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INTEGRATED-B-TECH-M-TECH in Computer Science Engineering at Sri Krishna College of Engineering and Technology
Coimbatore, Tamil Nadu
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About the Specialization
What is Computer Science & Engineering at Sri Krishna College of Engineering and Technology Coimbatore?
This Integrated B.Tech + M.Tech in Computer Science and Engineering program at Sri Krishna College of Engineering and Technology offers a comprehensive five-year learning journey, blending undergraduate fundamentals with advanced postgraduate research. The curriculum is meticulously designed to meet the evolving demands of the Indian IT industry, providing a seamless transition from core computing concepts to specialized, cutting-edge domains like AI, Machine Learning, and Cloud Computing, preparing students for leadership roles.
Who Should Apply?
This program is ideal for ambitious 10+2 science graduates seeking a fast-tracked and in-depth career in computer science, including those aspiring for R&D roles or higher studies. It also caters to students who wish to gain a competitive edge in the Indian technology job market, aiming for positions as AI engineers, data scientists, software architects, or research associates in top-tier companies and innovative startups, with a strong emphasis on continuous learning.
Why Choose This Course?
Graduates of this program can expect to secure lucrative career paths as full-stack developers, machine learning engineers, cybersecurity analysts, or cloud architects in prominent Indian and multinational corporations. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly more. The integrated nature also facilitates direct entry into research and development, aligning with industry-recognized professional certifications like AWS, Azure, or Google Cloud.
Student Success Practices
Foundation Stage
Build Strong Programming Fundamentals- (Semester 1-2)
Focus intently on mastering basic programming concepts in languages like C, C++, and Python. Practice regularly on various coding platforms to solidify logical thinking and problem-solving skills. Thoroughly understand fundamental data structures and algorithms, as they are the bedrock for advanced computing topics.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, freeCodeCamp, Basic C/C++/Python IDEs
Career Connection
A strong foundation in programming and algorithmic problem-solving is absolutely critical for cracking technical interviews and excelling in initial software development roles across the IT industry.
Engage in Peer Learning and Group Projects- (Semester 1-2)
Form study groups to discuss complex topics, share insights, and collaboratively work on academic projects. This not only enhances individual understanding but also develops crucial teamwork and communication skills. Actively participate in classroom discussions and seek clarification from faculty and peers.
Tools & Resources
Microsoft Teams, Discord, GitHub for collaborative coding projects, College library and departmental resources
Career Connection
Teamwork, collaboration, and effective communication are highly valued in the modern IT industry, directly preparing students for real-world project-based work environments.
Explore Basic Engineering Applications- (Semester 1-2)
Beyond core Computer Science subjects, pay close attention to fundamental electrical, electronics, and mechanical engineering laboratories. Gaining a basic understanding of hardware contexts broadens technical perspective and can be highly beneficial for future specializations like IoT, embedded systems, or robotics.
Tools & Resources
Lab manuals and equipment, YouTube tutorials on basic circuits and mechanics, Local workshops for hands-on experience if available
Career Connection
Provides a holistic engineering view, which is increasingly relevant for interdisciplinary roles and for a deeper understanding of the physical layer of complex computing systems.
Intermediate Stage
Pursue Internships and Industry Certifications- (Semester 3-5)
Actively seek short-term internships during summer breaks or online virtual internships in areas like web development, data science, cybersecurity, or cloud. Obtain relevant industry certifications from recognized platforms like NPTEL, Coursera, or edX in trending technologies to validate specialized skills.
Tools & Resources
Internshala, AICTE Internship Portal, NPTEL/Coursera/edX for certification courses, LinkedIn for professional networking and job alerts
Career Connection
Internships provide invaluable practical experience and networking opportunities, while certifications validate acquired skills, significantly boosting resume value and employability for placements.
Contribute to Open Source Projects- (Semester 3-5)
Initiate contributions to open-source projects available on platforms like GitHub. This exposes students to real-world codebases, industry-standard version control systems, and collaborative development practices, significantly enhancing their coding, debugging, and software development lifecycle skills.
Tools & Resources
GitHub, GitLab, Stack Overflow for problem-solving, Google Summer of Code for structured programs
Career Connection
Demonstrates practical coding ability, understanding of industry-standard tools, and a proactive learning attitude, all of which are highly valued by recruiters at tech companies.
Participate in Technical Competitions- (Semester 3-5)
Engage actively in coding contests, hackathons, and project competitions organized by colleges, industry bodies, or online platforms. This involvement hones problem-solving, critical thinking, and innovation skills under competitive pressure, fostering a spirit of continuous improvement.
Tools & Resources
Kaggle for data science competitions, TopCoder/Codeforces for competitive programming, College Tech Fests, IEEE/ACM student chapters'''' events
Career Connection
Winning or even actively participating in technical competitions showcases talent, resilience, and passion for technology, making candidates stand out significantly to potential employers during recruitment drives.
