional formatting. Text Functions: LEFT, RIGHT, MID, LEN, TRIM, CONCAT, TEXTJOIN Advanced Functions: Logical: IF, AND, OR, IFERROR, Lookup: VLOOKUP, HLOOKUP, XLOOKUP, INDEX, MATCH Unit 5. Data Analysis and Visualization: Conditional Formatting: Custom rules, Color scales, Icon sets, Data bars Data Analysis Tools: Pivot Tables and Pivot Charts, Data Validation (Drop-downs, Input Messages, Error Alerts), What-If Analysis: Goal Seek, Scenario Manager, Data Tables Charts and Dashboards: Creating Interactive Dashboards, Using slicers with Pivot Tables,Combo Charts and Sparklines
Productivity Tips: Using Named Ranges, Freeze Panes, Split View Textbooks: 1. Fundamentals of Computers, Reema Thareja, Second Edition 2. Fundamentals of Computers, V. Rajaraman PHI Learning 3. Introduction to Computers by Peter Norton McGraw Hill 4. Microsoft Office 365 In Practice by Randy Nordell McGraw Hill Education References: 1. Excel 2021 Bible by Michael Alexander, Richard Kusleika Wiley 2. Networking All-in-One For Dummies by Doug Lowe Wiley 3. Microsoft Official Docs and Training: https://learn.microsoft.com 4. Google Workspace Learning Center: https://support.google.com/a/users/ Activities: Outcome: At the End of the Course, The Students will be able to explain different number systems, the historical evolution of computers, and identify key components in a block diagram. Activity: Create a digital poster or infographic comparing number systems (binary, decimal, octal, hexadecimal) and illustrating the timeline of computer generations with key innovations. Evaluation Method: Rubric-based assessment of the poster presentation on a 10-point scale focusing on: Accuracy of number system conversions Correct identification of block diagram components Visual organization and creativity Outcome: Learners will demonstrate basic blocks of a computer and fundamental networking knowledge. Activity: Design a concept map showing the internal architecture of a computer and types of networks (LAN, WAN, MAN), including devices and topologies. Evaluation Method: Checklist-based peer review and instructor validation: Completeness of the map Correctness of networking concepts
Use of appropriate terminology Logical flow and structure of the map Outcome: Learners will create professional-level documents and design visually appealing presentations using word processing software and presentation software. Activity: Prepare a formal report (e.g., project proposal) in a word processor and present it using a slide deck with transitions, embedded media, and design elements. Evaluation Method: Performance-based evaluation using a 10-point scoring scale: Formatting and structure of the document Presentation aesthetics and clarity Communication skills during presentation Outcome: Learners will manipulate data within spreadsheets, apply formulas, and generate accurate summaries and visualizations. Activity: Analyze a dataset (e.g., student scores or sales data) using spreadsheet software. Apply formulas (SUM, AVERAGE, IF, VLOOKUP) and create relevant charts. Evaluation Method: Practical test with a rubric: Correct use of formulas Accuracy of data summaries Outcome: Learners will apply data modelling techniques to analyze, organize, and represent data effectively in various scenarios. Activity: Prepare an interactive dashboard for a given data set using EXCEL. Evaluation Method: Evaluation of the dashboard on a 10-point scoring scale: Presentation aesthetics and clarity Interactiveness Communication skills during presentation
SEMESTER-I COURSE 1: COMPUTER FUNDAMENTALS AND OFFICE AUTOMATION Practical Credits: 1 2 hrs/week List of Experiments: 1. Demonstration of Assembling and Dessembling of Computer Systems. 2. Identify and prepare notes on the type of Network topology of your institution. 3. Prepare your resume in Word. 4. Using Word, write a letter to your higher official seeking 10-days leave. 5. Prepare a presentation that contains text, audio and video. 6. Using a spreadsheet, prepare your class Time Table. 7. Using a Spreadsheet, calculate the Gross and Net salary of employees(Min 5) considering all the allowances. 8. Generate the class-wise and subject-wise results for a class of 20 students. Also generate the highest and lowest marks in each subject. 9. Using IF, AND, OR, and IFERROR to Automate Grade Evaluation. a. Create a table of student scores in different subjects. b. Use IF to assign grades (A/B/C/Fail). c. Use IFERROR to handle missing scores or invalid data. 10. Employee Database Search Using VLOOKUP, HLOOKUP, XLOOKUP, INDEX, and MATCH a. Create a database of employees (Name, ID, Department, Salary). b. Implement VLOOKUP to search by employee ID. c. Use HLOOKUP to extract department heads by role. d. Apply XLOOKUP for more flexible searches. e. Use INDEX + MATCH as an alternative to VLOOKUP. 11. Sales Report Analysis Using Pivot Tables and Charts a. Use a dataset of product sales (Product, Region, Date, Quantity, Revenue). b. Create Pivot Tables to summarize data by region/product. c. Insert Pivot Charts for visual analysis (e.g., bar, line). d. Add slicers to make the dashboard interactive. 12. Designing a Data Entry Form with Drop-downs and Input Rules a. Create a student registration form. b. Add drop-down lists for course selection using Data Validation.
