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M-SC in Applied Data Science at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Applied 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 advanced knowledge and practical skills in the rapidly evolving field of data science. It addresses the growing demand for skilled professionals who can extract meaningful insights from vast datasets to drive informed decision-making in various Indian industries. The program distinguishes itself through its comprehensive curriculum covering theoretical foundations and hands-on application of cutting-edge technologies.
Who Should Apply?
This program is ideal for fresh graduates with a Bachelor’s degree in Computer Science, Mathematics, Statistics, or Engineering seeking to enter the high-growth data science sector. It also caters to working professionals aiming to upskill in analytics, machine learning, and big data technologies, as well as career changers from related quantitative fields looking to transition into data-driven roles across diverse Indian businesses.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Developer, and AI Specialist. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The strong curriculum helps align with industry-recognized certifications, fostering growth trajectories within Indian and multinational companies operating in the country.
Student Success Practices
Foundation Stage
Master Programming & Statistical Foundations- (Semester 1-2)
Dedicate significant time to thoroughly understand Python programming, data structures, algorithms, and core statistical concepts. Practice coding regularly using platforms like HackerRank or LeetCode. Form study groups to solve complex problems together, reinforcing theoretical knowledge with practical application.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, Khan Academy for statistics
Career Connection
Strong fundamentals in programming and statistics are non-negotiable for data science roles, forming the backbone for advanced machine learning and analytical tasks in Indian tech companies.
Build a Strong Portfolio of Mini-Projects- (Semester 1-2)
As you learn new concepts in Python, databases, and early machine learning, immediately apply them by building small-scale projects. These could be simple data cleaning scripts, exploratory data analysis on public datasets (Kaggle), or basic predictive models. Document your code and methodology on GitHub.
Tools & Resources
Kaggle, GitHub, Google Colab, Jupyter Notebooks
Career Connection
A well-curated GitHub portfolio demonstrates practical skills and initiative to Indian recruiters, significantly improving internship and entry-level job prospects.
Engage in Data Science Communities- (Semester 1-2)
Join online forums, LinkedIn groups, or local meetups (if available) for data science professionals in India. Actively participate in discussions, ask questions, and learn from experienced practitioners. This helps in understanding industry trends and challenges beyond the classroom.
Tools & Resources
LinkedIn, Reddit (r/datascienceindia), Local university/industry meetups
Career Connection
Networking opens doors to mentorship, collaborative projects, and early awareness of job openings within the Indian data science ecosystem.
Intermediate Stage
Specialize through Electives and Advanced Labs- (Semester 3)
Carefully choose electives that align with your career interests (e.g., NLP, Computer Vision, Reinforcement Learning). Dive deep into the chosen area through additional online courses and implement complex projects in the associated labs (Deep Learning, Data Visualization). Aim to build expertise in a niche.
Tools & Resources
Coursera, edX, Udemy courses on specific ML/DL topics, TensorFlow, PyTorch, Tableau, Power BI
Career Connection
Specialization makes you a more attractive candidate for specific roles in Indian companies that require expertise in areas like AI, computer vision, or natural language processing.
Undertake Industry-Relevant Internships- (Semester 3 breaks/post-Semester 3)
Actively seek out internships during or after your third semester. Prioritize internships that offer exposure to real-world data science challenges, allowing you to apply your machine learning and big data skills in a corporate setting. This practical experience is crucial for understanding Indian industry demands.
Tools & Resources
SRMIST''''s placement cell, Internshala, LinkedIn Jobs, Naukri.com
Career Connection
Internships provide invaluable practical experience, enhance your resume, and often lead to pre-placement offers from Indian companies.
Participate in Data Science Competitions- (Semester 3 onwards)
Engage in online data science competitions on platforms like Kaggle. This helps in sharpening problem-solving skills, learning from diverse approaches, and benchmarking your abilities against a global community. Aim for top rankings to boost your profile.
Tools & Resources
Kaggle, Analytics Vidhya, DrivenData
Career Connection
Success in competitions demonstrates advanced analytical skills and a competitive edge, highly valued by data science teams in India.