Advanced Stage
Specialize and Build a Robust Portfolio- (Semester 6-8 (B.Tech part), Semester 9-10 (M.Tech part))
Identify a niche area such as AI/ML, Cybersecurity, Cloud Architecture, or Data Science and delve deeper through advanced electives, research projects, and self-study. Build a strong portfolio of projects, including your major project/thesis, meticulously showcasing your acquired expertise and problem-solving capabilities.
Tools & Resources
GitHub for project display and code sharing, Personal website or blog to showcase work, Medium for technical article writing, Industry-specific journals and research papers
Career Connection
A specialized skill set complemented by a robust project portfolio is essential for targeting specific high-paying roles and for demonstrating readiness to tackle complex industry challenges effectively.
Network with Industry Professionals and Alumni- (Semester 6-8 (B.Tech part), Semester 9-10 (M.Tech part))
Attend industry workshops, seminars, and conferences to stay updated with emerging trends and connect with professionals. Actively engage with alumni and industry experts on platforms like LinkedIn to gain insights, mentorship, and potential job leads. Leverage the college''''s alumni network proactively.
Tools & Resources
LinkedIn for professional networking, Industry-specific conferences and webinars, College alumni portal and networking events
Career Connection
Networking opens doors to hidden job markets, provides invaluable mentorship opportunities, and helps in understanding current industry trends and long-term career progression paths.
Focus on Thesis Research and Potential Publication- (Semester 9-10)
During the M.Tech phase, dedicate significant and sustained effort to your thesis/major project. Aim for high-quality, innovative research, explore novel solutions, and endeavor to publish your findings in reputable conferences or journals. This demonstrates advanced research acumen and problem-solving skills.
Tools & Resources
Scopus, Web of Science, arXiv for preprints, LaTeX for academic writing, Extensive research guidance from faculty advisors
Career Connection
A strong thesis and a publication record are invaluable for roles in R&D departments, academia, and for pursuing further doctoral studies, providing a significant competitive advantage in these highly specialized fields.
Program Structure and Curriculum
Eligibility:
- Candidates must have passed 10+2 (Higher Secondary Examination) with Physics, Chemistry, and Mathematics as compulsory subjects, with a minimum aggregate percentage as prescribed by AICTE/Anna University and the institution''''s admission norms. Admission is typically based on entrance examination scores.
Duration: 5 years / 10 semesters
Credits: 194 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23HS101 | Communicative English | Core | 3 | Grammar and Vocabulary, Listening and Speaking Skills, Reading Comprehension, Writing Professional Documents, Presentation Techniques, Soft Skills Development |
| 23MA101 | Matrices and Calculus | Core | 4 | Matrix Algebra, Eigenvalues and Eigenvectors, Differential Calculus, Integral Calculus, Functions of Several Variables, Vector Calculus |
| 23PH101 | Engineering Physics | Core | 4 | Quantum Physics, Material Science, Optics and Lasers, Fiber Optics and Sensors, Crystallography, Nanomaterials |
| 23CY101 | Engineering Chemistry | Core | 4 | Water Technology, Electrochemistry, Corrosion and its Control, Fuels and Combustion, Polymer Chemistry, Environmental Chemistry |
| 23CS101 | Problem Solving and Python Programming | Core | 3 | Algorithmic Problem Solving, Python Fundamentals, Control Flow and Functions, Data Structures in Python, File Handling, Object-Oriented Programming basics |
| 23GE181 | Engineering Practices Laboratory | Lab | 2 | Civil Engineering Practices, Mechanical Engineering Practices, Electrical Engineering Practices, Electronics Engineering Practices, Computer Hardware Assembly, Software Installation |
| 23CS181 | Problem Solving and Python Programming Laboratory | Lab | 2 | Python Basic Programs, Conditional Statements and Loops, Functions and Modules, Lists, Tuples, Dictionaries, String Manipulation, File Operations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MA201 | Advanced Calculus and Complex Analysis | Core | 4 | Partial Differential Equations, Fourier Series, Laplace Transforms, Analytic Functions, Complex Integration, Residue Theorem |
| 23PH201 | Material Science and Engineering | Core | 3 | Crystalline Materials, Conducting Materials, Semiconducting Materials, Dielectric Materials, Magnetic Materials, Nanomaterials |
| 23GE201 | Basic Civil and Mechanical Engineering | Core | 4 | Civil Engineering Materials, Building Construction, Surveying, Thermodynamics, Power Plants, Manufacturing Processes |
