Semester 1: Foundation in Basic Sciences (Focus: Building Computational Basics)
Subject
Chapters/Topics
Brief Description
Engineering Mathematics I
1. Differential Calculus (limits, derivatives, Taylor’s series).
2. Integral Calculus (definite/indefinite integrals, applications).
3. Vector Calculus (vectors, dot/cross products).
4. Ordinary Differential Equations (first-order, linear).
Provides mathematical foundations for algorithm analysis, optimization, and modeling in computing.
Engineering Physics
1. Mechanics (Newton’s laws, motion).
2. Electromagnetism (circuits, fields).
3. Waves and Optics (interference, diffraction).
4. Modern Physics (quantum basics).
Introduces physical principles for hardware design, signal processing, and quantum computing concepts
Engineering Chemistry
1. Atomic Structure and Bonding.
2. Thermodynamics and Kinetics.
3. Semiconductors and Materials.
4. Electrochemistry.
Covers material properties for electronic components and energy storage in computing systems.
Basic Electrical Engineering
1. DC Circuits (Ohm’s law, Kirchhoff’s laws).
2. AC Circuits (phasors, impedance).
3. Digital Electronics (logic gates, circuits).
4. Electrical Machines.
Fundamentals for computer hardware, digital circuits, and power systems in computing devices.
Introduction to Programming
1. Basics of C/C++ (variables, loops, functions).
2. Control Structures.
3. Arrays and Strings.
4. Input/Output Handling.
Entry-level programming for problem-solving and software development
Labs
Physics Lab, Chemistry Lab, Programming Lab.
Experiments in basic measurements, material testing, and hands-on coding in C/C++.
Semester 2: Advanced Foundations (Focus: Computing and Discrete Mathematics)
Engineering Mathematics II
1. Linear Algebra (matrices, determinants, eigenvalues).
2. Probability and Statistics.
3. Complex Variables (analytic functions).
4. Fourier Series and Transforms.
Tools for algorithm design, data analysis, and signal processing in computing.
Data Structures
1. Arrays, Linked Lists, Stacks, Queues.
2. Trees and Graphs.
3. Sorting and Searching.
4. Hashing.
Core concepts for efficient data organization and manipulation in software.
Digital Logic Design
1. Number Systems and Boolean Algebra.
2. Combinational Circuits.
3. Sequential Circuits (flip-flops, counters).
4. Finite State Machines.
Design of digital circuits and logic for computer hardware and processors.
Basic Electronics
1. Diodes and Transistors.
2. Amplifiers and Oscillators.
3. Operational Amplifiers.
4. Electronic Circuits.
Fundamentals of electronic components used in computer systems and peripherals.
Labs
Data Structures Lab, Digital Logic Lab.
Coding for data structures and hardware simulation for logic circuits.
Semester 3: Core Computing Concepts (Focus: Algorithms and Systems)
Discrete Mathematics
1. Set Theory and Logic.
2. Relations and Functions.
3. Graph Theory.
4. Combinatorics.
Mathematical foundation for algorithm design, cryptography, and network analysis.
Algorithms
1. Algorithm Analysis (time/space complexity).
2. Divide and Conquer.
3. Greedy Algorithms.
4. Dynamic Programming.
Techniques for designing efficient algorithms for computational problems.
Computer Organization
1. CPU Architecture (Von Neumann, registers).
2. Memory Hierarchy (cache, RAM).
3. Instruction Set Architecture.
4. I/O Systems.
Understanding of computer architecture and hardware-software interaction.
Object-Oriented Programming
1. OOP Concepts (classes, objects, inheritance).
2. Java/C++ Programming.
3. Polymorphism and Encapsulation.
4. Exception Handling.
Software design using object-oriented principles for modular and scalable code.
Operating Systems
1. Processes and Threads.
2. Memory Management.
3. File Systems.
4. Scheduling Algorithms.
Fundamentals of OS design for resource management in computing systems.
Labs
Algorithms Lab, OOP Lab.
Implementation of algorithms and object-oriented programs in Java/C++.
Semester 4: Advanced Systems and Software (Focus: Software Engineering and Databases)
Database Systems
1. Relational Model and SQL.
2. Database Design (normalization, ER diagrams).
3. Transactions and Concurrency.
4. Query Optimization.
Principles of designing and managing databases for data-driven applications.
Computer Networks
1. Network Models (OSI, TCP/IP).
2. Data Link Layer (MAC, Ethernet).
3. Network Layer (IP, routing).
4. Transport Layer (TCP, UDP).
Fundamentals of networking for communication in distributed systems.
Software Engineering
1. Software Development Life Cycle (SDLC).
2. Agile and Waterfall Models.
3. Requirements Analysis.
4. Testing and Maintenance.
Methodologies for designing, developing, and maintaining software systems.
