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INTEGRATED-M-TECH in Computer Science And Engineering With Data Science at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Computer Science and Engineering with Data Science at SRM Institute of Science and Technology Chengalpattu?
This Integrated M.Tech Computer Science and Engineering with Data Science program at SRM Institute of Science and Technology focuses on building a robust foundation in core computer science, augmented with advanced skills in data science, machine learning, and artificial intelligence. It prepares students for the high demand in India''''s booming data industry by providing a comprehensive, research-oriented curriculum, blending theoretical knowledge with practical applications.
Who Should Apply?
This program is ideal for ambitious 10+2 graduates with a strong aptitude for mathematics and problem-solving, seeking a direct and accelerated pathway to becoming a skilled data professional. It caters to those passionate about uncovering insights from data, building intelligent systems, and contributing to cutting-edge technological advancements in the Indian market.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths as Data Scientists, Machine Learning Engineers, Data Analysts, or AI Specialists in major Indian tech hubs like Bangalore, Hyderabad, and Chennai. Entry-level salaries typically range from INR 6-10 LPA, with significant growth potential. The integrated nature provides a distinct edge for advanced roles and research opportunities.
Student Success Practices
Foundation Stage
Master Programming Fundamentals and Logic- (Semester 1-2)
Dedicate time to consistently practice C and Java programming. Focus on understanding data structures and algorithms thoroughly. Engage with competitive programming platforms to hone logical thinking and problem-solving skills, which are crucial for advanced data science concepts.
Tools & Resources
Hackerrank, CodeChef, GeeksforGeeks, Jupyter Notebooks
Career Connection
A strong coding base is foundational for any data science role, enabling efficient data manipulation, algorithm implementation, and debugging, directly impacting placement readiness for technical interviews.
Build a Solid Mathematical and Statistical Base- (Semester 1-3)
Pay close attention to Calculus, Linear Algebra, Probability, and Statistics courses. These subjects form the backbone of machine learning and data analysis. Solve extra problems, attend tutorials, and use online resources to clarify concepts, ensuring a deep understanding.
Tools & Resources
Khan Academy, MIT OpenCourseware, NPTEL videos
Career Connection
A robust quantitative aptitude is indispensable for understanding complex ML algorithms and statistical modeling, which are core requirements for data scientist and analyst roles.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups with classmates to discuss challenging concepts and work on mini-projects together. Collaborative learning enhances understanding, exposes you to different problem-solving approaches, and builds teamwork skills vital for industry.
Tools & Resources
GitHub, Discord, Google Meet, SRMIST student forums
Career Connection
Teamwork and communication skills are highly valued by employers. Collaborative projects provide practical experience in version control and project management, boosting employability.
Intermediate Stage
Undertake Practical Data Science Projects- (Semester 3-5)
Beyond coursework, initiate personal projects applying machine learning algorithms, data visualization, and database management. Participate in hackathons and Kaggle competitions. This hands-on experience translates theoretical knowledge into practical skills.
Tools & Resources
Kaggle, GitHub, Python (Pandas, Scikit-learn, Matplotlib), SQL databases
Career Connection
A strong project portfolio demonstrates practical skills and problem-solving abilities to recruiters, making you a more attractive candidate for internships and entry-level data science jobs.
Seek Early Internships and Industry Exposure- (Semester 4-6 (especially summer breaks))
Actively look for summer internships or part-time roles in data analytics or software development. Even short-term engagements provide invaluable industry insights, networking opportunities, and a chance to apply academic learning in real-world scenarios.
Tools & Resources
LinkedIn, Internshala, SRMIST Placement Cell
Career Connection
Internships are often a direct path to full-time employment. They provide practical work experience, industry contacts, and enhance your resume significantly for future placements.
Specialize in a Niche Area of Data Science- (Semester 5-7)
As you progress, identify a specific area of interest within data science, such as Natural Language Processing, Computer Vision, or Time Series Analysis. Take relevant electives, complete online certifications, and build specialized projects to deepen your expertise.
Tools & Resources
Coursera, edX, Udemy (for specific certifications), TensorFlow/PyTorch
Career Connection
Specialized skills make you stand out in a competitive job market and align you with roles requiring specific expertise, potentially leading to higher-paying and more fulfilling career opportunities.
Advanced Stage
Focus on Advanced Research and Thesis Development- (Semester 7-10)
Leverage Project III, IV, and V for in-depth research in a chosen data science domain. Aim for publishing a research paper in conferences or journals. This demonstrates advanced problem-solving, critical thinking, and a commitment to academic excellence, particularly for M.Tech level.