c. Add input messages to guide users. d. Add error alerts for wrong entries. 13. Monthly Budget Planning using Goal Seek and Scenario Manager a. Create a simple personal budget (income, expenses, savings). b. Use Goal Seek to determine income needed to save a desired amount. c. Use Scenario Manager to compare different budgeting scenarios (best/ worst/ realistic case). d. Create a one-variable Data Table to analyze how different expenses affect savings. 14. Dashboard Creation Using Combo Charts, Sparklines & Slicers a. Use existing sales or attendance data. b. Insert combo charts (e.g., column + line). c. Add sparklines to show trends. d. Use slicers with Pivot Tables to control dashboard elements. e. Finalize and format for interactivity.
SEMESTER-I COURSE 2: PROBLEM SOLVING USING C Theory Credits: 3 3 hrs/week Course Objectives: 1. Understand the fundamentals of computer programming, Apply structured problem-solving approaches using algorithms, flowcharts, and C programming constructs. 2. Develop efficient logic using decision-making, loop, and jump control statements. 3. Utilize derived data types like arrays and strings for modular program design. 4. Design and implement modular solutions using functions, recursive logic, pointer operations, and dynamic memory management. 5. Handle complex data structures including structures, unions, and text file operations. Course Outcomes: At the end of the course, students will be able to: 1. Understand basic computing concepts, programming paradigms and write structured C programs. 2. Apply control flow statements to solve logical and repetitive tasks in C. 3. Implement arrays and string operations to manage and manipulate data efficiently. 4. Design modular code using functions, recursion, and appropriate parameter passing. 5. Utilize pointers and memory operations for effective data handling. Demonstrate competence in dynamic memory allocation and text file processing. Unit 1. Introduction to computer programming: Introduction, Types of software, Compiler and interpreter, Concepts of Machine level, Assembly level and high-level programming, Flowcharts and Algorithms, Fundamentals of C: History of C, Features of C, C Tokens-variables and keywords and identifiers, constants and Data types, Rules for constructing variable names, Operators, Structure of C program, Input /output statements in C-Formatted and Unformatted I/O Unit 2. Control statements: Decision making statements: if, if else, else if ladder, switch statements. Loop control statements: while loop, for loop and do-while loop. Jump Control statements: break,continue and goto.
Unit 3. Derived data types in C: Arrays: One Dimensional arrays - Declaration, Initialization and Memory representation; Two Dimensional arrays -Declaration, Initialization and Memory representation. Strings: Declaring & Initializing string variables; String handling functions, Character handling functions Unit 4. Functions: Pointers: Pointer data type, Pointer declaration, initialization, accessing values using pointers. Pointer arithmetic, Pointers and arrays. Function Prototype, definition and calling. Return statement. Nesting of functions. Categories of functions. Recursion (Basic Concept only). Parameter Passing by address & by value. Local and Global variables. Storage classes: automatic, external, static and register. Unit 5. Dynamic Memory Management: Introduction, Functions-malloc, calloc, realloc, free Structures: Basics of structure, structure members, accessing structure members, nested structures, array of structures, structure and functions, structures and pointers. Unions - Union definition; difference between Structures and Unions. Working with text files - modes: opening, reading, writing and closing text files. Text Books: 1. Programming in ANSI C, E. Balagurusamy, Tata McGraw Hill, 6 th Edn, 2. Computer fundamentals and programming in C, Reema Theraja Reference Books: 1. Let us C, Y Kanetkar, BPB publications 2. Head First C: A Brain-Friendly Guide, David Griffiths, Dawn Griffiths Activities: Outcome: Understand basic computing concepts, programming paradigms and write structured C programs. Activity: Create a concept map of computing fundamentals and programming paradigms (procedural, structured, object-oriented). Then, they write a structured C program (e.g., a calculator or student grade system) using proper syntax, indentation, and modular design. Evaluation Method: Rubric-based Code Review & Viva to check the The correctness of the concept map Correct use of structure (main + functions)
Identification of paradigm used Code readability and documentation Outcome: Apply control flow statements to solve logical and repetitive tasks in C. Activity: Implement a program that solves a logic puzzle (e.g., number guessing game, pattern generation, or prime number finder) using if, switch, for, while, and do-while. Evaluation Method: Automated Test Cases + Peer Review to check the Correct use of control statements Logical correctness of output Efficiency and edge case handling Peer feedback on clarity and logic Outcome: Implement arrays and string operations to manage and manipulate data efficiently. Activity: Build a program that stores and arranges student marks in ascending and descending order using arrays and performs string operations like concatenation, comparing, and formatting names. Evaluation Method: Functional Demonstration + Code Walkthrough to check the Correct array and string usage Memory efficiency Handling of invalid inputs Explanation of sorting/searching logic Activity: Recursive Problem Solver Students write a modular program to solve a recursive problem (e.g., factorial, Fibonacci, or Tower of Hanoi) using functions with parameters and return values. Evaluation Method: Code Trace + Written Quiz Correct function decomposition Proper parameter passing (by value/reference) Recursion depth and base case handling Quiz on tracing recursive calls Outcome: Utilize pointers and memory operations for effective data handling. Demonstrate competence in dynamic memory allocation and text file processing. Activity: Create a program that dynamically stores user input (e.g., survey responses) using pointers and writes/reads the data to/from a text file. Evaluation Method: Memory Debugging + File I/O Assessment to check the
