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INTEGRATED-M-SC-COMPUTATIONAL-STATISTICS-DATA-ANALYTICS in General at Vellore Institute of Technology
Vellore, Tamil Nadu
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About the Specialization
What is General at Vellore Institute of Technology Vellore?
This Integrated M.Sc. Computational Statistics & Data Analytics program at Vellore Institute of Technology focuses on equipping students with a robust foundation in statistical theory, computational methods, and advanced data analytics techniques. It addresses the burgeoning demand in the Indian market for skilled professionals who can derive actionable insights from complex datasets, integrating mathematical rigor with practical application.
Who Should Apply?
This program is ideal for ambitious 12th-grade graduates with a strong aptitude for mathematics or statistics, seeking a five-year direct entry into a data-centric career. It also attracts individuals passionate about quantitative analysis and computational problem-solving, aiming to become data scientists, statisticians, or business intelligence analysts in India''''s booming tech sector.
Why Choose This Course?
Graduates of this program can expect promising career paths in leading Indian and multinational companies as Data Scientists, Machine Learning Engineers, Statisticians, or Business Analysts, with entry-level salaries typically ranging from INR 6-10 LPA, growing significantly with experience. The comprehensive curriculum prepares students for industry certifications and leadership roles in data-driven decision making.
Student Success Practices
Foundation Stage
Build a Strong Mathematical and Programming Core- (Semester 1-2)
Dedicate significant effort to mastering foundational mathematics (Calculus, Linear Algebra, Statistics) and programming fundamentals (Python, Data Structures). Actively solve problems from textbooks and online platforms daily.
Tools & Resources
NPTEL courses for Maths, HackerRank, LeetCode, Khan Academy
Career Connection
A solid foundation is crucial for understanding advanced data science concepts and performing well in technical interviews for core data roles.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups with classmates to discuss challenging concepts, collaborate on assignments, and teach each other. Participate in internal coding competitions and hackathons to apply knowledge.
Tools & Resources
GitHub for collaborative coding, Discord/Slack for group discussions, VIT internal coding platforms
Career Connection
Develops teamwork, communication, and problem-solving skills vital for real-world projects in data science teams.
Explore Data Science Beyond the Curriculum- (Semester 1-2)
Begin exploring basic data analysis concepts and tools beyond classroom material. Follow data science blogs, online tutorials, and attempt simple projects using publicly available datasets.
Tools & Resources
Kaggle (for datasets), DataCamp (intro courses), Medium (data science blogs), YouTube tutorials
Career Connection
Fosters early interest, builds a portfolio of small projects, and demonstrates proactive learning to potential employers.
Intermediate Stage
Deep Dive into Machine Learning and Statistical Inference- (Semester 3-5)
Beyond textbook learning, implement machine learning algorithms from scratch using Python libraries (NumPy, Scikit-learn). Focus on understanding the statistical assumptions and limitations of models.
Tools & Resources
Coursera/edX specializations (Andrew Ng''''s ML course), Databricks Academy, Jupyter Notebooks
Career Connection
Essential for roles like Machine Learning Engineer or Data Scientist, demonstrating practical implementation and theoretical understanding.
Pursue Relevant Internships and Industry Projects- (Semester 4-5)
Actively seek out summer internships or part-time projects with startups or established companies focused on data analytics. Leverage VIT''''s career development cell and alumni network.
Tools & Resources
LinkedIn, Internshala, VIT''''s placement portal, Professional networking events
Career Connection
Gaining real-world experience, building industry contacts, and enhancing resume for better placements.
Develop Strong Data Visualization and Communication Skills- (Semester 3-5)
Practice presenting data insights clearly and effectively using visualization tools. Create compelling dashboards and learn to articulate complex technical findings to non-technical audiences.
Tools & Resources
Tableau Public, Power BI, Matplotlib, Seaborn, Storytelling with Data (book/resources)
Career Connection
Crucial for roles requiring stakeholder communication, such as Business Intelligence Analyst or Data Consultant, enhancing impact and visibility.
