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B-TECH in Computer Science And Engineering Data Science at Vellore Institute of Technology
Vellore, Tamil Nadu
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About the Specialization
What is Computer Science and Engineering (Data Science) at Vellore Institute of Technology Vellore?
This Computer Science and Engineering (Data Science) program at Vellore Institute of Technology focuses on equipping students with advanced analytical and computational skills crucial for the rapidly evolving data-driven Indian industry. It integrates core CSE principles with specialized knowledge in data analytics, machine learning, and big data technologies, preparing graduates for cutting-edge roles in various sectors. The curriculum emphasizes practical application and problem-solving, aligning with the industry''''s demand for skilled data professionals.
Who Should Apply?
This program is ideal for fresh graduates from science or engineering backgrounds with strong analytical aptitude, seeking entry into data science, machine learning, or analytics roles. It also caters to working professionals aiming to upskill and transition into data-centric careers, and individuals aspiring to contribute to data-intensive research and development within Indian tech companies and startups. Prerequisites typically include a solid foundation in mathematics and basic programming.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Data Scientist, Machine Learning Engineer, Business Intelligence Analyst, Big Data Engineer, and AI/ML Consultant. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning INR 15-30+ LPA. Growth trajectories are steep, moving towards lead data scientist or architect roles in Indian companies and MNCs. The program also prepares students for advanced studies and global professional certifications in data science.
Student Success Practices
Foundation Stage
Master Programming Fundamentals and Logic- (Semester 1-2)
Dedicate significant time to mastering programming languages like Python and C, along with core data structures and algorithms. Participate in coding contests and solve problems on platforms like HackerRank and LeetCode to build strong problem-solving logic essential for data science. Focus on building clean, efficient, and well-documented code from the start.
Tools & Resources
Python (Anaconda Distribution), C Programming Language, HackerRank, LeetCode, GeeksforGeeks for DSA
Career Connection
A strong foundation in programming and algorithms is paramount for any data science role, directly impacting your ability to implement models, optimize code, and clear technical interview rounds for top companies like TCS, Infosys, and startups.
Build a Solid Mathematical and Statistical Base- (Semester 1-3)
Focus intensely on Calculus, Linear Algebra, Probability, and Statistics. These are the bedrock of machine learning and data science. Utilize online courses from platforms like NPTEL or Coursera to supplement classroom learning. Practice problem-solving rigorously to ensure deep conceptual understanding, which will aid in understanding complex algorithms later.
Tools & Resources
NPTEL courses on Mathematics, Khan Academy, MIT OpenCourseware, Coursera (Probability & Statistics for Data Science)
Career Connection
Understanding the mathematical underpinnings of algorithms helps in selecting appropriate models, interpreting results, and troubleshooting, giving you an edge in advanced data science roles and research opportunities.
Engage in Early Peer Learning and Project Exploration- (Semester 1-2)
Form study groups with peers to discuss complex topics and clarify doubts. Start working on small, self-initiated projects, even simple data analysis tasks using public datasets (e.g., Kaggle). This hands-on experience, coupled with peer feedback, fosters collaborative skills and reinforces theoretical knowledge.
Tools & Resources
GitHub for collaboration, Kaggle for datasets, Google Colab, Discord/WhatsApp for study groups
Career Connection
Early project exposure provides practical experience to showcase in internships and placements, while peer learning enhances communication and teamwork skills, vital for working in data science teams.
Intermediate Stage
Deep Dive into Core Data Science Tools & Libraries- (Semester 3-5)
Beyond theoretical knowledge, gain practical mastery of essential data science libraries in Python such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and TensorFlow/PyTorch. Work through tutorials, implement algorithms from scratch, and apply them to real-world datasets. Participate in hackathons focused on data challenges.
Tools & Resources
Anaconda Python Distribution, Jupyter Notebooks, Google Colab, Kaggle Competitions, GitHub
Career Connection
Proficiency in these tools is a non-negotiable requirement for data scientist and machine learning engineer roles, enabling you to build, train, and deploy models efficiently in an industry setting.
Seek Internships and Industry Exposure- (Semester 4-6)
Actively apply for internships (summer/winter) in startups, mid-sized companies, or MNCs (e.g., in Bengaluru, Hyderabad, Chennai). Focus on roles like Data Analyst, ML Intern, or Business Intelligence Intern. Even short-term projects or virtual internships provide invaluable industry context, networking opportunities, and practical problem-solving experience.
Tools & Resources
LinkedIn, Internshala, Naukri.com, College Placement Cell
Career Connection
Internships are critical for bridging the academic-industry gap, often leading to pre-placement offers (PPOs) and providing substantial experience to bolster your resume for final placements.
