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B-SC-COMPUTER-SCIENCE in Data Analytics at Vellore Institute of Technology
Vellore, Tamil Nadu
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About the Specialization
What is Data Analytics at Vellore Institute of Technology Vellore?
This Data Analytics specialization program at Vellore Institute of Technology focuses on equipping students with a robust foundation in data science, statistical modeling, and machine learning techniques. Catering to the burgeoning demand in the Indian market, the program emphasizes practical application and problem-solving, preparing graduates to extract actionable insights from complex datasets. It integrates core computer science principles with advanced analytical methodologies, making it distinctively relevant for today''''s data-driven industries.
Who Should Apply?
This program is ideal for fresh graduates from science or engineering backgrounds with a keen interest in mathematics, statistics, and programming, seeking entry into high-growth data roles. It also suits working professionals aiming to upskill in analytics for career advancement, or career changers transitioning into data science from related quantitative fields. A strong aptitude for logical reasoning and computational thinking is beneficial for prospective students.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Data Analyst, Business Intelligence Developer, Machine Learning Engineer, and AI Specialist in IT services, e-commerce, and finance sectors. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The program aligns with industry-recognized certifications in Python, R, and cloud platforms, fostering rapid growth trajectories within top Indian and multinational companies operating in India.
Student Success Practices
Foundation Stage
Master Programming Fundamentals with Practical Projects- (Semester 1-2)
Actively engage with labs and assignments for C and Java, focusing on data structures. Apply concepts by building small projects, such as a simple library management system or calculator, from scratch to solidify understanding.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, VIT''''s programming labs, GitHub for version control
Career Connection
Strong foundational programming is essential for all data roles, enabling efficient data manipulation, algorithm implementation, and robust solution development.
Build a Strong Statistical & Mathematical Base- (Semester 1-2)
Pay extra attention to courses like Computational Statistics and Discrete Mathematics. Supplement classroom learning with online courses or books on probability, linear algebra, and calculus for data science applications.
Tools & Resources
Khan Academy, NPTEL courses, The Elements of Statistical Learning, Python''''s NumPy/SciPy libraries
Career Connection
A solid grasp of statistics and mathematics is critical for understanding data models, interpreting analytical results, and building robust predictive solutions.
Engage in Peer Learning and Coding Communities- (Semester 1-2)
Form study groups to discuss complex topics, solve problems collaboratively, and explain concepts to peers. Participate in VIT''''s coding clubs or local hackathons to build team collaboration skills and expand your network.
Tools & Resources
Discord study channels, Internal university forums, Local developer meetups, CodeChef/HackerEarth contests
Career Connection
Teamwork, effective communication, and problem-solving are highly valued in data science teams. Early networking can open doors to valuable career opportunities.
Intermediate Stage
Develop Database and Data Handling Expertise- (Semester 3-4)
Excel in Database Management Systems and practice SQL extensively. Explore different types of databases like SQL and NoSQL, and participate in data cleaning and manipulation challenges using Python or R.
Tools & Resources
MySQL, PostgreSQL, MongoDB, Kaggle datasets, Python''''s Pandas library, R''''s Tidyverse
Career Connection
Data analysts and scientists spend a significant portion of their time on data acquisition, cleaning, and preparation, making strong database skills indispensable.
Undertake Data Analytics Focused Mini-Projects- (Semester 3-4)
Apply concepts from Data Mining, Machine Learning, and Big Data Analytics to small, real-world datasets. Document and showcase these projects on platforms like GitHub to build a compelling professional portfolio.
Tools & Resources
Python (scikit-learn, matplotlib, seaborn), R (ggplot2, caret), Tableau/PowerBI (for visualization), Kaggle competitions
Career Connection
A strong portfolio of projects demonstrates practical skills and problem-solving abilities to potential employers, significantly boosting placement prospects in Indian companies.
Seek Early Internships and Industry Exposure- (Semester 3-4)
Actively look for summer internships or part-time roles in analytics, even if unpaid initially, to gain practical experience. Network with professionals through university career fairs and LinkedIn to explore opportunities.
Tools & Resources
VIT''''s Career Development Centre (CDC), LinkedIn, Internshala, Industry events
Career Connection
Internships provide invaluable real-world experience, help refine career goals, and often lead to pre-placement offers (PPOs) in top Indian companies, accelerating career launch.
Advanced Stage
Specialize with Advanced Analytics and AI/ML Techniques- (Semester 5-6)
Deep dive into Deep Learning, Cloud Computing, and advanced AI applications. Work on complex capstone projects involving large datasets and sophisticated models, demonstrating mastery of the specialization.