Theory of Computation
1. Finite Automata and Regular Expressions.
2. Context-Free Grammars.
3. Turing Machines.
4. Computability and Complexity.
Theoretical foundations of computation, languages, and complexity classes.
Engineering Mathematics III
1. Numerical Methods (interpolation, numerical integration).
2. Optimization Techniques.
3. Probability for Machine Learning.
Mathematical tools for optimization and numerical analysis in computing.
Labs
Database Lab, Networking Lab
SQL query implementation and network simulation experiments.
Semester 5: Advanced Computing and Applications (Focus: AI and Systems)
Artificial Intelligence
1. Search Algorithms (A*, heuristic).
2. Knowledge Representation.
3. Logic and Reasoning.
4. Introduction to Machine Learning.
Core concepts for building intelligent systems and AI applications.
Compiler Design
1. Lexical Analysis.
2. Syntax and Semantic Analysis.
3. Code Generation.
4. Optimization Techniques.
Techniques for designing compilers to translate high-level code to machine code.
Computer Architecture
1. Pipelining and Parallelism.
2. Multicore Processors.
3. Cache Coherence.
4. Advanced Instruction Sets.
Advanced study of processor design and performance optimization.
1. Data Preprocessing.
2. Statistical Analysis.
3. Data Visualization.
4. Introduction to Python Libraries (Pandas, NumPy).
Data Science Fundamentals
Foundations for analyzing and visualizing data for decision-making.
Elective I (e.g., Cloud Computing)
1. Cloud Models (IaaS, PaaS, SaaS).
2. Virtualization.
3. Cloud Security.
4. Distributed Systems.
Principles of cloud infrastructure and services for scalable computing.
Labs
AI Lab, Data Science Lab.
Experiments with AI algorithms and data analysis using Python tools.
Semester 6: Systems and Applications (Focus: Security and Advanced Programming)
Cyber Security
1. Cryptography (symmetric, asymmetric).
2. Network Security (firewalls, IDS).
3. Malware Analysis.
4. Ethical Hacking Basics.
Techniques for securing systems and networks against cyber threats.
Web Technologies
1. HTML, CSS, JavaScript.
2. Client-Server Architecture.
3. Frameworks (React, Node.js).
4. REST APIs.
Development of web applications and services for modern platforms.
Distributed Systems
1. Distributed Architectures.
2. Consensus Algorithms.
3. Fault Tolerance.
4. MapReduce and Hadoop.
Design of scalable, fault-tolerant systems for distributed computing.
Machine Learning
1. Supervised Learning (regression, classification).
2. Unsupervised Learning (clustering).
3. Neural Networks Basics.
4. Model Evaluation.
Algorithms and techniques for building predictive models.
Elective II (e.g., Mobile Computing)
1. Mobile App Development (Android/iOS).
2. Wireless Networks.
3. Mobile Security.
4. UI/UX Design.
Development of applications for mobile platforms and devices.
Labs
Web Development Lab, Security Lab.
Building web apps and conducting security penetration testing.
Semester 7: Advanced Topics and Projects (Focus: Specialization and Integration)
Big Data Technologies
1. Big Data Frameworks (Hadoop, Spark).
2. Data Lakes and Warehouses.
3. Stream Processing.
4. Scalability Issues.
Handling and processing large-scale data for real-time applications.
Software Project Management
1. Project Planning and Scheduling.
2. Risk Management.
3. Agile Methodologies.
4. Cost Estimation.
Managing software projects, from planning to delivery, with industry practices.
Health, Safety, and Environment (HSE)
1. Data Privacy Regulations.
2. Ethical Issues in Computing.
3. Green IT Practices.
4. Risk Assessment.
Compliance with regulations and ethical considerations in computing.
Elective III (e.g., Blockchain Technology)
1. Blockchain Architecture.
2. Smart Contracts.
3. Cryptocurrencies.
4. Decentralized Applications.
Principles of blockchain for secure, decentralized systems.
Industrial Training/Project
N/A
6-8 week internship in industry; mini-project on real-world problems.
Labs
Big Data Lab.
Experiments with big data tools like Spark and Hadoop.
Semester 8: Capstone and Emerging Technologies (Focus: Innovation and Professional Practice)
Major Project
N/A
Industry-sponsored research on topics like AI applications, cybersecurity, or cloud systems.
Seminar
Literature Review and Presentation.
Students present on trends like quantum computing, IoT, or AI ethics.
Elective IV (e.g., Internet of Things)
1. IoT Architecture.
2. Sensor Networks.
3. IoT Protocols (MQTT, CoAP).
4. IoT Security.
Design and implementation of interconnected smart devices.
Professional Practice
1. Ethics in Computing.
2. Intellectual Property and Contracts.
3. Leadership and Teamwork.
4. Career Development.
Career skills, industry ethics, and regulatory compliance in computing.
Comprehensive Viva
N/A
Oral exam covering the entire computer science curriculum.