| 23EC201 | Basic Electrical and Electronics Engineering | Core | 4 | DC and AC Circuits, Semiconductor Devices, Rectifiers and Filters, Amplifiers, Digital Logic Gates, Transducers |
| 23CS201 | Data Structures | Core | 3 | Arrays and Pointers, Stacks and Queues, Linked Lists, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| 23CS281 | Data Structures Laboratory | Lab | 2 | Implementation of Stacks and Queues, Linked List Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice, Application-based problems |
| 23EE281 | Electrical and Electronics Engineering Laboratory | Lab | 2 | Circuit Laws Verification, Diode Characteristics, Transistor Characteristics, Logic Gate Experiments, AC Circuit Analysis, Basic Sensor Interfacing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MA301 | Discrete Mathematics | Core | 4 | Propositional and Predicate Logic, Set Theory and Relations, Functions and Sequences, Combinatorics, Graph Theory, Algebraic Structures |
| 23CS301 | Object Oriented Programming and Design | Core | 3 | OOP Concepts (Classes, Objects, Inheritance), Polymorphism and Abstraction, Exception Handling, UML Diagrams, Design Patterns, Java/C++ Programming |
| 23CS302 | Database Management Systems | Core | 3 | ER Modeling, Relational Model, SQL Queries, Normalization, Transaction Management, Database Security |
| 23CS303 | Computer Architecture and Organization | Core | 3 | Processor Design, Memory Hierarchy, I/O Organization, Pipelining, Instruction Set Architecture, Parallel Processing |
| 23CS304 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, Deadlocks |
| 23CS381 | Object Oriented Programming and Design Laboratory | Lab | 2 | Implementing OOP principles, UML Tool Practice, Design Pattern Implementation, Java/C++ Application Development, Debugging Techniques, Version Control |
| 23CS382 | Database Management Systems Laboratory | Lab | 2 | DDL and DML Commands, Advanced SQL Queries, PL/SQL Programming, Database Design, Front-end Integration, Project-based Learning |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MA401 | Probability and Statistics | Core | 4 | Probability Axioms, Random Variables, Probability Distributions, Sampling Distributions, Hypothesis Testing, Regression Analysis |
| 23CS401 | Design and Analysis of Algorithms | Core | 3 | Asymptotic Notations, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, NP-Completeness |
| 23CS402 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design, Software Testing, Project Management, Agile Methodologies |
| 23CS403 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer Protocols, Network Layer Protocols, Transport Layer Protocols, Application Layer Protocols |
| 23CS404 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Undecidability |
| 23CS481 | Algorithms Laboratory | Lab | 2 | Implementation of Sorting and Searching, Graph Traversal Algorithms, Dynamic Programming Solutions, Greedy Algorithm Implementations, Time and Space Complexity Analysis, Problem Solving with Algorithms |
| 23CS482 | Computer Networks Laboratory | Lab | 2 | Socket Programming, Network Configuration, Protocol Implementation, Packet Analysis with Wireshark, Client-Server Applications, Network Security Tools |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CS501 | Artificial Intelligence | Core | 3 | Problem Solving Agents, Search Algorithms (DFS, BFS, A*), Knowledge Representation, Machine Learning Basics, Neural Networks Fundamentals, Natural Language Processing Introduction |
| 23CS502 | Web Technologies | Core | 3 | HTML5, CSS3, JavaScript, Client-Side Scripting, Server-Side Technologies (Node.js/Django/PHP), Database Connectivity, Web Security, Responsive Web Design |
| 23CS503 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization, Code Generation |
| 23GE501 | Professional Ethics and Human Values | Core | 3 | Engineering Ethics, Moral Values, Corporate Social Responsibility, Global Issues, Ethical Decision Making, Human Rights |
| 23CSEL01 | Elective I | Elective | 3 | Topics include Cloud Computing, Big Data Fundamentals, Digital Image Processing, etc. |
| 23CS581 | Artificial Intelligence Laboratory | Lab | 2 | Implementing Search Algorithms, Logic Programming (Prolog), Python Libraries for AI, Basic Machine Learning Models, Data Preprocessing, Mini AI Projects |
| 23CS582 | Web Technologies Laboratory | Lab | 2 | HTML/CSS Layouts, JavaScript DOM Manipulation, Server-side Scripting, RESTful API Consumption, Database Integration for Web, Full-Stack Web Development |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CS601 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Feature Engineering, Ensemble Methods |
| 23CS602 | Cryptography and Network Security | Core | 3 | Symmetric Key Cryptography, Asymmetric Key Cryptography, Hash Functions, Digital Signatures, Network Security Protocols, Firewalls and IDS |
| 23CS603 | Cloud Computing | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security, Containerization (Docker, Kubernetes), Serverless Computing |