Tools & Resources
TensorFlow, PyTorch, AWS/Azure/GCP platforms, Custom datasets, GPU resources
Career Connection
Expertise in advanced AI/ML and cloud platforms is highly sought after for roles like Machine Learning Engineer, AI Scientist, and Data Architect in leading Indian tech companies.
Focus on Placement Preparation and Interview Skills- (Semester 5-6)
Practice coding challenges, data structure, algorithm questions, and case studies relevant to data analytics roles. Prepare a polished resume and portfolio, and rehearse mock interviews, including both behavioral and technical rounds.
Tools & Resources
LeetCode, InterviewBit, Glassdoor, Professional resume builders, VIT''''s placement cell guidance
Career Connection
Thorough preparation directly impacts success in campus placements, enabling students to secure desirable roles with competitive salaries from India''''s top recruiters.
Network and Build a Professional Presence- (Semester 5-6)
Attend industry conferences, webinars, and workshops related to data science and AI. Actively engage on LinkedIn, connecting with alumni and industry leaders, and sharing your project work and insights to build a strong professional identity.
Tools & Resources
LinkedIn, Industry associations (e.g., Analytics India Magazine events), Professional meetups
Career Connection
A strong professional network can lead to mentorship opportunities, job referrals, and staying abreast of industry trends, crucial for long-term career growth in India''''s dynamic tech sector.
Program Structure and Curriculum
Eligibility:
- Pass in H.Sc / +2 / Equivalent with minimum 60% aggregate of marks (50% for SC/ST/North Eastern states and Jammu & Kashmir / Ladakh). Candidates from NIOS are also eligible.
Duration: 3 years (6 semesters)
Credits: 113 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS1001 | English for Engineers | University Core | 2 | Reading Comprehension, Writing Skills, Grammar and Vocabulary, Oral Communication, Technical Report Writing |
| BCS1002 | Professional Ethics | University Core | 2 | Ethics and Values, Moral Development Theories, Engineering Ethics, Professional Responsibility, Global Issues in Ethics |
| BCS1003 | Introduction to Problem Solving and Programming | Programme Core | 3 | Problem Solving Techniques, Programming Fundamentals (C), Control Structures, Functions and Arrays, Pointers and Structures |
| BCS1004 | Computational Statistics and Probability | Programme Core | 3 | Probability Theory, Random Variables, Statistical Inference, Hypothesis Testing, Regression and Correlation |
| BCS1005 | Digital Logic Design | Programme Core | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Memory and Programmable Logic |
| BCS1006 | Lab for Introduction to Problem Solving and Programming | Programme Core | 2 | C Programming Exercises, Conditional Statements and Loops, Functions and Arrays Implementation, Pointers and Strings, File Handling in C |
| BCS1007 | Lab for Digital Logic Design | Programme Core | 2 | Logic Gates Implementation, Boolean Function Realization, Combinational Circuit Design, Sequential Circuit Design, Hardware Description Language (HDL) Basics |
| SSC1001 | Soft Skills I | University Core | 1 | Verbal Communication, Non-verbal Communication, Listening Skills, Self-Introduction, Group Discussions |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS2001 | Environmental Sciences | University Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Climate Change, Natural Resources Management, Sustainable Development |
| BCS2002 | Data Structures and Algorithms | Programme Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms, Hashing Techniques |
| BCS2003 | Object Oriented Programming using Java | Programme Core | 3 | Classes and Objects, Inheritance and Polymorphism, Interfaces and Packages, Exception Handling, Multithreading and I/O Streams |
| BCS2004 | Discrete Mathematics | Programme Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics, Algebraic Structures |
| BCS2005 | Computer Organization and Architecture | Programme Core | 3 | Basic Computer Functions, CPU Structure and Function, Memory Hierarchy, Input/Output Organization, Instruction Sets |
| BCS2006 | Lab for Data Structures and Algorithms | Programme Core | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practical |
| BCS2007 | Lab for Object Oriented Programming using Java | Programme Core | 2 | Object-Oriented Concepts Implementation, Java Class and Object Programs, Exception Handling Exercises, Multithreading Applications, GUI Programming Basics |
| SSC1002 | Soft Skills II | University Core | 1 | Self-Awareness, Goal Setting, Time Management, Conflict Resolution, Decision Making |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS3001 | Indian Constitution and Traditional Knowledge | University Core | 2 | Indian Constitution Structure, Fundamental Rights and Duties, Parliamentary System, Traditional Indian Sciences, Indian Art and Culture |