Proper use of malloc, calloc, realloc, and free Pointer arithmetic and dereferencing File creation, reading, writing, and error handling Use of tools like Valgrind or manual memory trace (Optional for Unix flavours)
SEMESTER-I COURSE 2: PROBLEM SOLVING USING C Practical Credits: 1 2 hrs/week List of Experiments: 1. Write a program to check whether the given number is Armstrong or not. 2. Write a program to find the sum of individual digits of a positive integer. 3. Write a program to generate the first n terms of the Fibonacci sequence. 4. Write a program to find both the largest and smallest number in a list of integer values 5. Write a program to demonstrate change in parameter values while swapping two integer variables using Call by Value & Call by Address 6. Write a program to perform various string operations. 7. Write a program to search an element in a given list of values. 8. Write a program that uses functions to add two matrices. 9. Write a program to calculate factorial of given integer value using recursive functions 10. Write a program for multiplication of two N X N matrices. 11. Write a program to sort a given list of integers in ascending order. 12. Write a program to calculate the salaries of all employees using the Employee (ID, Name, Designation, Basic Pay, DA, HRA, Gross Salary, Deduction, Net Salary) structure. a. DA is 30 % of Basic Pay b. HRA is 15% of Basic Pay c. Deduction is 10% of (Basic Pay + DA) d. Gross Salary = Basic Pay + DA+ HRA e. Net Salary = Gross Salary - Deduction 13. Write a program to read / write the data from / to a file. 14. Write a program to reverse the contents of a file and store in another file. 15. Write a program to create Book (ISBN,Title, Author, Price, Pages, Publisher)structure and store book details in a file and perform the following operations a. Add book details b. Search a book details for a given ISBN and display book details, if available c. Update a book details using ISBN d. Delete book details for a given ISBN and display list of remaining Books
SEMESTER-II COURSE 3: PYTHON PROGRAMMING AND DATA STRUCTURES Theory Credits: 3 3 hrs/week Course Objectives 1. To introduce the fundamentals of Python programming, including environment setup, syntax, and core concepts. 2. To develop problem-solving skills using control flow, functions, and modules. 3. To provide knowledge of Python data structures, file handling, and exception handling for effective programming. 4. To impart object-oriented programming concepts and GUI development skills for building applications. Course Outcomes (COs) After successful completion of the course, students will be able to: 1. Explain the basic features, syntax, data types, and operators of Python programming. 2. Apply control flow constructs, functions, and modules to develop structured Python programs. 3. Demonstrate the use of sequences, sets, and dictionaries for effective data handling and manipulation. 4. Implement file handling techniques and apply exception handling mechanisms for robust applications. 5. Develop object-oriented and GUI-based applications using Python. Unit 1. Basics of Python Programming: Introduction to Python, Features of Python, Programming Modes - Interactive Mode & Script Mode, Identifiers, Naming Conventions, Keywords (Reserved Words), Built-in Data Types, Literals - Integer, Float, Complex, Boolean, String, Variables, Operators, Expressions, Assignment Statements, Input/Output Statements, Python Syntax (Lines, Comments, Indentation) Operators & Operands, Classification of Operators - Arithmetic Operators, Relational Operators, Logical Operators, Bitwise Operators, Assignment, Augmented Assignment, Identity Operators, Expressions & Precedence Rules
Unit 2. Control Flow, Functions & Modules: Control Flow - if Statement, if-else, if-elif-else. Iterative Statements while, for, Nested Loops, Loop Control Statements break, continue, pass; else with loops Need for Functions, Defining & Invoking User-defined Functions, Return Statement, Function Input/Output Cases, Scope of Variables - Local, Global, Nested Functions, Function Arguments - Required, Positional, Default, Variable-length, main() Function, Documentation Strings, Recursive Functions, Anonymous Functions (Lambda), Library Functions Modules - Import, from..import, Creating & Using Modules, Namespaces Unit 3. Sequence, Set, Mapping Types: Strings- Representation, Indexing, Slicing, Immutability, String Operators, Traversal, Accumulation, Formatting & Methods Lists - Overview, Indexing, Slicing, Methods, Mutability, List Operations - Add, Update, Delete, Search, Copy, Traverse, Comprehension Tuples - Operations, Immutability, Tuple Assignment, Arrays & Operations Sets - Overview, Methods, Mathematical Operations, Frozenset, Comprehension Dictionaries - Overview, Methods, Operations, Traversal, Comparison Unit 4. File Handling, Exception Handling & Object Oriented Programming: File Handling - Types, Paths, Basic Operations on Files - Open/Close, Read/Write, CSV Files, OS/Pathlib Error & Exception Handling - Syntax Errors, Built-in Exceptions, Catching and Handling Exceptions: try-except, raise, User-defined Exceptions, Assertions OOP Concepts: Classes, Objects, Attributes, Methods, Constructor and Destructors Encapsulation: Private and Public Members Inheritance: Single, Multilevel, Multiple, Method Overriding Unit 5: Abstract Data Structures and GUI Programming Abstract Data Structures (ADTs): Concepts and Importance Linked List: Definition, Types- Singly, Doubly, Circular; Node Structure, Insertion, Deletion, Traversal (Single Linked list implementation only) Stacks: LIFO Principle, Implementation using List, Applications Queues: FIFO Principle, Implementation using List, Priority Queues
