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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 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 datasets. It is highly relevant to the burgeoning Indian industry, which sees significant demand for data scientists across e-commerce, finance, healthcare, and IT sectors. The program differentiates itself by integrating theoretical foundations with hands-on practical experience, preparing graduates for real-world challenges.
Who Should Apply?
This program is ideal for fresh graduates with a B.E/B.Tech or M.Sc/MCA in relevant fields seeking entry into the data science domain, and working professionals aiming to upskill or transition into advanced analytical roles. It caters to individuals passionate about problem-solving, possessing strong quantitative aptitude, and eager to leverage data for strategic decision-making in diverse Indian industries.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Data Scientists, Machine Learning Engineers, Data Analysts, or AI Specialists in top MNCs and startups. Entry-level salaries typically range from INR 6-10 LPA, growing significantly with experience. The program fosters critical thinking and problem-solving, aligning with certifications like AWS Certified Machine Learning Specialty or Google Professional Data Engineer.
Student Success Practices
Foundation Stage
Master Foundational Math & Programming- (Semester 1-2)
Focus on deeply understanding the mathematical underpinnings (linear algebra, probability, statistics) and strengthening programming skills in Python. Regularly solve problems from competitive programming platforms like HackerRank or LeetCode specific to data structures and algorithms.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Khan Academy (for Math)
Career Connection
A strong foundation is crucial for cracking technical interviews and building efficient machine learning models, laying the groundwork for advanced data science roles.
Hands-on Data Science Projects- (Semester 1-2)
Actively participate in lab sessions and take initiative to work on small personal projects using publicly available datasets (e.g., from Kaggle). Focus on data cleaning, exploratory data analysis, and basic model building to translate theoretical knowledge into practical skills.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebook, Scikit-learn, Pandas, Matplotlib
Career Connection
Showcasing practical projects demonstrates initiative and applied skills to potential employers, which is highly valued in the Indian job market for freshers.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups with classmates to discuss complex concepts, review lecture materials, and collaboratively solve problems. Explain concepts to each other to solidify understanding and develop communication skills essential for team-based data science roles.
Tools & Resources
Discord, Google Meet, Whiteboards
Career Connection
Enhances communication and teamwork abilities, critical for collaborative data science projects in industry, while improving academic performance.
Intermediate Stage
Specialize through Electives and Advanced Topics- (Semester 3)
Strategically choose electives that align with your career interests (e.g., NLP, Cloud Computing, Explainable AI). Dive deeper into these areas through online courses and advanced tutorials. Aim to build a portfolio project related to your chosen specialization.
Tools & Resources
Coursera, edX, NPTEL, Medium articles on ML/DL, GitHub
Career Connection
Specialization makes you a more targeted candidate for specific roles and provides a competitive edge in a niche market segment.
Seek Industry Internships- (Semester 3)
Actively apply for internships (summer or short-term) at startups or established companies in India. This provides invaluable real-world experience, exposure to industry tools, and networking opportunities. Focus on applying theoretical knowledge to business problems.
Tools & Resources
LinkedIn Jobs, Internshala, Naukri.com, College Placement Cell
Career Connection
Internships are often the gateway to full-time employment and offer practical exposure highly valued by Indian recruiters, sometimes leading to pre-placement offers.
Participate in Data Science Competitions- (Semester 3)
Engage in data science competitions on platforms like Kaggle, Analytics Vidhya, or even university-level hackathons. This sharpens problem-solving skills, exposes you to diverse datasets, and allows you to benchmark your abilities against others.
Tools & Resources
Kaggle, Analytics Vidhya, HackerEarth
Career Connection
Winning or performing well in competitions adds significant weight to your resume and provides tangible evidence of your skills to employers.
Advanced Stage
Undertake a Capstone Project- (Semester 4)
Dedicate significant effort to your M.Tech final year project (Project Work - Phase II). Choose a challenging problem, ideally with industry relevance, and implement a robust solution. Document your work meticulously, focusing on impact and scalability.
Tools & Resources
Research Papers, Git/GitHub for version control, Cloud platforms (AWS/GCP/Azure), Jupyter Notebooks
Career Connection
The capstone project serves as the ultimate showcase of your learning, skills, and ability to deliver a complete data science solution, often being the most important talking point in placement interviews.
Intensive Placement Preparation- (Semester 4)
Begin dedicated preparation for placements, focusing on resume building, mock interviews (technical, HR, and behavioral), and quantitative aptitude tests. Practice explaining your projects and theoretical concepts clearly and concisely. Network with alumni.
Tools & Resources
InterviewBit, GeeksforGeeks Interview Prep, LinkedIn, Alumni Mentorship
Career Connection
Thorough preparation directly translates into a higher chance of securing desirable placements in leading Indian and international companies, impacting your starting salary and career trajectory.
Develop Professional Communication & Soft Skills- (Semester 4)
Refine presentation skills, technical writing, and business communication. Participate in seminars, workshops, and deliver project presentations to hone your ability to articulate complex data insights to both technical and non-technical audiences, a vital skill in industry.
Tools & Resources
Toastmasters International, Online courses on communication skills, Regular presentations
Career Connection
Strong soft skills differentiate candidates and are often the deciding factor in hiring, enabling career progression into leadership and client-facing roles.