| BCS3002 | Database Management Systems | Programme Core | 3 | Database System Concepts, ER Model and Relational Model, SQL Queries and Constraints, Normalization, Transaction Management |
| BCS3003 | Operating Systems | Programme Core | 3 | Operating System Services, Process Management, CPU Scheduling, Memory Management, File Systems |
| BCS3004 | Theory of Computation | Programme Core | 3 | Finite Automata, Regular Expressions and Languages, Context-Free Grammars, Pushdown Automata, Turing Machines and Computability |
| BCS3005 | Lab for Database Management Systems | Programme Core | 2 | SQL DDL and DML Commands, Database Design and Normalization, Stored Procedures and Triggers, Query Optimization, Database Connectivity (JDBC/ODBC) |
| BCS3006 | Lab for Operating Systems | Programme Core | 2 | Linux Commands, Shell Scripting, Process Management in C/Java, CPU Scheduling Simulation, Memory Allocation Simulation |
| BCS4001 | Data Mining | Programme Elective | 3 | Data Preprocessing, Association Rule Mining, Classification Algorithms, Clustering Techniques, Data Mining Applications |
| SSC1003 | Soft Skills III | University Core | 1 | Presentation Skills, Report Writing, Email Etiquette, Interpersonal Skills, Negotiation Skills |
| UE1 | University Elective 1 | University Elective | 3 | As per student choice |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS4007 | Computer Networks | Programme Core | 3 | Network Topologies and Models, Physical and Data Link Layer, Network Layer and IP Addressing, Transport Layer Protocols, Application Layer Services |
| BCS4008 | Software Engineering | Programme Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing, Software Project Management |
| BCS4009 | Design and Analysis of Algorithms | Programme Core | 3 | Algorithm Complexity Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| BCS4010 | Lab for Computer Networks | Programme Core | 2 | Network Configuration, Socket Programming, Packet Tracing, Network Security Tools, Client-Server Applications |
| BCS4002 | Machine Learning | Programme Elective | 3 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Model Evaluation |
| BCS4003 | Big Data Analytics | Programme Elective | 3 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Programming, Spark Framework, NoSQL Databases |
| SSC1004 | Soft Skills IV | University Core | 1 | Leadership Skills, Teamwork, Emotional Intelligence, Problem Solving, Critical Thinking |
| UE2 | University Elective 2 | University Elective | 3 | As per student choice |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS5001 | Artificial Intelligence | Programme Core | 3 | Problem Solving using AI, Knowledge Representation, Search Algorithms, Expert Systems, Natural Language Processing Basics |
| BCS5002 | Web Technologies | Programme Core | 3 | HTML, CSS, JavaScript, Client-Side Scripting, Server-Side Scripting, Web Frameworks, Database Connectivity for Web |
| BCS5003 | Lab for Artificial Intelligence | Programme Core | 2 | Prolog/Python for AI, Search Algorithm Implementation, Constraint Satisfaction Problems, Logic Programming, Mini AI Project |
| BCS4004 | Introduction to R Programming | Programme Elective | 3 | R Data Structures, Data Import/Export in R, Data Manipulation with R, Statistical Graphics in R, Functions and Control Flow in R |
| BCS4005 | Business Intelligence | Programme Elective | 3 | BI Concepts and Architecture, Data Warehousing, ETL Processes, OLAP and Data Cubes, BI Reporting and Dashboards |
| SSC1005 | Soft Skills V | University Core | 1 | Career Planning, Resume Building, Interview Skills, Personal Branding, Professional Etiquette |
| BCS5998 | Project-I | Project | 2 | Problem Identification, Literature Review, System Design, Methodology Selection, Preliminary Implementation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS6001 | Generic Skills | University Core | 2 | Communication Skills, Teamwork and Collaboration, Leadership Skills, Problem Solving and Critical Thinking, Time Management |
| BCS4006 | Deep Learning | Programme Elective | 3 | Neural Network Fundamentals, Feedforward Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Learning Frameworks (TensorFlow/PyTorch) |
| BCS6002 | Cloud Computing | Programme Core | 3 | Cloud Computing Architecture, Virtualization, Cloud Services (IaaS, PaaS, SaaS), Cloud Security, Cloud Deployment Models |
| BCS6003 | Internet of Things | Programme Core | 3 | IoT Architecture, IoT Devices and Sensors, Communication Protocols, Data Analytics in IoT, IoT Applications |
| SSC1006 | Soft Skills VI | University Core | 1 | Entrepreneurial Thinking, Innovation Mindset, Global Awareness, Cross-cultural Communication, Sustainable Practices |
| BCS6999 | Project-II | Project | 4 | Advanced Implementation, Testing and Validation, Result Analysis, Project Report Writing, Presentation and Demonstration |
