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M-SC 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.Sc. Data Science program at SRM Institute of Science and Technology focuses on equipping students with a robust foundation in statistical analysis, machine learning, and big data technologies essential for the burgeoning Indian digital economy. The curriculum is designed to foster a deep understanding of data-driven decision-making, preparing graduates to tackle complex real-world problems. It emphasizes practical application, aligning with the industry''''s demand for skilled data professionals.
Who Should Apply?
This program is ideal for fresh graduates from B.Sc. (Maths, Stats, CS, IT, BCA, Physics, Electronics) or B.E./B.Tech backgrounds seeking a comprehensive entry into the data science domain. It also caters to working professionals aiming to upskill or transition into analytical roles, leveraging their existing domain knowledge with advanced data science techniques. Candidates with a strong quantitative aptitude and problem-solving mindset will thrive.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Analyst, and Big Data Engineer across various sectors in India, including IT, finance, healthcare, and e-commerce. Entry-level salaries typically range from INR 4-8 lakhs, growing significantly with experience to 15-30+ lakhs. The program prepares students for industry-recognized certifications and leadership roles in data-driven organizations.
Student Success Practices
Foundation Stage
Master Core Programming & Math Fundamentals- (Semester 1-2)
Dedicate significant time to mastering Python programming for data science, alongside a strong grasp of linear algebra, calculus, probability, and statistics. Solve daily coding challenges on platforms to build logical thinking and efficient code writing.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Khan Academy, NumPy, Pandas Documentation
Career Connection
A solid foundation in these areas is non-negotiable for all data science roles and is heavily tested in technical interviews.
Engage in Early Data Exploration & Visualization Projects- (Semester 1-2)
Start working on small, independent data analysis projects using publicly available datasets (e.g., Kaggle). Focus on cleaning data, performing exploratory data analysis, and creating compelling visualizations to tell a story.
Tools & Resources
Kaggle, UCI Machine Learning Repository, Matplotlib, Seaborn, Tableau Public
Career Connection
Develops practical skills in data handling and presentation, crucial for data analyst and junior data scientist roles.
Build a Strong Peer Learning Network- (Semester 1-2)
Form study groups with classmates to discuss complex concepts, review code, and collaborate on assignments. Actively participate in department workshops and seminars to expand your network and learn from peers and faculty.
Tools & Resources
University forums, WhatsApp groups, SRMIST departmental clubs
Career Connection
Fosters collaborative skills, enhances problem-solving through diverse perspectives, and creates a support system for academic and career growth.
Intermediate Stage
Specialize through Electives & Advanced Machine Learning- (Semester 3)
Carefully choose electives that align with your career interests (e.g., NLP, Computer Vision, Reinforcement Learning). Dive deeper into advanced machine learning algorithms and their practical applications, focusing on understanding the underlying mathematics and implementation.
Tools & Resources
TensorFlow, PyTorch, Keras, Official documentation, deeplearning.ai courses
Career Connection
Develops specialized skills highly sought after in advanced AI/ML roles and provides a competitive edge in niche areas.
Pursue Industry-Relevant Internships- (Semester 3)
Actively seek and complete internships during semester breaks or as part of the curriculum (Internship III is in Sem 3). Focus on gaining hands-on experience with real-world datasets and business problems, building a professional network.
Tools & Resources
SRMIST Placement Cell, LinkedIn, Internshala, Company career pages
Career Connection
Provides invaluable practical experience, strengthens your resume, and often leads to pre-placement offers.
Participate in Data Science Competitions- (Semester 3)
Engage in online data science competitions (e.g., Kaggle, Analytics Vidhya) to test your skills against others, learn new techniques, and build a portfolio of impactful projects. Focus on improving your ranking and learning from winning solutions.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub for sharing solutions
Career Connection
Showcases problem-solving abilities, provides measurable achievements for your resume, and demonstrates continuous learning to potential employers.
Advanced Stage
Execute a Comprehensive Capstone Project- (Semester 4)
Dedicate significant effort to your final year project (Project Phase I & II). Choose a complex, industry-relevant problem, define clear objectives, implement a robust solution, and meticulously document your work. Aim for a deployable prototype.