Advanced Stage
Specialize in Advanced Data Analytics Domains- (Semester 6-8)
Choose electives wisely to specialize in areas like Deep Learning, NLP, Big Data, or Cloud Computing. Undertake advanced projects demonstrating expertise in chosen domains.
Tools & Resources
Kaggle competitions (advanced), Specialized online courses, Industry-specific forums, Research papers
Career Connection
Differentiates candidates for specialized roles, showcasing deep knowledge and practical application in high-demand areas.
Prepare Rigorously for Placements and Higher Studies- (Semester 7-8)
Begin intensive preparation for campus placements, focusing on aptitude tests, technical interviews (data structures, algorithms, ML concepts), and HR rounds. Explore options for further studies if desired.
Tools & Resources
Placement training modules, Mock interviews, Company-specific preparation guides, GRE/GMAT/CAT resources
Career Connection
Securing desirable job offers or admission to prestigious graduate programs in India or abroad.
Initiate and Progress Towards a Capstone Project- (Semester 8)
Start identifying a research problem or an industry challenge for your final year project. Work with faculty mentors to define scope, gather resources, and begin preliminary work and literature review for Project Work I.
Tools & Resources
Research papers (IEEE, ACM), Academic databases, Faculty mentorship, Industry problem statements
Career Connection
A well-executed capstone project showcases problem-solving, independent research, and practical application skills, making a strong impression on recruiters.
Program Structure and Curriculum
Eligibility:
- Minimum of 60% marks in 10+2 / Intermediate or any other equivalent examination with Mathematics/Statistics/Business Maths/Accountancy as one of the subjects. Candidates who have appeared for the 12th Std. examination in the current year and expecting results are also eligible to apply.
Duration: 10 semesters / 5 years
Credits: 200 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VMC1001 | Calculus | Core | 4 | Functions of Several Variables, Partial Derivatives, Multiple Integrals, Vector Calculus, Differential Equations |
| VMC1002 | Linear Algebra and its Applications | Core | 4 | Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors, Inner Product Spaces, Applications of Linear Algebra |
| VMC1003 | Probability and Random Processes | Core | 4 | Probability Axioms, Random Variables, Probability Distributions, Stochastic Processes, Markov Chains |
| VMC1004 | Introduction to Programming | Core | 3 | Basic Programming Constructs, Data Types and Variables, Control Flow Statements, Functions and Modules, Object-Oriented Concepts |
| VMC1005 | Programming Lab | Lab | 2 | Hands-on Programming Exercises, Debugging Techniques, Basic Data Handling, Algorithmic Implementation, File Operations |
| VMA1001 | Soft Skills (Gaining Competency) | Core | 2 | Self-Introduction, Goal Setting, Time Management, SWOT Analysis, Presentation Skills, Email Etiquette |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VMC1006 | Discrete Mathematics | Core | 4 | Set Theory and Logic, Combinatorics, Graph Theory, Recurrence Relations, Boolean Algebra |
| VMC1007 | Statistical Methods | Core | 4 | Descriptive Statistics, Probability Distributions, Sampling Distributions, Hypothesis Testing, Correlation and Regression |
| VMC1008 | Data Structures and Algorithms | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Searching and Sorting Algorithms, Algorithm Analysis |
| VMC1009 | Data Structures and Algorithms Lab | Lab | 2 | Implementation of Data Structures, Algorithm Design and Analysis, Performance Measurement, Debugging and Testing, Problem-solving using DS and Algorithms |
| VMC1010 | Introduction to Data Science | Core | 3 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Introduction to Machine Learning, Ethical Considerations in Data Science |
| VMA1002 | Soft Skills (Enhancing Effectiveness) | Core | 2 | Group Discussion, Critical Thinking, Problem Solving, Team Work, Conflict Resolution, Interpersonal Skills |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VMC2001 | Advanced Calculus | Core | 4 | Sequences and Series, Power Series, Fourier Series, Laplace Transforms, Vector Spaces and Normed Spaces |