GUI Programming with Tkinter: Widgets (Label, Button, Entry, Menu, Listbox, Canvas etc.), Event Handling, Building Simple GUI Apps Textbooks: 1. Python Programming-An Object Oriented approach, Anita Goel 2. Python Programming using Problem Solving Approach Reema Thareja 2020 3. Exploring Python, Budd T A, McGraw-Hill Education, 1st Edition, 2011. Reference Book: 1. Python: The Complete Reference, Martin C. Brown, Mc Graw-Hill, 2018 2. Fundamentals of Python, Kenneth A. Lambert. (2019), First Programs,2nd Edition, CENGAGE Publication. Activities: Outcome: Explain the basic features, syntax, data types, and operators of Python programming. Activity: Conduct a "Python Basics Lab" where students write small programs to demonstrate literals, variables, data types, and operators (e.g., swapping numbers, simple calculator). Evaluation Method: Lab performance checklist (execution of 3 mini tasks) Short quiz with multiple-choice and fill-in-the-blanks on syntax, data types, and operators Outcome: Apply control flow constructs, functions, and modules to develop structured Python programs. Activity: Group activity - "Python Problem Solving Challenge": Students solve real-life problems (e.g., finding prime numbers, grade calculator, menu-driven calculator) using control structures, functions, and importing standard modules. Evaluation Method: Code submission with proper use of functions/modules (20%) Viva-voce to explain logic and flow of control (40%) Unit test with scenario-based programming questions (40%) Outcome: Demonstrate the use of sequences, sets, and dictionaries for effective data handling and manipulation.
Activity: Hands-on mini project "Student Data Manager": Students create a program using lists, tuples, sets, and dictionaries to store and manipulate student records (e.g., marks, courses, hobbies). Evaluation Method: Practical demo of program with at least 5 data operations (add, search, delete, update, traverse) Evaluation rubric for correctness, efficiency, and use of appropriate data structure Outcome: Implement file handling techniques and apply exception handling mechanisms for robust applications. Activity: Individual assignment - "File-Based Address Book": Students create a program to store, update, and retrieve data from files, with exception handling for invalid inputs or missing files. Evaluation Method: Assessment of program correctness (file read/write, append, delete, exception handling) Short quiz with error-tracing and debugging questions (given code with errors, students identify and correct) Outcome: Develop object-oriented and GUI-based applications using Python. Activity: Mini Project "Student Information System with GUI": Students design a simple Tkinter-based application with classes/objects for handling student data, including basic GUI widgets (Entry, Button, Listbox). Evaluation Method: Project demo and presentation (50%) Rubric-based evaluation for OOP concepts (classes, inheritance, encapsulation) and GUI design (widgets, event handling) (30%) Peer review/feedback on usability (20%)
SEMESTER-II COURSE 3: PYTHON PROGRAMMING AND DATA STRUCTURES Practical Credits: 1 2 hrs/week 1. Basic Python Programs: a. Write a program to display basic details (name, roll number, department) using print() and demonstrate different literal types (int, float, string, boolean, complex). b. Write a program to perform arithmetic, relational, logical, bitwise, and assignment operations on given inputs. 2. Control Flow Practice a. Write a program to find the largest of three numbers using if-elif-else. b. Write a program to check whether a number is prime or not using loops. c. Write a program to illustrate the use of loop control statements (break, continue, pass). 3. Functions and Recursion a. Write a program to define a function to calculate factorial of a number (using recursion). b. Write a program to demonstrate different types of function arguments (default, positional, keyword, variable-length). 4. Write a program to illustrate string slicing, concatenation, repetition, and built-in methods. 5. Write a program to create a list of numbers, perform insertion, deletion, searching, sorting, and list comprehension. 6. Write a program to demonstrate tuple packing, unpacking, and immutability. 7. Write a program to implement set operations (union, intersection, difference, subset, superset). 8. Write a program to create a dictionary of student roll numbers and marks, and perform add, update, delete, and traversal operations. 9. Write a program to read and display count of vowels, consonants, digits, and spaces of a text file. 10. Write a program to copy the contents of one file into another file. 11. Write a program to read and process student marks from a CSV file (calculate average, highest, lowest).