Tools & Resources
Latest ML/DL frameworks, Cloud platforms (AWS, Azure, GCP), Project management tools
Career Connection
The capstone project is often the centerpiece of a data science portfolio, demonstrating your ability to lead and deliver a complete solution, crucial for job interviews.
Master Interview Skills & Portfolio Presentation- (Semester 4)
Practice technical interviews, focusing on data structures, algorithms, SQL, machine learning concepts, and case studies. Refine your resume, LinkedIn profile, and present your project portfolio effectively, highlighting your contributions and impact.
Tools & Resources
LeetCode, InterviewBit, Mock interviews with peers/mentors, LinkedIn
Career Connection
Essential for converting job opportunities. A well-presented portfolio and strong interview performance are critical for securing top placements.
Network with Industry Professionals- (Semester 4)
Attend industry meetups, conferences (both online and offline), and webinars. Connect with professionals on LinkedIn, seek mentorship, and stay updated on the latest trends and hiring demands in the data science field.
Tools & Resources
LinkedIn, Industry-specific communities, Tech event platforms
Career Connection
Opens doors to referrals, provides insights into career paths, and helps identify potential employers and opportunities beyond formal placements.
Program Structure and Curriculum
Eligibility:
- A pass in B.Sc. Degree (10+2+3 pattern) with Mathematics / Statistics / Computer Science / IT / Computer Applications / Data Science / Physics / Electronics / B.C.A. / B.E. / B.Tech. with minimum of 50% aggregate marks.
Duration: 2 years / 4 semesters
Credits: 86 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PDS21101 | Mathematical Foundation for Data Science | Core | 4 | Linear Algebra, Calculus, Probability Theory, Statistical Inference, Optimization Techniques |
| PDS21102 | Programming with Python for Data Science | Core | 4 | Python Fundamentals, Data Structures, Object-Oriented Programming, NumPy and Pandas, Data Manipulation |
| PDS21103 | Data Structures and Algorithms | Core | 3 | Arrays and Linked Lists, Trees and Graphs, Sorting Algorithms, Searching Algorithms, Algorithm Analysis |
| PDS21104 | Database Management Systems | Core | 3 | Relational Model, SQL Queries, Normalization, Transaction Management, NoSQL Databases Concepts |
| PDS21105 | Programming with Python for Data Science Lab | Lab | 2 | Python Programming Practice, Data Handling with Pandas, Numerical Operations with NumPy, Problem Solving, Debugging Techniques |
| PDS21106 | Data Structures and Algorithms Lab | Lab | 2 | Implementation of Data Structures, Algorithm Design, Performance Analysis, Hands-on Coding, Problem-Solving Scenarios |
| PDS21107 | Database Management Systems Lab | Lab | 2 | SQL Query Writing, Database Design, PL/SQL Programming, Data Manipulation, Database Administration |
| PDS21108 | Communication Skills | Soft Skills | 2 | Presentation Skills, Listening and Speaking, Professional Etiquette, Report Writing, Interpersonal Communication |
| PDS21109 | Internship - I | Project | 1 | Industry Exposure, Project Implementation, Report Submission, Problem Identification, Basic Research |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PDS21201 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation and Validation |
| PDS21202 | Big Data Technologies | Core | 4 | Hadoop Ecosystem, MapReduce, HDFS, Apache Spark, NoSQL Databases |
| PDS21203 | Data Visualization | Core | 3 | Principles of Data Visualization, Exploratory Data Analysis, Interactive Dashboards, Data Storytelling, Visualization Tools (Matplotlib, Seaborn) |
| PDS21204 | Data Warehousing and Data Mining | Core | 3 | Data Warehousing Concepts, OLAP, ETL Process, Data Mining Techniques, Clustering and Association Rules |
| PDS21205 | Machine Learning Lab | Lab | 2 | Implementing ML Algorithms, Model Training and Testing, Hyperparameter Tuning, Scikit-learn and TensorFlow Basics, Case Studies |
| PDS21206 | Big Data Technologies Lab | Lab | 2 | Hadoop Cluster Setup, MapReduce Programming, Spark Data Processing, Hive and Pig Scripting, Real-time Data Processing |