Tools & Resources
Scopus, IEEE Xplore, arXiv, LaTeX for thesis writing
Career Connection
Research experience and publications are crucial for roles in R&D, academia, and for pursuing further higher education (Ph.D.). It showcases advanced analytical and problem-solving capabilities.
Intensive Placement Preparation and Skill Refinement- (Semester 8-10)
Dedicate significant time to mock interviews (technical and HR), resume building, and aptitude test preparation. Practice coding challenges, data structure, and algorithm questions. Network with alumni and industry professionals through workshops and seminars.
Tools & Resources
LeetCode, Glassdoor, SRMIST Career Development Center, LinkedIn
Career Connection
Thorough preparation directly translates into securing placements in top companies. It builds confidence, refines communication, and ensures you are ready for rigorous recruitment processes.
Develop Leadership and Soft Skills- (Semester 6-10)
Participate in student organizations, lead projects, and mentor junior students. Focus on improving presentation, negotiation, and interpersonal communication skills. These ''''power skills'''' are increasingly vital for career progression, especially in management or team lead roles.
Tools & Resources
Toastmasters International (if available), TED Talks for inspiration, SRMIST club activities
Career Connection
Beyond technical prowess, strong soft skills are critical for leadership roles, client interaction, and successful team collaboration, enabling faster career advancement in the Indian corporate landscape.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 with Physics, Mathematics, and Chemistry/Biotechnology/Biology/Technical Vocational subjects with 50% aggregate marks.
Duration: 5 years (10 semesters)
Credits: 216 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEH101T | English | Humanities and Social Sciences | 3 | Grammar and Vocabulary, Reading Comprehension, Writing Skills, Presentation Skills, Communication Strategies |
| 21LEM101T | Calculus and Linear Algebra | Basic Sciences | 4 | Matrices and Determinants, Eigenvalues and Eigenvectors, Differential Calculus, Integral Calculus, Vector Calculus |
| 21LEP101T | Physics | Basic Sciences | 3 | Modern Physics, Quantum Mechanics, Electromagnetism, Optics and Lasers, Material Science |
| 21LEP101L | Physics Laboratory | Basic Sciences | 1.5 | Experiments on Optics, Electrical Measurements, Semiconductor Devices, Magnetic Properties, Thermal Physics |
| 21LEC101T | Chemistry | Basic Sciences | 3 | Electrochemistry, Corrosion and its Control, Polymer Chemistry, Water Treatment, Spectroscopic Techniques |
| 21LEC101L | Chemistry Laboratory | Basic Sciences | 1.5 | Volumetric Analysis, Conductometric Titration, pH Metry, Viscosity Measurement, Water Hardness Determination |
| 21LCS101T | Programming with C | Engineering Sciences | 3 | C Language Fundamentals, Data Types and Operators, Control Structures, Functions and Arrays, Pointers and Structures, File Handling |
| 21LCS101L | Programming with C Laboratory | Engineering Sciences | 1.5 | C Program Development, Debugging Techniques, Function Implementation, Array and Pointer Usage, Basic Algorithm Implementation |
| 21LCE101L | Engineering Graphics and Design Laboratory | Engineering Sciences | 2.5 | Orthographic Projections, Isometric Views, Sectional Views, CAD Software Basics, Assembly Drawings |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEH201T | Environmental Science and Engineering | Humanities and Social Sciences | 3 | Ecosystems and Biodiversity, Environmental Pollution, Waste Management, Climate Change, Sustainable Development |
| 21LEM201T | Differential Equations and Transforms | Basic Sciences | 4 | Ordinary Differential Equations, Partial Differential Equations, Laplace Transforms, Fourier Series and Transforms, Z-Transforms |
| 21LEE201T | Basic Electrical and Electronics Engineering | Engineering Sciences | 3 | DC Circuits, AC Circuits, Diodes and Rectifiers, Transistors, Operational Amplifiers |
| 21LEE201L | Basic Electrical and Electronics Engineering Laboratory | Engineering Sciences | 1.5 | Circuit Analysis, Diode Characteristics, Transistor Amplifier Circuits, Op-Amp Applications, Basic Logic Gates |
| 21LCS201T | Data Structures | Program Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Hashing Techniques, Sorting and Searching Algorithms |
| 21LCS201L | Data Structures Laboratory | Program Core | 1.5 | Implementation of Data Structures, Algorithm Efficiency Analysis, Problem Solving with Data Structures, Practical Sorting Algorithms, Graph Traversal Algorithms |
| 21LCS202T | Object-Oriented Programming using Java | Program Core | 3 | OOP Concepts, Classes, Objects, Methods, Inheritance and Polymorphism, Exception Handling, Collections Framework, GUI Programming |