Advanced Stage
Excel in Your Master''''s Project- (Semester 4)
Treat your final project as a capstone opportunity to solve a significant real-world problem. Choose a challenging topic, conduct thorough research, implement a robust solution, and meticulously document your work. Present your findings professionally, practicing for viva voce.
Tools & Resources
Research papers (arXiv, Google Scholar), Domain-specific libraries, Version control (Git)
Career Connection
A high-quality, impactful project is a strong talking point in interviews, showcasing your ability to deliver end-to-end data science solutions to potential employers in India.
Refine Interview Skills and Build a Professional Brand- (Semester 4)
Practice technical interviews focusing on machine learning concepts, algorithms, SQL, and case studies. Work on communication skills to articulate your project experiences and problem-solving approaches clearly. Update your LinkedIn profile and resume, tailoring them for specific data science roles in India.
Tools & Resources
LeetCode for algorithm practice, SQLZoo for SQL, Glassdoor for interview experiences, LinkedIn
Career Connection
Polished interview skills and a strong professional brand are critical for securing top placements and navigating the competitive Indian job market effectively.
Explore Advanced Certifications and Continuing Education- (Post-Semester 4 / Ongoing)
Consider pursuing additional certifications in specialized areas (e.g., AWS Certified Machine Learning Specialty, Google Professional Data Engineer) or exploring research opportunities. Stay updated with the latest advancements in data science through continuous learning.
Tools & Resources
Official certification guides, Academic journals, Industry blogs (e.g., Towards Data Science)
Career Connection
These advanced credentials and a commitment to lifelong learning demonstrate initiative and deepen expertise, leading to faster career progression and leadership roles in Indian and global tech firms.
Program Structure and Curriculum
Eligibility:
- Bachelor’s Degree in Computer Science / Computer Applications / Information Technology / Mathematics / Statistics / Physics / Chemistry / Electronics/ Engineering or Technology with a minimum aggregate of 50% marks in the qualifying examination.
Duration: 2 years (4 semesters)
Credits: 72 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CDS101J | Mathematical and Statistical Foundations for Data Science | Core | 4 | Linear Algebra, Calculus and Optimization, Probability Theory, Statistical Inference, Hypothesis Testing |
| 22CDS102J | Data Structures and Algorithms | Core | 4 | Array and Linked Lists, Stack and Queue, Trees and Graphs, Sorting Algorithms, Searching Techniques |
| 22CDS103J | Principles of Data Science | Core | 4 | Data Science Life Cycle, Data Collection and Cleaning, Exploratory Data Analysis, Big Data Fundamentals, Ethics in Data Science |
| 22CDS104J | Python Programming for Data Science | Core | 4 | Python Fundamentals, NumPy for Numerical Computing, Pandas for Data Manipulation, Data Visualization with Matplotlib, File I/O and Exception Handling |
| 22CDS105L | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Linear Data Structures, Implementation of Non-Linear Data Structures, Sorting and Searching Algorithms, Graph Traversal Techniques, Problem Solving using Data Structures |
| 22CDS106L | Python Programming for Data Science Lab | Lab | 1 | Python Basics and Control Flow, NumPy Array Operations, Pandas Dataframe Manipulation, Data Visualization using Libraries, Building Simple Python Data Tools |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CDS201J | Database Management Systems | Core | 4 | Relational Database Model, SQL Queries and Joins, ER Modeling and Normalization, Transactions and Concurrency Control, Database Security and Backup |
| 22CDS202J | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression and Classification Models, Clustering Algorithms, Model Evaluation and Validation |
| 22CDS203J | Big Data Technologies | Core | 4 | Hadoop Ecosystem, HDFS and MapReduce, Apache Spark, Hive and Pig, NoSQL Databases |