Build a Robust Project Portfolio- (Semester 3-6)
Develop 3-5 significant data science projects that showcase diverse skills (e.g., predictive modeling, NLP, computer vision, big data). Document your projects thoroughly on GitHub, explaining your methodology, code, and results. Present these projects at college tech fests or online platforms to gather feedback and improve.
Tools & Resources
GitHub Pages, Medium for technical blogs, Streamlit/Gradio for web apps
Career Connection
A strong project portfolio is your most powerful asset in interviews, demonstrating practical application of knowledge and problem-solving abilities to recruiters at companies like Deloitte, Accenture, and product-based firms.
Advanced Stage
Specialize and Prepare for Advanced Roles- (Semester 6-8)
Identify a specific area within Data Science (e.g., Deep Learning, MLOps, Data Engineering, Reinforcement Learning) and take advanced electives. Pursue certifications (e.g., AWS Certified Machine Learning, Google Cloud Professional Data Engineer) and contribute to open-source projects. This specialization makes you a highly sought-after candidate for niche roles.
Tools & Resources
Coursera Specializations, edX MicroMasters, AWS/GCP/Azure ML Certifications, Open-source communities
Career Connection
Specialization allows you to target specific, high-paying roles in leading tech companies and equips you with the in-depth expertise required for complex industry challenges.
Excel in Capstone Project and Research- (Semester 7-8)
Treat your final year project as a flagship endeavor. Aim for an innovative solution to a real-world problem, potentially collaborating with industry mentors or faculty research groups. Consider publishing your work in national conferences or journals if it''''s research-oriented, demonstrating strong academic and practical rigor.
Tools & Resources
Research papers (arXiv, IEEE Xplore), Plagiarism checker tools, LaTeX for documentation
Career Connection
An outstanding capstone project can differentiate you significantly, serving as a powerful conversation starter in interviews and potentially attracting attention from research-focused organizations or startups.
Master Interview Skills and Networking- (Semester 6-8)
Actively participate in mock interview sessions, focusing on technical questions (DSA, ML concepts, SQL, case studies) and behavioral aspects. Network with alumni, industry professionals, and recruiters through LinkedIn, industry events, and college alumni meets. Understand current industry trends and company expectations.
Tools & Resources
Mock interview platforms, Glassdoor for company reviews, LinkedIn for networking, VIT''''s Career Development Centre
Career Connection
Effective interview skills and a strong professional network are crucial for securing desired placements. Networking often opens doors to unadvertised opportunities and provides valuable career guidance.
Program Structure and Curriculum
Eligibility:
- 10+2 with a minimum aggregate of 60% (50% for SC/ST/North-Eastern states) in Physics, Chemistry, Mathematics (PCM) or Physics, Chemistry, Biology (PCB) and a valid score in VITEEE entrance examination.
Duration: 8 semesters / 4 years
Credits: 162 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSE1001 | Problem Solving and Programming | Core | 4 | Programming Paradigms, Data Types and Operators, Control Flow, Functions and Recursion, Pointers and Arrays, File Handling |
| MAT1011 | Calculus for Engineers | Core | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Sequences and Series, Vector Calculus |
| PHY1701 | Engineering Physics | Core | 4 | Quantum Mechanics, Material Science, Optics and Lasers, Semiconductor Physics, Nanomaterials |
| ENG1001 | English for Engineers | Core | 2 | Technical Communication, Reading Comprehension, Writing Skills, Presentation Skills, Group Discussions |
| BME1001 | Biology for Engineers | Core | 2 | Cell Biology, Genetics, Biomolecules, Human Physiology, Ecology |
| FCS1001 | Foreign Language (French) | Elective | 2 | Basic Greetings, Grammar Fundamentals, Conversational French, Cultural Aspects, Reading Simple Texts |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSE2001 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms, Hashing Techniques |
| MAT2001 | Linear Algebra and Differential Equations | Core | 4 | Matrices and Determinants, Vector Spaces, Eigenvalues and Eigenvectors, First-Order ODEs, Higher-Order ODEs, Laplace Transforms |
| CHY1701 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion, Water Technology, Polymer Chemistry, Analytical Techniques |
| EEE1001 | Basic Electrical and Electronics Engineering | Core | 4 | DC Circuits, AC Circuits, Diodes and Transistors, Operational Amplifiers, Digital Logic Gates |
| CSE1002 | Programming in Python | Core | 4 | Python Basics, Data Structures in Python, Functions and Modules, Object-Oriented Programming, File I/O and Exception Handling |