SEMESTER-II COURSE 4: STATISTICAL FOUNDATIONS FOR DATA SCIENCE Theory Credits: 3 3 hrs/week Course Objectives 1. To introduce the fundamental concepts of probability and statistics for quantifying and analyzing uncertainty in real-world problems. 2. To develop an understanding of random variables, expectations, and common probability distributions (discrete and continuous). 3. To build the ability to summarize and describe data using measures of central tendency, dispersion, correlation, and visualization techniques. 4. To equip students with statistical tools for modeling relationships using correlation and regression analysis. 5. To provide knowledge of estimation and hypothesis testing for making valid inferences from sample data about populations. Course Outcomes At the end of the course, students will be able to: 1. Apply the basic rules of probability, conditisolve problems involving uncertainty. 2. Compute and interpret descriptive statistics (mean, median, mode, variance, standard deviation, correlation, covariance) and represent data effectively using histograms, bar charts, and scatter plots. 3. Analyze random variables and probability distributions (Binomial, Poisson, Normal, Exponential, etc.) to model real-life situations. 4. Perform correlation and regression analysis to identify and interpret relationships between variables. 5. Conduct statistical inference through confidence intervals and hypothesis testing (z-test, t-test, chi-square, F-test) for decision-making. Unit 1: Fundamentals of Probability & Basic Statistics Probability: Concept of Uncertainty, Axioms and rules of probability, Conditional probability
Measures of central tendency: Mean, Median, Mode Measures of dispersion: range, interquartile range, variance, standard deviation Introduction to correlation and covariance Data representation: histograms, bar charts, scatter plots Unit 2: Random Variables, Expectation, and Variance Random variables: definition, types (discrete & continuous), and properties, Probability mass function (PMF) and probability density function (PDF), Cumulative distribution function (CDF), Mathematical expectation (mean), variance, and standard deviation, Moments and moment-generating functions Unit 3: Probability Distributions Discrete distributions: Binomial, Poisson, Geometric, Negative Binomial distributions - definitions, properties, and examples Continuous distributions: Uniform, Normal (Gaussian), Exponential, Gamma distributions -definitions, properties, and applications Joint, marginal, and conditional distributions, Introduction to Central Limit Theorem Unit 4: Correlation and Regression Bivariate data and scatter plots Correlation: Pearson and Spearman coefficients, interpretation Simple linear regression: model, estimation, properties, and analysis of variance Multiple linear regression basics (conceptual understanding) Residuals and goodness of fit Unit 5: Statistical Inference, Estimation, and Hypothesis Testing Population and sample, parameters and statistics, Sampling distributions, Point and interval estimation (confidence intervals), Tests of significance: z-test, t-test, chi-square test, and F-test, p-values and errors (Type I & II), Power of a statistical test Textbooks: 1. Probability and Statistics for Engineers and Scientists, Ronald E. Walpole, Wiley. 2. Sheldon M. Ross, Introduction to Probability and Statistics for Engineers and Scientists
SEMESTER-II COURSE 4: STATISTICAL FOUNDATIONS FOR DATA SCIENCE Practical Credits: 1 2 hrs/week Advanced Spreadsheets/Excel Lab/PSPP Open Source 1. Construct a contingency table from sales data and compute conditional probabilities. Verify independence of variables. 2. a positive result. 3. Calculate measures of central tendency (mean, median, mode) for student marks dataset. 4. Compute measures of dispersion (range, variance, standard deviation, IQR) for the same dataset and interpret variability. 5. Create a histogram of student marks and comment on the shape of the distribution. 6. Prepare bar charts of categorical data (e.g., Gender vs Section) and interpret group comparisons. 7. Generate scatter plots between Hours Studied and Exam Score, compute correlation and covariance, and interpret the relationship. 8. Random Variable Simulation: Simulate and visualize discrete/continuous random variables using Excel functions and Data Analysis Tool pack. 9. Expectation & Variance Calculation: Use Excel formulas to compute expected value, variance, and standard deviation from given datasets. 10. Modeling Discrete Probability Distributions: Generate and plot Binomial and Poisson distributions; analyze probabilities and mean/variance. 11. Modeling Continuous Distributions: Simulate Normal and Exponential distributions; use NORM.DIST, NORM.INV, EXPON.DIST functions. 12. Correlation Analysis: Calculate Pearson/Spearman correlation coefficients; visualize with scatter plots and trendlines. 13. Linear Regression in Excel: Fit a linear regression model, interpret coefficients, predict new values; use REGRESSION tool. 14. Statistical Inference & Estimation: Create confidence intervals using Excel formulas; visualize sampling distributions 15. Hypothesis Testing: Perform z-test, t-test, chi-square tests in Excel; interpret p-values and results.
