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B-TECH in Artificial Intelligence Data Science at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Thiruvallur, Tamil Nadu
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About the Specialization
What is Artificial Intelligence & Data Science at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvallur?
This Artificial Intelligence & Data Science program at Vel Tech focuses on equipping students with advanced skills in designing, developing, and deploying AI and data-driven solutions. The curriculum is tailored to meet the surging demand for skilled professionals in India''''s rapidly growing digital economy, emphasizing practical applications and interdisciplinary knowledge essential for innovation.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and programming, aspiring to build careers in cutting-edge technology fields. It also benefits working professionals seeking to upskill in AI/ML or data analytics, and career changers from related domains who wish to transition into the high-demand AI and Data Science industry in India.
Why Choose This Course?
Graduates of this program can expect diverse career paths, including Data Scientist, Machine Learning Engineer, AI Developer, Business Intelligence Analyst, and Research Scientist in India. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly higher. The program aligns with industry certifications, fostering continuous growth in top Indian and global tech companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals Early- (Semester 1-2)
Dedicate significant time to solidify programming basics in C/Python. Practice extensively on online coding platforms to build problem-solving logic and algorithmic thinking. Focus on data structures implementation.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, CodeChef
Career Connection
Strong fundamentals are the bedrock for cracking technical interviews and building efficient AI/DS applications later on.
Build a Strong Mathematical Foundation- (Semester 1-2)
Pay close attention to Calculus, Linear Algebra, Probability, and Statistics. These are crucial for understanding underlying AI/ML algorithms. Seek out additional resources and practice problems regularly.
Tools & Resources
Khan Academy, NPTEL courses, MIT OpenCourseWare (Mathematics)
Career Connection
Essential for deeper understanding of machine learning models, research roles, and advanced data analysis in the AI/DS domain.
Participate in Peer Learning Groups- (Semester 1-2)
Form study groups with peers to discuss concepts, solve problems collaboratively, and clarify doubts. Teach others to reinforce your own understanding. Attend department workshops and seminars.
Tools & Resources
College study rooms, Discord groups, Internal university forums
Career Connection
Enhances communication, teamwork, and problem-solving skills, which are highly valued in entry-level industry roles and collaborative projects.
Intermediate Stage
Engage in Mini-Projects and Kaggle Competitions- (Semester 3-5)
Apply learned concepts from Data Structures, DBMS, AI, ML, and Data Science to develop small projects. Participate in data science competitions on platforms like Kaggle to tackle real-world datasets and hone practical skills.
Tools & Resources
Kaggle, GitHub, Jupyter Notebooks, Google Colab
Career Connection
Builds a strong portfolio, demonstrates practical application of knowledge, and provides experience with industry-relevant tools and techniques for internships and job applications.
Seek Early Industry Exposure through Internships- (Semester 4-5 (Summer breaks))
Actively look for internships after the 2nd or 3rd year, even if unpaid or short-term. Focus on roles related to data analysis, AI research assistance, or software development with data components to gain hands-on experience.
Tools & Resources
LinkedIn, Internshala, College placement cell, Networking events
Career Connection
Provides invaluable hands-on experience, industry insights, and networking opportunities that are critical for securing full-time placements in AI/DS roles.
Specialize and Deepen Skill Sets- (Semester 5)
Identify areas within AI/DS that interest you most (e.g., NLP, Computer Vision, Big Data). Take relevant professional electives and pursue online certifications to deepen expertise in these niche areas.
Tools & Resources
Coursera, Udemy, NPTEL, edX, NVIDIA DLI courses
Career Connection
Differentiates your profile for specialized roles, showcasing expertise in high-demand domains for the Indian tech market and increasing employability.
Advanced Stage
Focus on Real-World Capstone Projects- (Semester 7-8)
Undertake significant capstone projects (Major Project and Internship projects) that solve real-world problems. Document them thoroughly, emphasizing the problem statement, methodology, results, and impact. Seek industry mentorship.
Tools & Resources
Research papers, Industry reports, Expert mentors, Advanced development frameworks
Career Connection
This becomes a key talking point in interviews, demonstrating problem-solving capabilities, project management, and domain expertise for final placements in top companies.
Master Interview and Communication Skills- (Semester 6-8)
Practice technical interview questions, especially in data structures, algorithms, AI/ML concepts, and SQL. Develop strong communication and presentation skills, crucial for explaining complex ideas in industry settings.
Tools & Resources
Mock interviews (peer/mentor), Pramp, Glassdoor, LinkedIn preparation tools
Career Connection
Directly impacts success in placement interviews, group discussions, and professional interactions, ensuring you can articulate your technical knowledge effectively.