| VMC2002 | Database Management Systems | Core | 3 | Relational Model, SQL Queries, ER Diagrams, Normalization, Transaction Management |
| VMC2003 | Database Management Systems Lab | Lab | 2 | SQL Query Writing, Database Design and Implementation, Stored Procedures and Triggers, Data Manipulation Language, Database Administration Basics |
| VMC2004 | Introduction to Artificial Intelligence | Core | 3 | AI Agents and Environments, Search Algorithms, Knowledge Representation, Machine Learning Basics, Expert Systems |
| VMC2005 | Python for Data Science | Core | 3 | Python Programming Fundamentals, NumPy for Numerical Operations, Pandas for Data Manipulation, Matplotlib for Visualization, Data Wrangling and Cleaning |
| VMC2006 | Python for Data Science Lab | Lab | 2 | Implementing Python Libraries, Data Analysis with Pandas, Data Visualization with Matplotlib/Seaborn, Scripting for Data Tasks, API Integration for Data Sources |
| VMA2001 | Professional Communication | Core | 2 | Technical Writing, Business Communication, Presentation Skills, Report Writing, Negotiation Skills, Cross-Cultural Communication |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VMC2007 | Numerical Methods | Core | 4 | Solutions of Equations, Interpolation and Approximation, Numerical Differentiation and Integration, Numerical Solutions of Differential Equations, Matrix Methods |
| VMC2008 | Operating Systems | Core | 3 | OS Concepts, Process Management, Memory Management, File Systems, I/O Management |
| VMC2009 | Operating Systems Lab | Lab | 2 | Shell Scripting, Process Creation and Management, Inter-Process Communication, Thread Synchronization, System Calls and APIs |
| VMC2010 | Statistical Inference | Core | 4 | Estimation Theory, Point Estimation, Interval Estimation, Hypothesis Testing, Parametric and Non-Parametric Tests |
| VMC2011 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Clustering Techniques, Model Evaluation and Validation |
| VMC2012 | Machine Learning Lab | Lab | 2 | Implementing ML Algorithms, Scikit-learn Library, Model Training and Testing, Hyperparameter Tuning, Visualization of ML Results |
| VMA2002 | Quantitative Aptitude and Reasoning | Core | 2 | Number Systems, Percentages and Ratios, Data Interpretation, Logical Reasoning, Analytical Puzzles, Time and Work |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VMC3001 | Stochastic Processes | Core | 4 | Markov Chains, Poisson Processes, Renewal Theory, Queueing Theory, Brownian Motion |
| VMC3002 | Big Data Analytics | Core | 3 | Big Data Ecosystem, Hadoop Distributed File System, MapReduce Framework, Spark for Big Data, NoSQL Databases |
| VMC3003 | Big Data Analytics Lab | Lab | 2 | Hadoop and HDFS Commands, MapReduce Programming, Spark RDDs and DataFrames, Hive and Pig, Data Processing on Distributed Systems |
| VMC3004 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Deep Learning Frameworks (TensorFlow/PyTorch) |
| VMC3005 | Deep Learning Lab | Lab | 2 | Implementing CNNs for Image Recognition, RNNs for Sequence Data, Generative Adversarial Networks, Model Training and Deployment, Transfer Learning |
| VME | Elective 1 | Elective | 3 | Topics vary based on chosen elective, Examples include Time Series Analysis, Categorical Data Analysis, Bayesian Statistics, Data Visualization Techniques, Survival Analysis |
| VMA3001 | Soft Skills (Building Teamwork and Leadership) | Core | 2 | Leadership Styles, Team Building, Motivation Techniques, Conflict Management, Change Management, Professional Ethics |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VMC3006 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Q-learning, Policy Gradient Methods |
| VMC3007 | Reinforcement Learning Lab | Lab | 2 | Implementation of RL Algorithms, OpenAI Gym Environments, Deep Q-Networks, Policy Optimization, Agent Training and Evaluation |
| VMC3008 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| VMC3009 | Compiler Design Lab | Lab | 2 | Lexical Analyzer Implementation, Parser Implementation, Intermediate Code Generation Tools, Symbol Table Management, Error Handling in Compilers |