SEMESTER-III COURSE 5: DATABASE MANAGEMENT SYSTEMS Theory Credits: 3 3 hrs/week Course Objectives: 1. To understand the fundamentals of data, information, and the evolution from file-based systems to modern database management systems. 2. To develop the ability to design conceptual data models using Entity-Relationship (ER) and Enhanced ER diagrams. 3. To explore relational model principles, such as keys, integrity constraints and normalization. 4. To perform data definition and manipulation using SQL commands including queries, joins, subqueries, views, and set operations. 5. To apply procedural logic using PL/SQL, incorporating control structures, functions, procedures, and database triggers. Course Outcomes: At the end of the course, students will be able to: 1. Describe the fundamentals of data, database systems, and the differences between file-based and database approaches. Compare and classify various DBMS architectures, data models, and their components, including the three-schema architecture. 2. Design conceptual data models using Entity-Relationship and Enhanced ER diagrams, applying generalization, specialization, and constraints. 3. Apply relational model concepts, including CODD rules and normalization techniques. 4. Construct and execute SQL queries for data definition, manipulation, aggregation, joining, and subqueries, including views and set operations. 5. Develop PL/SQL programs incorporating control structures, procedures, and functions to manage database behavior effectively. Unit 1. Overview of Database Management System: Introduction to data, information, database, database management systems, file-based system, Drawbacks of file-Based System, database approach, Classification of Database Management Systems, advantages of database approach, Various Data Models, Components of Database Management System, three schema architecture of data base, costs and risks of database approach.
Unit 2. Entity-Relationship Model: Introduction, the building blocks of an entity relationship diagram, classification of entity sets, attribute classification, relationship degree, relationship classification, reducing ER diagram to tables, enhanced entity-relationship model (EER model), generalization and specialization, IS A relationship and attribute inheritance, multiple inheritance, constraints on specialization and generalization, advantages of ER modeling. Unit 3. Relational Model: Introduction, CODD Rules, relational data model, concept of key, relational integrity, relational algebra, relational algebra operations, advantages of relational algebra, limitations of relational algebra, Functional dependencies and normal forms. Unit 4. Structured Query Language: Introduction, Commands in SQL, Data Types in SQL, Data Definition Language, Selection Operation, Projection Operation, Aggregate functions, Data Manipulation Language, Table Modification Commands, Join Operation, Set Operations, View, Sub Query. Unit 5. PL/SQL: Introduction, Shortcomings of SQL, Structure of PL/SQL, PL/SQL Language Elements,Data Types, Operators Precedence, Control Structures, Steps to Create a PL/SQL, Program, Iterative Control, Procedures, Functions. Textbooks: 1. Database System Concepts, Avi Silberschatz, Henry F. Korth,S. Sudarshan, Seventh Edition, McGraw-Hill 2. Database Management Systems by Raghu Ramakrishnan, McGrawhill Reference Books: 1. Fundamentals of Database Systems, Elmasri Navathe Pearson Education 2. An Introduction to Database systems, C.J. Date, A.Kannan, S.Swami Nadhan, Pearson
Activities: Outcome: Describe the fundamentals of data, database systems, and the differences between file-based and database approaches. Compare and classify various DBMS architectures, data models, and their components, including the three-schema architecture. Activity: Create a comparative presentation or infographic illustrating: File-based vs. DBMS approaches Types of DBMS architectures (1-tier, 2-tier, 3-tier) Data models and the three-schema architecture Evaluation Method: Rubric-based assessment of the presentation covering clarity, accuracy, and depth of comparison. Include a short quiz to test conceptual understanding. Outcome: Design conceptual data models using Entity-Relationship and Enhanced ER diagrams, applying generalization, specialization, and constraints. Activity: Model a university or hospital database using ER and Enhanced ER diagrams that shows: Entity sets, relationships Generalization/specialization Participation and cardinality constraints Evaluation Method: Diagram submission with peer review and instructor feedback. Use a checklist to assess completeness, correctness, and notation usage. Outcome: Apply relational model concepts, including CODD rules, and normalization techniques. Activity: Normalize a given Database upto 3NF. Evaluation Method: Written assignment graded on: Correctness of normalization steps Short-answer questions on CODD rules Outcome: Construct and execute SQL queries for data definition, manipulation, aggregation, joining, and subqueries, including views and set operations. Activity: Implement a mini-project (e.g., Library or Inventory DB) using SQL. Include: Table creation (DDL) Data manipulation (DML) Aggregation, joins, subqueries, views, and set operations Evaluation Method: Lab-based practical test with query execution and output validation. Include a viva to explain logic and optimization.