| 21LCS202L | Object-Oriented Programming using Java Laboratory | Program Core | 1.5 | Java Program Development, Debugging Java Applications, Implementing OOP Principles, Database Connectivity with Java, Multithreading Applications |
| 21LEL201L | Digital Marketing and Communications | Engineering Sciences | 1.5 | Digital Marketing Fundamentals, Search Engine Optimization (SEO), Social Media Marketing, Content Marketing, Email Marketing Campaigns |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEM301T | Probability and Statistical Methods | Basic Sciences | 4 | Probability Theory, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Correlation and Regression Analysis |
| 21LCS301T | Computer Architecture | Program Core | 3 | CPU Organization, Memory Hierarchy, Input/Output Organization, Pipelining and Parallelism, Instruction Set Architectures |
| 21LCS302T | Design and Analysis of Algorithms | Program Core | 3 | Algorithm Design Paradigms, Time and Space Complexity, Sorting and Searching Algorithms, Graph Algorithms, Dynamic Programming |
| 21LCS302L | Design and Analysis of Algorithms Laboratory | Program Core | 1.5 | Implementation of Algorithms, Empirical Analysis of Algorithms, Problem Solving Strategies, Data Structure Application, Complexity Evaluation |
| 21LCS303T | Operating Systems | Program Core | 3 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks and Concurrency |
| 21LCS303L | Operating Systems Laboratory | Program Core | 1.5 | Shell Scripting, Process Synchronization Problems, Memory Allocation Algorithms, System Calls Implementation, Deadlock Avoidance Techniques |
| 21LCS304T | Database Management Systems | Program Core | 3 | Relational Model, SQL Queries, ER Diagrams, Normalization, Transaction Management |
| 21LCS304L | Database Management Systems Laboratory | Program Core | 1.5 | SQL Query Optimization, Database Design and Implementation, PL/SQL Programming, NoSQL Database Exploration, Database Connectivity Applications |
| 21LCS305E | Introduction to Data Science | Professional Elective | 3 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Data Visualization, Basic Predictive Modeling |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEM401T | Discrete Mathematics | Basic Sciences | 4 | Logic and Proofs, Set Theory and Relations, Functions and Sequences, Graph Theory, Combinatorics and Probability |
| 21LCS401T | Computer Networks | Program Core | 3 | Network Topologies, OSI and TCP/IP Models, Network Layer Protocols, Transport Layer Protocols, Network Security Basics |
| 21LCS401L | Computer Networks Laboratory | Program Core | 1.5 | Network Configuration, Socket Programming, Packet Analysis using Wireshark, Routing Protocols Implementation, Network Security Tools |
| 21LCS402T | Compiler Design | Program Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| 21LCS403T | Web Technologies | Program Core | 3 | HTML5 and CSS3, JavaScript Fundamentals, Server-Side Scripting (PHP/Node.js), Web Frameworks (e.g., Bootstrap), API Development and Consumption |
| 21LCS403L | Web Technologies Laboratory | Program Core | 1.5 | Responsive Web Design, Dynamic Web Applications, Database Integration with Web, Frontend Framework Usage, Web Security Best Practices |
| 21LCS404E | Data Warehousing and Data Mining | Professional Elective | 3 | Data Warehouse Architecture, ETL Process, OLAP Operations, Association Rule Mining, Classification and Clustering |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS501T | Theory of Computation | Program Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| 21LCS502T | Internet of Things | Program Core | 3 | IoT Architecture, Sensors and Actuators, IoT Communication Protocols, Cloud Integration for IoT, IoT Security and Privacy |
| 21LCS502L | Internet of Things Laboratory | Program Core | 1.5 | Sensor Interfacing, IoT Device Programming, Data Transmission with Protocols, Cloud Platform Integration, IoT Application Development |
| 21LCS503T | Cryptography and Network Security | Program Core | 3 | Symmetric Key Cryptography, Asymmetric Key Cryptography, Hashing and Digital Signatures, Network Security Protocols (SSL/TLS), Firewalls and Intrusion Detection |
| 21LCS504E | Deep Learning | Professional Elective | 3 | Neural Network Fundamentals, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) |
| 21LCS5XXO | Open Elective I | Open Elective | 3 | Key topics depend on the chosen elective from a predefined interdisciplinary list |
| 21LCS504P | Industrial Training I / Summer Internship | Program Core | 1 | Industry Exposure, Practical Skill Application, Professional Development, Project Implementation, Technical Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS601T | Cloud Computing | Program Core | 3 | Cloud Deployment Models, Virtualization Technologies, Cloud Services (IaaS, PaaS, SaaS), Cloud Security, Containerization (Docker, Kubernetes) |