Program Structure and Curriculum
Eligibility:
- B.E/B.Tech in Computer Science and Engineering / Information Technology / Computer and Communication Engineering / Software Engineering / Electrical and Electronics Engineering / Electronics and Communication Engineering / Electronics and Instrumentation Engineering / Instrumentation and Control Engineering or MCA / M.Sc. (Computer Science / Information Technology / Software Engineering / Data Science / Applied Mathematics / Statistics / Computer Applications) with a minimum of 60% aggregate marks / 6.5 CGPA in the qualifying examination.
Duration: 2 years / 4 semesters
Credits: 70 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDE21001 | Mathematical Foundations for Data Science | PCC - Program Core Course | 4 | Probability and Statistics, Linear Algebra, Calculus and Optimization, Random Variables and Distributions, Hypothesis Testing |
| MDE21002 | Advanced Data Structures and Algorithms | PCC - Program Core Course | 4 | Algorithm Analysis, Graph Algorithms, Dynamic Programming, Hashing Techniques, Advanced Tree Structures |
| MDE21003 | Machine Learning Algorithms | PCC - Program Core Course | 4 | Supervised Learning, Unsupervised Learning, Ensemble Methods, Model Evaluation Metrics, Feature Engineering and Selection |
| MDE21L01 | Data Science Lab – I | PCC - Program Core Course | 2 | Python Programming for Data Science, Data Manipulation with Pandas, Data Visualization with Matplotlib/Seaborn, Scikit-learn for Machine Learning, Model Training and Prediction |
| MDE21L02 | Data Science Lab – II | PCC - Program Core Course | 2 | Implementation of Data Structures, Graph Algorithm Implementation, Sorting and Searching Algorithms, Algorithmic Problem Solving, Performance Analysis of Algorithms |
| MDE21RM01 | Research Methodology and IPR | PCC - Program Core Course | 3 | Research Design and Problem Formulation, Data Collection Methods, Statistical Analysis for Research, Technical Report Writing, Intellectual Property Rights and Ethics |
| MDE21AD01 | Audit Course – I | Audit Course | 0 | Stress Management Techniques, Value Education Principles, Professional Ethics in Practice, Social Responsibility, Yoga and Meditation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDE21004 | Deep Learning | PCC - Program Core Course | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| MDE21005 | Big Data Analytics | PCC - Program Core Course | 4 | Hadoop Ecosystem, Apache Spark for Big Data, NoSQL Databases, Stream Processing with Kafka, Data Warehousing and Data Lake Concepts |
| MDE21006 | Data Visualization and Storytelling | PCC - Program Core Course | 4 | Principles of Data Visualization, Data Storytelling Techniques, Interactive Dashboards (Tableau/Power BI), Exploratory Data Analysis Visualization, Infographics Design |
| MDE21L03 | Data Science Lab – III | PCC - Program Core Course | 2 | Deep Learning Model Implementation, Image Classification and Object Detection, Natural Language Processing Tasks, Model Deployment Strategies, Hyperparameter Tuning for Deep Learning |
| MDE21AD02 | Audit Course – II | Audit Course | 0 | Disaster Management Planning, Indian Constitution Fundamentals, Professional Communication Skills, Environmental Science, Human Values and Ethics |
| MDE21E03 | Natural Language Processing | PE - Program Elective I | 3 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Language Models (RNN, Transformers), Sentiment Analysis, Text Generation and Summarization |
| MDE21E04 | Cloud Computing for Data Science | PE - Program Elective II | 3 | Cloud Architectures and Deployment Models, AWS/Azure/GCP Services for Data Science, Serverless Computing, Data Storage in Cloud Environments, Cloud Security and Compliance |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDE21007 | Reinforcement Learning | PCC - Program Core Course | 4 | Markov Decision Processes, Q-learning and SARSA, Policy Gradients, Deep Reinforcement Learning, Applications in Robotics and Games |
| MDE21PJ01 | Project Work - Phase I | Project | 6 | Problem Identification and Scoping, Literature Survey and State-of-Art Analysis, Methodology Design and Planning, Data Collection and Preprocessing, Initial Prototype Development |
| MDE21E05 | Data Security and Privacy | PE - Program Elective III | 3 | Cryptography Principles, Privacy-Preserving Techniques, Data Protection Regulations (GDPR/DPDP), Data Anonymization and De-identification, Threat Models and Vulnerability Analysis |
| MDE21E06 | Explainable AI (XAI) | PE - Program Elective IV | 3 | Model Interpretability and Transparency, LIME and SHAP Techniques, Causal Inference in AI, Fairness and Bias in AI Systems, Ethical AI Principles and Governance |
| MOE21002 | Entrepreneurship Development | OE - Open Elective | 3 | Startup Ecosystem and Innovation, Business Model Canvas, Market Research and Analysis, Funding and Venture Capital, Legal Aspects of Entrepreneurship |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDE21PJ02 | Project Work - Phase II | Project | 12 | Advanced System Implementation, Comprehensive Testing and Evaluation, Results Analysis and Interpretation, Thesis Writing and Documentation, Project Presentation and Defense |
