| # | Category | Subject | L-T-P | Credits |
|---|---|---|---|---|
| 1 | Management Course-I | Optimization Techniques | 2-0-0 | 2 |
| 2 | Engineering Science/Basic Science | Probability & Statistics | 3-0-0 | 3 |
| 3 | Professional Core | Operating Systems | 3-0-0 | 3 |
| 4 | Professional Core | Database Management Systems | 3-0-0 | 3 |
| 5 | Professional Core | Software Engineering | 3-0-0 | 3 |
| 6 | Professional Core | Operating Systems & Software Engineering Lab | 0-0-3 | 1.5 |
| 7 | Professional Core | Database Management Systems Lab | 0-0-3 | 1.5 |
| 8 | Skill Enhancement Course | Python with Django | 0-1-2 | 2 |
| 9 | BS&H | Design Thinking & Innovation | 1-0-2 | 2 |
| Total | 15-1-10 | 21 |
A mandatory Community Service Project Internship of 8 weeks runs during the following summer vacation; the source document lists this requirement without an accompanying unit-wise syllabus.
Optimization Techniques
introduces the mathematical machinery (classical calculus-based optimization, linear programming, and dynamic programming) engineers use to find the best solution among competing design or resource-allocation options.
- Unit 1: Classical optimization — single/multivariable optimization, Lagrange multipliers, and Kuhn-Tucker conditions
- Unit 2: Linear programming — standard form, simplex algorithm, and geometry of LP problems
- Unit 3: Transportation problem — north-west corner rule, least-cost method, and Vogel’s approximation
- Unit 4: Nonlinear programming — one-dimensional minimization and penalty function methods
- Unit 5: Dynamic programming — multistage decision processes and the principle of optimality
Probability & Statistics
equips students with descriptive statistics, correlation/regression, probability distributions, and hypothesis testing — the statistical foundation that underlies data science, machine learning, and quality analysis later in the curriculum.
- Unit 1: Descriptive statistics — data types, central tendency, variability, skewness, and kurtosis
- Unit 2: Correlation and regression — correlation coefficients, linear and curvilinear regression
- Unit 3: Probability and distributions — conditional probability, Bayes’ theorem, and binomial/Poisson/normal distributions
- Unit 4: Sampling theory — sampling distributions, point/interval estimation, and the central limit theorem
- Unit 5: Hypothesis testing — Type I/II errors, significance levels, and t-test/F-test/chi-square test
Operating Systems
explains how an OS manages processes, memory, and files, and builds the concurrency and synchronization intuition (scheduling, deadlocks, semaphores) that every systems-facing IT role eventually needs.
- Unit 1: OS overview and system structures — OS services, system calls, and OS design/implementation
- Unit 2: Processes and CPU scheduling — process concepts, threads, multithreading models, and scheduling algorithms
- Unit 3: Synchronization and deadlocks — critical section problem, mutex locks, semaphores, and deadlock handling
- Unit 4: Memory management — paging, virtual memory, demand paging, and storage management
- Unit 5: File systems and protection — file access methods, directory implementation, and protection domains
Database Management Systems
covers relational database theory from ER modeling through SQL and normalization to transaction management, giving students the design and query skills needed to build reliable data-backed applications.
- Unit 1: Database fundamentals and ER modeling — schema architecture, entities, relationships, and specialization/generalization
- Unit 2: Relational model and basic SQL — relational algebra, relational calculus, and DDL/DML operations
- Unit 3: SQL querying — joins, nested queries, aggregation, grouping, and views
- Unit 4: Normalization — functional dependency, 1NF through 5NF, and BCNF
- Unit 5: Transactions and indexing — ACID properties, concurrency control, recovery, and B+ tree/hash indexing
Software Engineering
surveys the software development lifecycle from requirements and design through testing, quality management, and maintenance, establishing the process discipline behind building software as a team rather than an individual.
- Unit 1: Software life cycle models — waterfall, RAD, agile, and spiral models
- Unit 2: Project management and requirements — cost estimation, COCOMO, and SRS specification
- Unit 3: Software design — cohesion/coupling, agile practices, function-oriented design, and UI design
- Unit 4: Coding and testing — black-box/white-box testing, debugging, and software quality standards (ISO 9000, CMM)
- Unit 5: CASE tools and maintenance — CASE environments, software maintenance, and software reuse
Operating Systems & Software Engineering Lab
a joint lab pairing OS-level systems programming (scheduling, IPC, memory management) with software-engineering artefacts (SRS, UML diagrams, test cases) for real mini-projects.
- UNIX commands, system calls, and CPU scheduling/page-replacement algorithm simulations
- Semaphore/monitor-based synchronization and Banker’s Algorithm deadlock avoidance
- Requirement analysis, ER/DFD diagrams, UML modeling, and test-case design for sample applications
Database Management Systems Lab
hands-on SQL and PL/SQL practice covering everything from table creation through stored procedures, cursors, triggers, and JDBC connectivity.
- DDL/DML/DCL commands, nested queries, aggregate functions, and views
- PL/SQL control structures, procedures, functions, cursors, and triggers
- Database connectivity from Java programs using JDBC
Python with Django
moves from Python’s web-development libraries into full-stack web application development with the Django framework, covering authentication, database integration, and cloud deployment.
- Unit 1: Python web libraries — Tkinter, Requests, BeautifulSoup4, and lightweight frameworks like Flask
- Unit 2: Django fundamentals — MVC/MTV architecture, URL mapping, templates, and models
- Unit 3: Authentication — Django’s authentication system, registration, and email integration
- Unit 4: Database integration — migrations, CRUD operations, sessions, and cookies
- Unit 5: Cloud deployment — deploying a Django application to Heroku with static file handling
Design Thinking & Innovation
introduces design thinking as a structured, human-centred problem-solving process and connects it to product development and entrepreneurial innovation.
- Unit 1: Introduction to design thinking — design elements, principles, and history
- Unit 2: The design thinking process — empathize, analyze, ideate, and prototype
- Unit 3: Innovation — the relationship between creativity and innovation in organizations
- Unit 4: Product design — problem formulation, product strategy, and specifications
- Unit 5: Design thinking in business — applying design thinking to startups and business model testing