| HUM1021 | Ethics and Values | Core | 2 | Ethical Theories, Professional Ethics, Moral Dilemmas, Social Responsibility, Values in Society |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSE3001 | Database Management Systems | Core | 4 | Database Architecture, ER Modeling, Relational Algebra, SQL Querying, Normalization, Transaction Management |
| CSE3002 | Operating Systems | Core | 4 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, Deadlocks |
| CSE3003 | Computer Networks | Core | 4 | OSI and TCP/IP Models, Physical Layer, Data Link Layer, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols |
| MAT3004 | Probability and Statistics for Data Science | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Descriptive Statistics, Inferential Statistics, Hypothesis Testing |
| CSE3005 | Discrete Mathematics | Core | 4 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Combinatorics, Recurrence Relations |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSE4001 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, NP-Completeness |
| CSE4002 | Machine Learning Fundamentals | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation, Feature Engineering |
| CSE4003 | Software Engineering | Core | 4 | Software Development Life Cycle, Requirements Engineering, Software Design, Testing Strategies, Project Management, Agile Methodologies |
| CSE4004 | Computer Architecture and Organization | Core | 4 | Processor Design, Memory Hierarchy, Input/Output Organization, Pipelining, Instruction Set Architectures, Multiprocessors |
| CSE4005 | Object-Oriented Programming | Core | 4 | Classes and Objects, Inheritance, Polymorphism, Abstraction, Encapsulation, Exception Handling |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSE5001 | Big Data Technologies | Core | 4 | Hadoop Ecosystem, MapReduce, HDFS, Spark, NoSQL Databases, Data Stream Processing |
| CSE5002 | Data Mining and Warehousing | Core | 4 | Data Preprocessing, Association Rule Mining, Classification and Prediction, Clustering Techniques, Data Warehouse Design, OLAP |
| CSE5003 | Artificial Intelligence | Core | 4 | Problem Solving Agents, Search Algorithms, Knowledge Representation, Logical Reasoning, Planning, Uncertainty Management |
| CSE5004 | Web Technology | Core | 4 | HTML, CSS, JavaScript, Client-Server Architecture, Web Frameworks, API Development, Database Connectivity, Security in Web |
| CSEEL1 | Professional Elective I | Elective | 3 | Topics based on chosen elective like IoT, Cloud, Cybersecurity etc. |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSE6001 | Deep Learning | Core | 4 | Neural Network Architectures, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks, Generative Models, Deep Learning Frameworks |
| CSE6002 | Natural Language Processing | Core | 4 | Text Preprocessing, Language Models, Syntactic Analysis, Semantic Analysis, Machine Translation, Text Classification |
| CSE6003 | Data Visualization Techniques | Core | 4 | Principles of Data Visualization, Visual Encoding, Interactive Visualizations, Dashboards, Tools like Tableau, Power BI, Storytelling with Data |
| CSEPR1 | Project Work - Phase I | Project | 6 | Problem Identification, Literature Survey, Requirement Analysis, Design and Planning, Initial Implementation |
| CSEEL2 | Professional Elective II | Elective | 3 | Topics based on chosen elective like Blockchain, Quantum Computing, Robotics etc. |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSE7001 | Time Series Analysis and Forecasting | Core | 4 | Time Series Components, ARIMA Models, Exponential Smoothing, Stationarity, Feature Engineering for Time Series, Forecasting Techniques |
| CSE7002 | Reinforcement Learning | Core | 4 | Markov Decision Processes, Value Iteration, Policy Iteration, Q-Learning, Deep Reinforcement Learning, Applications of RL |
| CSE7003 | Cloud Computing and Big Data Analytics | Core | 4 | Cloud Service Models, Cloud Deployment Models, Virtualization, Cloud Storage, Big Data Analytics on Cloud, AWS/Azure/GCP Services |
| CSEPR2 | Project Work - Phase II | Project | 8 | Advanced Implementation, Testing and Evaluation, Result Analysis, Report Writing, Demonstration and Presentation |
| CSEEL3 | Professional Elective III | Elective | 3 | Topics based on chosen elective like Robotics, DevOps, Cybersecurity, UI/UX etc. |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSE8001 | Internship / Capstone Project | Project | 12 | Real-world problem solving, Industry best practices, Project management, Collaboration, Professional documentation, Presentation |
| CSE8002 | Professional Ethics and Intellectual Property Rights | Core | 2 | Ethical Frameworks, Cyber Ethics, Privacy and Data Protection, Copyrights, Patents, Trademarks |
| CSEOEL1 | Open Elective I | Elective | 3 | Interdisciplinary topics from other engineering or humanities domains. |
