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M-TECH in Data Science at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Data Science at SRM Institute of Science and Technology Chengalpattu?
This M.Tech Data Science program at SRM Institute of Science and Technology focuses on equipping students with advanced analytical and computational skills to extract insights from complex data. It addresses the burgeoning demand for data professionals across diverse Indian industries, preparing graduates for roles that drive innovation and data-driven decision-making. The curriculum blends theoretical foundations with practical applications, emphasizing real-world problem-solving relevant to the Indian market.
Who Should Apply?
This program is ideal for engineering graduates from CSE, IT, ECE, EEE, EIE, ICE or postgraduates in MCA/M.Sc (CS/IT/SE) holding a minimum of 60% aggregate. It caters to fresh graduates aspiring to kickstart a career in data science, working professionals aiming to upskill for leadership roles, and career changers transitioning into the rapidly evolving analytics and AI sectors, who possess a strong analytical aptitude and basic programming knowledge.
Why Choose This Course?
Graduates of this program can expect to secure lucrative career paths as Data Scientists, Machine Learning Engineers, Data Analysts, and AI Specialists in top Indian and multinational companies. Entry-level salaries typically range from INR 6-10 lakhs per annum, with experienced professionals earning significantly more. The program fosters critical thinking and problem-solving, aligning with professional certifications and promoting rapid growth trajectories within India''''s booming digital economy.
Student Success Practices
Foundation Stage
Master Programming & Math Fundamentals- (Semester 1-2)
Dedicate time to thoroughly understand Python, data structures, algorithms, and mathematical concepts like linear algebra and probability. These are the bedrock of data science. Regularly practice coding problems and solve mathematical exercises from textbooks and online platforms to solidify your grasp.
Tools & Resources
HackerRank, LeetCode, Coursera (Python, Linear Algebra), GeeksforGeeks, Khan Academy (Probability & Statistics)
Career Connection
A strong foundation ensures you can efficiently implement complex algorithms and understand the theoretical underpinnings, crucial for cracking technical interviews and building robust data science models.
Build a Strong Project Portfolio Early On- (Semester 1-2)
Start working on small data analysis projects, even if they are basic, using publicly available datasets. Focus on applying learned concepts from your courses, like data cleaning, exploratory data analysis, and basic machine learning models. Document your code and insights on platforms like GitHub.
Tools & Resources
Kaggle (datasets and competitions), GitHub, Jupyter Notebooks, Google Colab
Career Connection
A portfolio demonstrates practical skills beyond academics, making you stand out to recruiters during internship and placement drives. It shows initiative and a proactive learning approach.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups with classmates to discuss difficult concepts, solve problems together, and prepare for exams. Teaching others can reinforce your own understanding. Participate in department workshops and seminars to broaden your exposure to current trends and research.
Tools & Resources
Discord/WhatsApp groups, University Library resources, Departmental seminars
Career Connection
Develops teamwork, communication skills, and diverse perspectives, which are highly valued in industry settings. Networking with peers can also lead to future collaborative opportunities.
Intermediate Stage
Seek Industry Internships & Capstone Projects- (Semester 3-4)
Actively apply for internships during semester breaks or pursue capstone projects with industry mentorship. This hands-on experience in a real-world setting provides invaluable exposure to industry tools, workflows, and challenges, bridging the gap between academia and professional practice.
Tools & Resources
LinkedIn Jobs, Internshala, SRMIST Career Development Centre, Company career pages
Career Connection
Internships are often a direct pathway to pre-placement offers (PPOs) and significantly boost your resume for full-time roles, providing practical experience and networking opportunities.
Specialize and Dive Deeper- (Semester 3-4)
Beyond core courses, identify areas within Data Science (e.g., Deep Learning, NLP, Big Data Engineering) that genuinely interest you. Take relevant electives, complete online certifications, and conduct mini-projects in your chosen niche to develop specialized expertise and make your profile unique.
Tools & Resources
Coursera (Deep Learning Specialization), edX (Data Engineering programs), NPTEL courses, TensorFlow/PyTorch documentation
Career Connection
Specialized skills are highly sought after in specific roles, making you a more attractive candidate for targeted positions and enabling you to command better salary packages.
Participate in Hackathons & Data Challenges- (Semester 3-4)
Engage in university-level, national, or even international hackathons and data science competitions. These events provide intense, time-bound problem-solving experiences, exposure to diverse datasets, and opportunities to collaborate, learn new tools, and showcase your skills under pressure.
Tools & Resources
Kaggle Competitions, Analytics Vidhya, Major League Hacking (MLH) events, SRMIST Tech Fests
Career Connection
Winning or even participating actively in competitions demonstrates problem-solving abilities, resilience, and practical application of knowledge, which are highly valued by employers and enhance your resume.
Advanced Stage
Focus on Thesis/Project Excellence- (Semester 3-4 (Project Phase I & II))
Your final year project or thesis is your biggest opportunity to demonstrate expertise. Choose a challenging problem, conduct thorough research, implement innovative solutions, and ensure high-quality documentation and presentation. Aim for publishable work if possible.
Tools & Resources
Academic research papers (arXiv, Google Scholar), Mendeley/Zotero for referencing, LaTeX for professional documentation
Career Connection
A strong final project is a powerful talking point in interviews, showcases your ability to conduct independent research, and contributes significantly to your academic and professional credibility.
Practice Mock Interviews & Aptitude Tests- (Semester 3-4)
Regularly practice for technical interviews, including coding rounds, machine learning concepts, and behavioral questions. Solve aptitude tests (quantitative, logical reasoning, verbal) to prepare for company-specific assessments, which are a critical filter in Indian placements.