| 23CSEL02 | Elective II | Elective | 3 | Topics include Data Analytics, Mobile Computing, Internet of Things, etc. |
| 23CS681 | Machine Learning Laboratory | Lab | 2 | Implementing various ML algorithms, Using Scikit-learn, TensorFlow/PyTorch, Data Visualization, Model Training and Evaluation, Hyperparameter Tuning, Mini-project on a real-world dataset |
| 23CS682 | Cloud Computing Laboratory | Lab | 2 | Cloud Platform Setup (AWS/Azure/GCP), Virtual Machine Deployment, Container Orchestration, Serverless Function Deployment, Cloud Storage Services, Cloud Security Best Practices |
| 23CS691 | Mini Project I | Project | 2 | Problem Identification, Requirement Gathering, System Design, Implementation and Testing, Report Writing, Presentation Skills |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CS701 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transfer Learning, Deep Learning Frameworks (TensorFlow, PyTorch) |
| 23CS702 | Big Data Analytics | Core | 3 | Hadoop Ecosystem, MapReduce Programming, Spark Framework, NoSQL Databases, Data Warehousing, Data Stream Processing |
| 23GE701 | Entrepreneurship Development | Core | 3 | Business Idea Generation, Market Analysis, Business Plan Creation, Funding and Venture Capital, Legal Aspects of Business, Startup Ecosystem |
| 23CSEL03 | Elective III | Elective | 3 | Topics include Blockchain Technologies, Digital Forensics, Augmented Reality, etc. |
| 23CS781 | Deep Learning Laboratory | Lab | 2 | Implementing CNNs and RNNs, Image Classification, Natural Language Processing with Deep Learning, Generative Models, GPU Computing, Advanced Deep Learning Projects |
| 23CS782 | Big Data Analytics Laboratory | Lab | 2 | HDFS Operations, MapReduce Programming with Hadoop, Spark RDD and DataFrames, NoSQL Database Interaction, Data Ingestion and Processing, Case Studies with Big Data |
| 23CS791 | Project Work I | Project | 6 | Extensive Literature Survey, Problem Formulation, Detailed Design Document, Feasibility Study, Partial Implementation, Mid-term Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CS801 | Internet of Things | Core | 3 | IoT Architecture, IoT Protocols, Sensor Networks, Edge and Fog Computing, IoT Security, Smart Applications |
| 23CS802 | Compiler Optimization Techniques | Core | 3 | Control Flow Analysis, Data Flow Analysis, Loop Optimizations, Register Allocation, Peephole Optimization, Inter-procedural Analysis |
| 23CSEL04 | Elective IV | Elective | 3 | Topics include Cyber Physical Systems, Quantum Computing, DevOps, etc. |
| 23CS881 | IoT Laboratory | Lab | 2 | Microcontroller Programming (Arduino/Raspberry Pi), Sensor Interfacing, Actuator Control, Data Transmission over IoT Protocols, Cloud Integration for IoT, Building Smart Devices |
| 23CS891 | Project Work II | Project | 6 | Implementation of Proposed Design, Rigorous Testing and Validation, Result Analysis, Documentation (Thesis/Report), Final Presentation and Viva-Voce, Publication Readiness |
| 23CS892 | Industrial Internship / Practical Training | Internship | 2 | Real-world Industry Exposure, Application of Academic Knowledge, Professional Skill Development, Teamwork and Communication, Problem Solving in Industry, Internship Report and Presentation |
Semester 9
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CS901 | Advanced Data Structures and Algorithms | Core | 4 | Amortized Analysis, Advanced Tree Structures, Network Flow Algorithms, String Matching Algorithms, Computational Geometry, Randomized Algorithms |
| 23CS902 | Research Methodology and IPR | Core | 3 | Research Problem Formulation, Literature Review, Research Design, Data Collection and Analysis, Technical Report Writing, Intellectual Property Rights |
| 23CSEL05 | Elective V (M.Tech Level) | Elective | 3 | Topics include Data Privacy and Security, Advanced Computer Vision, Natural Language Processing, etc. |
| 23CSEL06 | Elective VI (M.Tech Level) | Elective | 3 | Topics include Cyber-Physical Systems, Software Defined Networks, Parallel and Distributed Computing, etc. |
| 23CS991 | Thesis / Major Project Phase I | Project | 6 | Identification of Research Gap, Extensive Literature Review, Hypothesis Formulation, Methodology Design, Initial Prototyping, Progress Report and Presentation |
Semester 10
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23CSEL07 | Elective VII (M.Tech Level) | Elective | 3 | Topics include Quantum Machine Learning, Edge AI, Ethical Hacking, etc. |
| 23CSEL08 | Elective VIII (M.Tech Level) | Elective | 3 | Topics include Augmented and Virtual Reality, Bio-inspired Computing, Financial Technology, etc. |
| 23CS1091 | Thesis / Major Project Phase II | Project | 12 | Full System Implementation, Experimental Evaluation, Result Analysis and Discussion, Thesis Writing and Documentation, Final Presentation and Viva-Voce, Potential for Publication |