| 21LCS601L | Cloud Computing Laboratory | Program Core | 1.5 | Cloud Platform Deployment (AWS/Azure), Virtual Machine Management, Serverless Computing, Cloud Storage Solutions, Microservices Deployment |
| 21LCS602T | Artificial Intelligence | Program Core | 3 | Intelligent Agents, Search Algorithms, Knowledge Representation, Machine Learning Basics, Expert Systems |
| 21LCS603E | Big Data Analytics | Professional Elective | 3 | Big Data Concepts, Hadoop Ecosystem, Spark Framework, NoSQL Databases, Stream Processing |
| 21LCS6XXO | Open Elective II | Open Elective | 3 | Key topics depend on the chosen elective from a predefined interdisciplinary list |
| 21LCS603P | Industrial Training II / Summer Internship | Program Core | 1 | Advanced Industry Exposure, Specialized Project Work, Professional Networking, Career Exploration, Technical Report |
| 21LCS6XXL | Project I | Program Core | 3 | Problem Definition, Literature Review, Methodology Selection, Initial Design and Prototyping, Technical Report and Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS701T | Research Methodology and IPR | Program Core | 3 | Research Design, Data Collection and Analysis, Scientific Writing, Plagiarism and Ethics, Intellectual Property Rights (IPR) |
| 21LCS702T | Advanced Data Structures and Algorithms | Program Core | 3 | Advanced Tree Structures, Graph Algorithms, String Matching Algorithms, Amortized Analysis, Geometric Algorithms |
| 21LCS703T | Software Engineering | Program Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Patterns, Software Testing, Agile Methodologies |
| 21LCS704E | Data Ethics and Governance | Professional Elective | 3 | Data Privacy Regulations (GDPR, India''''s DPDPA), Ethical AI Principles, Data Security and Compliance, Bias and Fairness in Algorithms, Data Stewardship |
| 21LCS7XXO | Open Elective III | Open Elective | 3 | Key topics depend on the chosen elective from a predefined interdisciplinary list |
| 21LCS7XXL | Project II | Program Core | 3 | Advanced Project Development, System Implementation, Testing and Validation, Performance Evaluation, Technical Documentation and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS801T | Quantum Computing | Program Core | 3 | Quantum Mechanics Basics, Qubits and Quantum Gates, Quantum Algorithms, Quantum Cryptography, Quantum Machine Learning |
| 21LCS802T | Mobile Computing | Program Core | 3 | Mobile Device Architecture, Wireless Communication Technologies, Mobile Operating Systems, Mobile Application Development, Mobile Security |
| 21LCS805E | Recommender Systems | Professional Elective | 3 | Collaborative Filtering, Content-Based Filtering, Hybrid Recommender Models, Evaluation Metrics, Cold Start Problem |
| 21LCS8XXO | Open Elective IV | Open Elective | 3 | Key topics depend on the chosen elective from a predefined interdisciplinary list |
| 21LCS8XXL | Project III | Program Core | 6 | Comprehensive Project Development, Research and Experimentation, Advanced Implementation, Validation and Benchmarking, Technical Thesis and Viva Voce |
Semester 9
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS901T | Advanced Machine Learning | Program Core | 3 | Ensemble Methods, Bayesian Machine Learning, Kernel Methods, Dimensionality Reduction, Reinforcement Learning |
| 21LCS902T | Advanced Database Systems | Program Core | 3 | Distributed Database Systems, Object-Oriented Databases, Graph Databases, Big Data Storage, Database Security and Privacy |
| 21LDSC01T | Data Visualization and Storytelling | Professional Elective | 3 | Principles of Data Visualization, Visualization Tools (Tableau/PowerBI), Interactive Dashboards, Infographics, Storytelling with Data |
| 21LDSC02T | Time Series Analysis and Forecasting | Professional Elective | 3 | Time Series Components, ARIMA Models, Exponential Smoothing, Forecasting Techniques, GARCH Models |
| 21LCS9XXP | Project IV | Program Core | 6 | M.Tech Thesis Proposal, In-depth Literature Review, Research Design and Methodology, Preliminary Data Analysis, Research Paper Writing |
Semester 10
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS1001T | Ethical Hacking and Cyber Security | Program Core | 3 | Penetration Testing Methodologies, Vulnerability Assessment, Malware Analysis, Incident Response, Cyber Forensics |
| 21LDSC06T | Explainable AI | Professional Elective | 3 | Interpretability vs Explainability, Local Interpretable Model-agnostic Explanations (LIME), SHAP Values, Counterfactual Explanations, Responsible AI Development |
| 21LCS1002P | Project V | Program Core | 12 | M.Tech Thesis Finalization, Advanced Research and Development, System Implementation and Testing, Rigorous Evaluation and Validation, Scientific Paper Publication and Thesis Defense |