Outcome: Develop PL/SQL programs incorporating control structures, procedures and functions to manage database behaviour effectively. Activity: Build a PL/SQL-based payroll or student grading system using: Procedures and functions Control structures (IF, LOOP) Triggers for automated updates Evaluation Method: Code review and demonstration. Evaluate based on: Syntax correctness Logical flow
SEMESTER-III COURSE 5: DATABASE MANAGEMENT SYSTEMS Practical Credits: 1 2 hrs/week Experiment 1 : Database: Inventory Management Table 1: Products Structure: Column Name Data Type Constraints product_id INT PRIMARY KEY product_name VARCHAR(50) NOT NULL price DECIMAL(10,2) CHECK(price > 0) stock_qty INT CHECK(stock_qty >= 0) Sample Data: product_id product_name price stock_qty 1 Pen 10.00 100 2 Notebook 50.00 200 3 Stapler 120.00 50 4 Marker 25.00 80 5 File Folder 60.00 150 Table 2: Suppliers Structure: Column Name Data Type Constraints supplier_id INT PRIMARY KEY supplier_name VARCHAR(50) NOT NULL contact_no VARCHAR(20) UNIQUE product_id INT FOREIGN KEY REFERENCES Products(product_id) Sample Data: supplier_id supplier_name contact_no product_id 101 StationeryMart 9876543210 1 102 PaperWorld 9876500000 2 103 OfficeSupplies 9876512345 3 104 MarkerHub 9876522222 4 105 FileDepot 9876533333 5
Section A: DDL (Data Definition Language) 1. Create a database called InventoryDB. 2. Create a table Products and table Suppliers with the specified columns and constraints: Section B: DML (Data Manipulation Language) 4. Insert at least 5 rows into the Products table. 5. Insert at least 5 rows into the Suppliers table. 6. Update the stock quantity of 7. Delete a supplier with a specific supplier_id. 8. Section C: DQL (SELECT Queries) 9. Display all records from the Products table. 10. Display only product_name and price of all products. 11. List all products that have a stock quantity less than 100. 12. Show all products between 20 and 100 price range. 14. Find the average price of products. 15. Display the total number of products in the inventory. 16. Show the maximum and minimum stock quantities. 17. Count how many suppliers supply each product. 18. Show all products where price > 50 AND stock_qty > 100. 19. Show all products where price < 20 OR stock_qty < 80. 21. List all suppliers along with the product they supply (use INNER JOIN). 23. Find products whose name has exactly 5 characters 24. Find suppliers who supply products costing more than 100. Experiment 2 : ONLINE BOOKSTORE DB An online book store wants to implement a BOOKSTORE DB for managing their online transactions by using the following tables. Authors Table Column Name Data Type Constraints author_id INTEGER PRIMARY KEY first_name VARCHAR NOT NULL last_name VARCHAR NOT NULL nationality VARCHAR NULL allowed Books Table Column Name Data Type Constraints book_id INTEGER PRIMARY KEY
Title VARCHAR NOT NULL author_id INTEGER FOREIGN KEY REFERENCES Authors publication_year INTEGER Price DECIMAL Customers Table Column Name Data Type Constraints customer_id INTEGER PRIMARY KEY first_name VARCHAR NOT NULL last_name VARCHAR NOT NULL Email VARCHAR UNIQUE, NOT NULL Address VARCHAR NOT NULL Orders Table Column Name Data Type Constraints order_id INTEGER PRIMARY KEY customer_id INTEGER FOREIGN KEY REFERENCES Customers book_id INTEGER FOREIGN KEY REFERENCES Books order_date DATE NOT NULL quantity INTEGER NOT NULL SAMPLE DATA SET for BOOKSTORE DB Authors Table author_id first_name last_name nationality 1 Jane Austen British 2 George Orwell British 3 Gabriel Garcia Marquez Colombian 4 Toni Morrison American 5 Mark Twain American 6 Harper Lee American 7 Fyodor Dostoevsky Russian Books Table book_id Title author_id publication_year price
101 Pride and Prejudice 1 1813 12.99 102 1984 2 1949 9.50 103 One Hundred Years of Solitude 3 1967 15.00 104 Beloved 4 1987 11.25 105 Animal Farm 2 1945 8.75 106 Adventures of Huckleberry Finn 5 1884 10.50 107 To Kill a Mockingbird 6 1960 14.00 Customers Table customer_id first_name last_name Email address 201 Alice Smith alice.s@example.com 12 Oak St, London 202 Bob Johnson bob.j@example.com 45 Pine Ave, Oxford 203 Charlie Brown charlie.b@example.com 78 Maple Rd, Bristol 204 Diana Prince diana.p@example.com 34 Queen St, York 205 Edward Norton edward.n@example.com 22 River Ln, Leeds 206 Fiona Hall fiona.h@example.com 56 Lake Dr, Bath 207 Greg Miller greg.m@example.com 89 Park Ave, Glasgow Orders Table order_id customer_id book_id order_date Quantity 301 201 101 2025-07-20 1 302 202 102 2025-07-21 2 303 201 105 2025-07-22 1 304 203 103 2025-07-23 1 305 204 106 2025-07-24 1 306 205 107 2025-07-25 3 307 206 104 2025-07-26 2 Section A: DDL (Schema Design & Constraints) 1. Write SQL statements to create all 4 tables (Authors, Books, Customers, Orders) with: o Primary Keys o Foreign Keys o Appropriate data types o NOT NULL constraints where necessary. 2. Alter the Books table to add a constraint that price must be greater than 0.