| PDS21207 | Data Visualization Lab | Lab | 2 | Creating Static Visualizations, Interactive Dashboards, Data Storytelling Tools, Advanced Plotting, Custom Visualizations |
| PDS21208 | Research Methodology | Core | 2 | Research Design, Data Collection Methods, Statistical Analysis, Hypothesis Testing, Scientific Report Writing |
| PDS21209 | Internship - II | Project | 2 | Advanced Project Implementation, Problem Solving, Industry Best Practices, Documentation, Presentation |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PDS21301 | Deep Learning | Core | 4 | Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), TensorFlow and Keras, Deep Learning Architectures |
| PDS21302 | Natural Language Processing | Core | 3 | Text Preprocessing, Word Embeddings (Word2Vec, GloVe), Text Classification, Sentiment Analysis, Sequence Models |
| PDS21303 | Ethics in AI and Data Science | Core | 2 | Data Privacy and Security, Algorithmic Bias and Fairness, Accountability and Transparency, AI Governance, Ethical Frameworks |
| PDS213EL- | Program Elective – I | Elective | 3 | Student Choice from Elective Pool, Specialized Topics, Advanced Concepts |
| PDS213EL- | Program Elective – II | Elective | 3 | Student Choice from Elective Pool, Specialized Topics, Advanced Concepts |
| PDS21304 | Deep Learning Lab | Lab | 2 | Implementing CNNs and RNNs, Image Recognition, Sequence Generation, Frameworks (TensorFlow, PyTorch), Model Optimization |
| PDS21305 | Natural Language Processing Lab | Lab | 2 | Text Preprocessing and Tokenization, NLTK and SpaCy, Sentiment Analysis Implementation, Building Chatbots, Text Summarization |
| PDS21306 | Internship - III | Project | 2 | Advanced Industry Project, Application Development, Complex Problem Solving, Technical Documentation, Project Presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PDS21EL- | Program Elective – III | Elective | 3 | Student Choice from Elective Pool, Specialized Topics, Advanced Concepts |
| PDS21EL- | Program Elective – IV | Elective | 3 | Student Choice from Elective Pool, Specialized Topics, Advanced Concepts |
| PDS21401 | Project Phase - I | Project | 4 | Problem Identification, Literature Survey, System Design, Feasibility Study, Prototype Development |
| PDS21402 | Project Phase - II | Project | 6 | System Implementation, Testing and Evaluation, Results Analysis, Final Report Writing, Project Defense |
Semester pool
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PDS21EL01 | Image Processing and Computer Vision | Elective | 3 | Image Filtering, Image Segmentation, Feature Extraction, Object Detection, Computer Vision Applications |
| PDS21EL02 | Cloud Computing | Elective | 3 | Cloud Service Models, Virtualization, Cloud Storage, AWS/Azure/GCP Services, Cloud Security |
| PDS21EL03 | Time Series Analysis | Elective | 3 | Time Series Models (ARIMA), Forecasting Techniques, Trend and Seasonality, Stationarity, Financial Time Series |
| PDS21EL04 | Reinforcement Learning | Elective | 3 | Markov Decision Processes, Q-Learning, Deep Q-Networks (DQNs), Policy Gradient Methods, Exploration vs. Exploitation |
| PDS21EL05 | Data Security and Privacy | Elective | 3 | Cryptography, Data Anonymization, Access Control, Data Protection Regulations (GDPR), Privacy-Preserving Techniques |
| PDS21EL06 | Edge Computing for Data Science | Elective | 3 | Edge Computing Architecture, IoT Devices, Distributed AI at the Edge, Latency Optimization, Resource Management |
| PDS21EL07 | Statistical Modeling | Elective | 3 | Linear Regression, ANOVA, Generalized Linear Models, Hypothesis Testing, Bayesian Statistics |
| PDS21EL08 | Financial Analytics | Elective | 3 | Financial Data Analysis, Market Prediction, Risk Management, Algorithmic Trading, Investment Strategies |
| PDS21EL09 | Health Care Analytics | Elective | 3 | Healthcare Data Management, Predictive Healthcare, Electronic Health Records (EHR) Analysis, Disease Prediction, Clinical Decision Support |
| PDS21EL10 | Business Intelligence and Analytics | Elective | 3 | Business Intelligence Concepts, Data Warehousing for BI, Dashboarding and Reporting, Performance Metrics, BI Tools (e.g., PowerBI, Tableau) |