| VMC3010 | Computer Networks | Core | 3 | OSI and TCP/IP Models, Network Topologies, Routing Protocols, Transport Layer Protocols, Network Security Basics |
| VME | Elective 2 | Elective | 3 | Topics vary based on chosen elective, Examples include Stochastic Calculus, Financial Data Analytics, Operations Research, Game Theory, Quality Control and Reliability |
| VMA3002 | Soft Skills (Career Competence) | Core | 2 | Resume Writing, Interview Skills, Group Discussion Strategies, Corporate Etiquette, Career Planning, Professional Networking |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VMC4001 | Research Methodology | Core | 3 | Research Design, Data Collection Methods, Statistical Analysis for Research, Thesis Writing, Ethical Considerations in Research |
| VMC4002 | Optimization Techniques | Core | 4 | Linear Programming, Non-Linear Programming, Integer Programming, Dynamic Programming, Metaheuristics and Heuristics |
| VMC4003 | Cloud Computing | Core | 3 | Cloud Service Models, Cloud Deployment Models, Virtualization Technologies, Cloud Security, AWS, Azure, Google Cloud Platforms |
| VMC4004 | Cloud Computing Lab | Lab | 2 | Cloud Resource Provisioning, Virtual Machine Management, Containerization (Docker, Kubernetes), Serverless Computing, Cloud Storage Solutions |
| VMC4005 | Time Series and Forecasting | Core | 3 | Time Series Components, ARIMA Models, Exponential Smoothing, ARCH/GARCH Models, Forecasting Techniques |
| VME | Elective 3 | Elective | 3 | Topics vary based on chosen elective, Examples include High Performance Computing, Distributed Systems, Blockchain Technology, Internet of Things, Ethical Hacking and Cyber Security |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VMC4006 | Natural Language Processing | Core | 3 | Text Preprocessing, Word Embeddings, Sequence Models (RNNs, LSTMs), Transformers, Sentiment Analysis |
| VMC4007 | Natural Language Processing Lab | Lab | 2 | NLTK and spaCy usage, Text Classification, Named Entity Recognition, Chatbot Development, Hugging Face Transformers |
| VMC4008 | Data Visualisation | Core | 3 | Principles of Data Visualization, Matplotlib and Seaborn, Interactive Dashboards (Tableau/Power BI), Storytelling with Data, Geospatial Visualization |
| VMC4009 | Data Visualisation Lab | Lab | 2 | Creating various chart types, Designing Infographics, Developing Interactive Dashboards, Visualizing Geospatial Data, Customizing Visualizations |
| VMC4010 | Computer Vision | Core | 3 | Image Processing Fundamentals, Feature Detection and Matching, Object Recognition, Image Segmentation, Deep Learning for Vision |
| VME | Elective 4 | Elective | 3 | Topics vary based on chosen elective, Examples include Computer Graphics, Augmented Reality and Virtual Reality, Image Processing, Medical Imaging, Human Computer Interaction |
Semester 9
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VMC5001 | Project Work I | Project | 12 | Problem Definition and Scoping, Literature Review, Methodology Design, Data Collection and Preparation, Preliminary Analysis and Prototyping |
| VME | Elective 5 | Elective | 3 | Topics vary based on chosen elective, Examples include Advanced Machine Learning, Explainable AI, Generative AI, Quantum Computing, Robotics |
| VME | Elective 6 | Elective | 3 | Topics vary based on chosen elective, Examples include Bio-informatics, Environmental Data Analytics, Social Media Analytics, Geospatial Data Analytics, Text Mining |
Semester 10
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| VMC5002 | Project Work II | Project | 12 | System Implementation and Development, Experimentation and Testing, Results Analysis and Interpretation, Thesis Writing and Documentation, Project Presentation and Defense |
| VME | Elective 7 | Elective | 3 | Topics vary based on chosen elective, Examples include Advanced Machine Learning, Explainable AI, Generative AI, Quantum Computing, Robotics |
| VME | Elective 8 | Elective | 3 | Topics vary based on chosen elective, Examples include Bio-informatics, Environmental Data Analytics, Social Media Analytics, Geospatial Data Analytics, Text Mining |
