*Semester 1: Core Foundations*

1. *Programming in C/Python* 

   - Basics of programming, data types, control structures, functions, file handling, and OOP concepts.

2. *Data Structures and Algorithms* 

   - Arrays, stacks, queues, linked lists, trees, graphs, sorting, and searching algorithms.

3. *Database Management Systems (DBMS)* 

   - Relational models, SQL, normalization, transactions, and NoSQL basics (MongoDB/Cassandra).

4. *Operating Systems* 

   - Process management, memory management, file systems, and virtualization.

5. *Discrete Mathematics* 

   - Logic, sets, relations, combinatorics, and graph theory.

6. *Lab Work* 

   - Programming in C/Python + DBMS/SQL assignments.

 

*Semester 2: Advanced Topics*

1. *Object-Oriented Programming (Java/C++) * 

   - Advanced OOP concepts, exception handling, multithreading, and GUI programming.

2. *Design and Analysis of Algorithms* 

   - Dynamic programming, greedy algorithms, NP-completeness, and complexity analysis.

3. *Computer Networks* 

   - OSI/TCP-IP models, routing protocols, network security, and IoT basics.

4. *Web Technologies* 

   - HTML/CSS, JavaScript, PHP/Node.js, REST APIs, and frontend frameworks (React/Angular).

5. *Software Engineering* 

   - SDLC, Agile/Scrum, UML diagrams, and project management tools (Jira).

6. *Lab Work* 

   - Java/C++ projects + Web development (full-stack mini-project).

 

*Semester 3: Specialization & Electives*

1. *Cloud Computing* 

   - AWS/Azure basics, SaaS/PaaS/IaaS, serverless architecture, and Docker/Kubernetes.

2. *Big Data Analytics* 

   - Hadoop/Spark, data preprocessing, machine learning basics, and visualization tools (Tableau).

3. *Mobile Application Development* 

   - Android/iOS app development (Kotlin/Swift) or cross-platform frameworks (Flutter/React Native).

4. *Advanced DBMS* 

   - Distributed databases, data warehousing, and OLAP.

5. *Electives (Choose 1–2)* 

   - IoT, Blockchain, Cybersecurity, Natural Language Processing (NLP), or Data Visualization.

6. *Project Phase I* 

   - Proposal submission and initial implementation.

7. *Professional Ethics & Cyber Laws* 

   - IT Act, GDPR, ethical hacking basics, and case studies.

 

*Semester 4: Capstone Projects & Industry Integration*

1. *Final Project (Dissertation)* 

   - End-to-end development of a software product/research project with industry mentorship.

2. *Internship* 

   - 6–8 weeks of industry experience (optional in some universities).

3. *Electives (Choose 1–2) * 

   - Quantum Computing, DevOps, Robotics Process Automation (RPA), or Advanced AI/ML.

4. *Seminar* 

   - Presentation of project/research findings to faculty and peers.

 

*Elective Tracks (Choose a Specialization) *

1. *Artificial Intelligence & Machine Learning* 

   - Neural networks, deep learning (TensorFlow/PyTorch), computer vision, and NLP.

2. *Cybersecurity* 

   - Cryptography, penetration testing, ethical hacking, and network security.

3. *Data Science* 

   - Predictive analytics, statistical modelling, and big data tools (Hadoop/Spark).

4. *Software Development* 

   - Microservices, DevOps (CI/CD pipelines), and cloud-native applications.

5. *Cloud & Distributed Systems* 

   - Edge computing, serverless architecture, and distributed algorithms.

 

*Additional Components*

- *Workshops/Seminars*: On emerging tech (AI, blockchain, AR/VR). 

- *Soft Skills*: Communication, teamwork, and technical writing. 

- *Research Methodology*: For dissertation preparation. 

- *Industry Visits/Guest Lectures*: By tech professionals.

 

*Assessment*

- *Exams*: Theory and practical’s (60–70% weightage). 

- *Projects/Labs*: 20–30% weightage. 

- *Presentations/Reports*: 10–20% weightage.