Tools & Resources
InterviewBit, Glassdoor (interview questions), Placement preparation books (RS Aggarwal), SRMIST Placement Cell workshops
Career Connection
Thorough preparation for interviews and aptitude tests significantly increases your chances of clearing placement rounds and securing offers from desired companies.
Network Professionally & Seek Mentorship- (Semester 3-4)
Attend industry conferences, workshops, and alumni meet-ups. Connect with professionals and alumni on LinkedIn. Seek mentorship from faculty or industry experts in your field of interest. Building a professional network opens doors to opportunities and provides valuable career guidance.
Tools & Resources
LinkedIn, Professional conferences (Data Science Congress, Cypher), SRMIST Alumni Network portal
Career Connection
Networking can lead to referrals, job opportunities, and invaluable insights into industry trends and career paths, giving you an edge in the competitive job market.
Program Structure and Curriculum
Eligibility:
- B.E. / B.Tech. in CSE / IT / SWE / ECE / EEE / EIE / ICE or MCA or M.Sc. (CS / IT / SE) with a minimum of 60% aggregate. GATE / SRMJEEE (PG) qualified candidates are preferred.
Duration: 2 years (4 semesters)
Credits: 76 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PDS23101 | Mathematical Foundations for Data Science | Core | 4 | Linear Algebra and Matrices, Calculus and Optimization, Probability and Random Variables, Statistical Inference and Hypothesis Testing, Regression and ANOVA |
| PDS23102 | Data Structures and Algorithms | Core | 4 | Introduction to Data Structures, Linear Data Structures, Non-Linear Data Structures, Graph Algorithms, Algorithm Design and Analysis |
| PDS23103 | Python for Data Science | Core | 4 | Python Fundamentals, Data Manipulation with Pandas, Numerical Computing with NumPy, Data Visualization with Matplotlib, Introduction to Machine Learning Libraries |
| PDS23104 | Database Management Systems | Core | 3 | Introduction to DBMS, Relational Model and SQL, Database Design, Transaction Management, Database Security and Administration |
| PDS23181 | Python for Data Science Lab | Lab | 2 | Python Programming Exercises, Data Manipulation using Pandas, Numerical Operations with NumPy, Data Visualization techniques, Basic Machine Learning Implementations |
| PDS23182 | Database Management Systems Lab | Lab | 2 | SQL Querying and Data Definition, Data Manipulation Language (DML), Stored Procedures and Functions, Database Normalization, Transaction Control |
| PDS23183 | Research Methodology | Core | 3 | Introduction to Research, Research Design and Methods, Data Collection and Analysis, Report Writing and Presentation, Ethics in Research |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PDS23201 | Machine Learning | Core | 4 | Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Ensemble Methods, Model Evaluation and Deployment |
| PDS23202 | Big Data Technologies | Core | 4 | Introduction to Big Data, Hadoop Ecosystem, Spark and Real-time Processing, NoSQL Databases, Data Ingestion and Management |
| PDS23203 | Data Visualization | Core | 3 | Fundamentals of Data Visualization, Visualization Techniques, Tools for Data Visualization, Interactive Visualizations, Storytelling with Data |
| PDS23XXX | Professional Elective I | Elective | 3 | Specialized topics based on elective chosen, Advanced concepts in chosen domain, Application of theory to specific problems, Emerging trends, Case studies and problem solving |
| PDS23XXX | Professional Elective II | Elective | 3 | Specialized topics based on elective chosen, Advanced concepts in chosen domain, Application of theory to specific problems, Emerging trends, Case studies and problem solving |
| PDS23281 | Machine Learning Lab | Lab | 2 | Implementation of Supervised Learning Algorithms, Implementation of Unsupervised Learning Algorithms, Model Training and Evaluation, Feature Engineering, Hyperparameter Tuning |
| PDS23282 | Big Data Technologies Lab | Lab | 2 | Hadoop File System Operations, MapReduce Programming, Spark Data Processing, NoSQL Database Operations, Data Ingestion tools |
| PDS23283 | Data Visualization Lab | Lab | 2 | Using Matplotlib and Seaborn, Interactive plots with Plotly, Dashboard creation with Tableau/PowerBI, Geospatial Data Visualization, Web-based Visualization tools |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PDS23301 | Deep Learning | Core | 4 | Fundamentals of Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Learning Frameworks and Applications |
| PDS23302 | Natural Language Processing | Core | 4 | Introduction to NLP, Text Preprocessing and Tokenization, Linguistic Models, Deep Learning for NLP, NLP Applications (Sentiment Analysis, Machine Translation) |
| PDS23XXX | Professional Elective III | Elective | 3 | Specialized topics based on elective chosen, Advanced concepts in chosen domain, Application of theory to specific problems, Emerging trends, Case studies and problem solving |
| PDS23XXX | Professional Elective IV | Elective | 3 | Specialized topics based on elective chosen, Advanced concepts in chosen domain, Application of theory to specific problems, Emerging trends, Case studies and problem solving |
| PDS23391 | Project Phase I | Project | 4 | Problem Identification and Literature Survey, Methodology Design, Tools and Technology Selection, Preliminary Implementation, Report Writing and Presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PDS23491 | Project Phase II | Project | 14 | System Design and Development, Experimentation and Result Analysis, Validation and Optimization, Comprehensive Report Preparation, Final Presentation and Thesis Defense |