Experiment 3: EMPLOYEE DB An enterprise wants to automate its employee management process by implementing an Employee Database.The goal is to replace manual record-keeping with a centralized system that stores employee, department, and project details. Use the following table structures and data set to implement Employee DB. EmployeeDB - Table Structures 1. Departments Table Column Type Constraints dept_id INT PRIMARY KEY dept_name VARCHAR UNIQUE, NOT NULL location VARCHAR NOT NULL 2. Employees Table Column Type Constraints emp_id INT PRIMARY KEY first_name VARCHAR NOT NULL last_name VARCHAR NOT NULL email VARCHAR UNIQUE, NOT NULL phone VARCHAR CHECK (phone LIKE '--____') hire_date DATE NOT NULL job_title VARCHAR NOT NULL salary DECIMAL CHECK (salary > 0) dept_id INT FOREIGN KEY REFERENCES Departments(dept_id) manager_id INT FOREIGN KEY REFERENCES Employees(emp_id) (self-referential) 3. Projects Table Column Type Constraints project_id INT PRIMARY KEY project_name VARCHAR NOT NULL start_date DATE NOT NULL end_date DATE NULL dept_id INT FOREIGN KEY REFERENCES Departments(dept_id)
104 Diana Prince diana.p@corp.com 456-789-0123 2018-07-12 IT Manager 90000 2 NULL 105 Ethan Hunt ethan.h@corp.com 567-890-1234 2022-02-25 Marketing Lead 62000 4 NULL 106 Fiona Hall fiona.h@corp.com 678-901-2345 2017-11-01 Finance Manager 85000 3 NULL 107 Greg Miles greg.m@corp.com 789-012-3456 2023-04-15 IT Support 45000 2 104 108 Hannah White hannah.w@corp.com 890-123-4567 2021-09-05 HR Executive 50000 1 101 109 Ian Scott ian.s@corp.com 901-234-5678 2020-11-20 Operations Analyst 56000 5 NULL 110 Julia Adams julia.a@corp.com 012-345-6789 2019-12-18 Legal Advisor 70000 6 NULL 3. Projects Table project_id project_name start_date end_date dept_id 201 Payroll System 2023-01-01 NULL 3 202 Website Upgrade 2023-02-10 NULL 2 203 Recruitment Drive 2023-03-05 NULL 1 204 Ad Campaign 2023-05-20 NULL 4 205 New CRM Tool 2023-04-15 NULL 7 206 Compliance Portal 2023-06-10 NULL 6 207 Inventory System 2023-07-01 NULL 5 208 AI Research 2023-08-05 NULL 8 209 Customer Feedback 2023-09-10 NULL 10 210 Procurement System 2023-10-01 NULL 9 4. Employee_Project Table emp_id project_id hours_allocated 102 202 120
104 202 80 103 201 100 106 201 150 101 203 50 105 204 70 107 202 60 109 207 90 110 206 110 108 203 40 Section A: DDL (Schema Creation & Modification) 1. Write SQL statements to create the above tables with the specified constraints 2. Alter the Employees table to add a column bonus DECIMAL(8,2) with default value0. 3. Drop the column bonus from Employees. Section B: DML (Insert, Update, Delete) 4. Insert at least 10 rows into Departments, Employees, Projects, and Employee_Project.(use the above data set) 5. Try inserting an employee with a negative salary (should fail due to CHECK constraint). 6. Update the salary of the employee with emp_id = 103 by 15%. 7. Delete an employee record who has resigned (choose any emp_id). 9. Change the department of an employee to "Research".(should fail due to FK constraint) Section C: DQL (Select Queries) 10. List all employees and their details. 11. Show all employees in the "HR" department. 12. Find employees with salaries between 50,000 and 80,000. 13. Retrieve employees hired after 2020. 14. Show employees who are in either the IT or Finance department. 15. Find employees whose email ends with "@corp.com". 16. List all employees with salary > 60,000 AND located in "New York". 17. Display employees in descending order of salary. 18. Count the number of employees in each department. 19. Show the average salary of employees department-wise. 20. Display departments where the average salary is greater than 70,000. 21. Find the number of employees in each project. 22. Display departments with more than 3 employees. 23. Show the sum of all salaries department-wise. 24. List all distinct department IDs from the Employees table.