| 22CDSE011J | Cloud Computing | Elective | 4 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization Technologies, Cloud Security and Privacy, AWS, Azure, Google Cloud Platforms, Cloud Migration Strategies |
| 22CDSE012J | Social Network Analysis | Elective | 4 | Network Properties and Metrics, Centrality Measures, Community Detection Algorithms, Link Prediction, Graph Data Structures |
| 22CDSE013J | Optimization Techniques | Elective | 4 | Linear Programming, Non-Linear Programming, Simplex Method, Evolutionary Algorithms, Heuristic Search |
| 22CDSE014J | Data Storage and Management | Elective | 4 | Storage Area Networks (SAN), Network Attached Storage (NAS), Data Warehousing Concepts, Data Lake Architectures, ETL Processes |
| 22CDSE015J | Natural Language Processing | Elective | 4 | Text Preprocessing, N-grams and Language Models, Part-of-Speech Tagging, Sentiment Analysis, Word Embeddings |
| 22CDS204L | Database Management Systems Lab | Lab | 1 | DDL and DML Commands, Advanced SQL Queries, PL/SQL Programming, Database Design and Implementation, Transaction Control |
| 22CDS205L | Machine Learning Lab | Lab | 1 | Implementing Regression Models, Implementing Classification Algorithms, Clustering Techniques, Feature Engineering, Model Hyperparameter Tuning |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CDS301J | Deep Learning | Core | 4 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers Architecture, Deep Learning Frameworks (TensorFlow, PyTorch) |
| 22CDS302J | Data Visualization Techniques | Core | 4 | Principles of Data Visualization, Visual Encoding, Interactive Dashboards (Tableau/Power BI), Geospatial Visualizations, Data Storytelling |
| 22CDSE021J | Data Mining | Elective | 4 | Association Rule Mining, Classification and Prediction, Clustering Analysis, Web Mining, Text Mining |
| 22CDSE022J | Time Series Analysis | Elective | 4 | Time Series Components, ARIMA and SARIMA Models, Forecasting Techniques, Spectral Analysis, State Space Models |
| 22CDSE023J | Internet of Things | Elective | 4 | IoT Architecture, Sensors and Actuators, IoT Protocols (MQTT, CoAP), Edge and Fog Computing, IoT Data Analytics |
| 22CDSE024J | Business Analytics | Elective | 4 | Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, Business Intelligence, Decision Support Systems |
| 22CDSE025J | Reinforcement Learning | Elective | 4 | Markov Decision Processes, Q-Learning and Sarsa, Deep Q-Networks (DQN), Policy Gradients, Exploration-Exploitation Dilemma |
| 22CDSE031J | Computer Vision | Elective | 4 | Image Processing Fundamentals, Feature Extraction, Object Detection, Image Segmentation, Facial Recognition Systems |
| 22CDSE032J | Edge Computing for Data Science | Elective | 4 | Edge Computing Architecture, Fog Computing, IoT-Edge Integration, Real-time Data Processing at Edge, Edge AI Applications |
| 22CDSE033J | Cognitive Science | Elective | 4 | Cognitive Architectures, Perception and Attention, Memory and Learning, Language and Communication, Problem Solving and Reasoning |
| 22CDSE034J | Speech Processing | Elective | 4 | Speech Production and Perception, Signal Processing for Speech, Feature Extraction (MFCC), Automatic Speech Recognition (ASR), Speaker Recognition |
| 22CDSE035J | Bio-Medical Data Analytics | Elective | 4 | Electronic Health Records (EHR) Data, Genomics and Proteomics Data, Medical Imaging Analysis, Predictive Models in Healthcare, Bio-statistics and Clinical Trials |
| 22CDS303L | Deep Learning Lab | Lab | 1 | Implementing Feedforward Networks, Building CNNs for Image Classification, RNNs for Sequence Data, Working with TensorFlow and PyTorch, Hyperparameter Tuning in Deep Models |
| 22CDS304L | Data Visualization Techniques Lab | Lab | 1 | Creating Static Visualizations, Developing Interactive Dashboards, Using Tableau/Power BI Tools, Customizing Visualizations with Python, Presenting Data Insights Visually |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CDS401P | Project Work | Project | 18 | Problem Definition and Literature Review, Methodology and Design, Implementation and Experimentation, Results Analysis and Discussion, Report Writing and Presentation |