Build a Professional Network and Personal Brand- (Semester 6-8)
Attend industry conferences, webinars, and workshops. Connect with alumni and professionals on LinkedIn. Maintain an active GitHub profile showcasing projects and contributions to build a strong personal brand.
Tools & Resources
LinkedIn, GitHub, Industry events, College alumni network
Career Connection
Opens doors to referrals, mentorship, and unadvertised job opportunities, vital for career growth and long-term success in the competitive Indian job market.
Program Structure and Curriculum
Eligibility:
- A pass in the 10+2 system of Examination with Physics and Mathematics as compulsory subjects along with one of the Chemistry / Biotechnology / Biology / Technical Vocational subject. Obtained at least 45% marks (40% in case of candidates belonging to reserved category) in the above subjects taken together.
Duration: 4 years / 8 semesters
Credits: 160 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS1001 | Professional English - I | Humanities & Social Sciences | 3 | Listening Comprehension, Speaking Skills, Reading Strategies, Writing Paragraphs, Vocabulary and Grammar |
| MA1003 | Calculus and Linear Algebra | Basic Science | 4 | Differential Calculus, Integral Calculus, Matrices and Determinants, Eigenvalues and Eigenvectors, Vector Spaces |
| PH1001 | Engineering Physics | Basic Science | 3 | Laser Technology, Fiber Optics, Quantum Physics, Crystal Physics, Magnetic Materials |
| CS1001 | Programming for Problem Solving | Engineering Science | 3 | C Programming Fundamentals, Operators and Expressions, Control Flow Statements, Functions and Pointers, Arrays and Strings |
| CS1002 | Programming for Problem Solving Lab | Engineering Science | 1.5 | C Program Execution, Conditional and Loop Structures, Function Implementation, Array and String Manipulation, Pointers and Structures |
| ES1001 | Engineering Graphics & Design | Engineering Science | 2 | Orthographic Projections, Isometric Views, Sectional Views, Development of Surfaces, Introduction to CAD |
| ES1002 | Engineering Practices Lab | Engineering Science | 1.5 | Carpentry and Fitting, Welding and Sheet Metal, Plumbing Practices, Basic Electrical Wiring, Electronics Soldering |
| PE1001 | Physical Education | Mandatory | 1 | Fitness and Wellness, Team Sports, Individual Sports, Yoga and Meditation, Health Awareness |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS1002 | Professional English - II | Humanities & Social Sciences | 3 | Advanced Reading Skills, Technical Report Writing, Presentation Skills, Group Discussion, Resume and Cover Letter Writing |
| MA1005 | Probability, Statistics and Queuing Theory | Basic Science | 4 | Probability Distributions, Random Variables, Statistical Inference, Regression Analysis, Queuing Models |
| CH1001 | Engineering Chemistry | Basic Science | 3 | Water Treatment, Corrosion and its Control, Electrochemistry, Fuels and Combustion, Polymer Chemistry |
| EE1001 | Basic Electrical and Electronics Engineering | Engineering Science | 3 | DC and AC Circuits, Semiconductor Diodes, Transistors and Amplifiers, Digital Logic Gates, Rectifiers and Power Supplies |
| CS1003 | Data Structures | Professional Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Trees, Graphs and Graph Traversal, Sorting and Searching Algorithms |
| CS1004 | Data Structures Lab | Professional Core | 1.5 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Programs |
| EE1002 | Basic Electrical and Electronics Engineering Lab | Engineering Science | 1.5 | Basic Electrical Measurements, Verification of Circuit Laws, Diode Characteristics, Transistor Amplifier Circuits, Logic Gate Experiments |
| NC1001 | Environmental Science | Mandatory | 1 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Climate Change, Waste Management |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA2001 | Discrete Mathematics and Graph Theory | Basic Science | 4 | Mathematical Logic, Set Theory and Relations, Functions and Combinatorics, Graph Theory Fundamentals, Trees and Network Flows |
| AI2001 | Object Oriented Programming and Design | Professional Core | 3 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Exception Handling, GUI Programming Basics |
| AI2002 | Database Management Systems | Professional Core | 3 | ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| AI2003 | Computer Architecture and Organization | Professional Core | 3 | Basic Computer Organization, CPU Design, Memory Hierarchy, Input/Output Organization, Pipelining |
| AI2004 | Operating Systems | Professional Core | 3 | Process Management, CPU Scheduling, Memory Management, File Systems, I/O Management |
| AI2005 | Object Oriented Programming and Design Lab | Professional Core | 1.5 | Java/Python OOP Implementation, Class and Object Creation, Inheritance and Interface Usage, Polymorphism Exercises, Exception Handling and File I/O |
| AI2006 | Database Management Systems Lab | Professional Core | 1.5 | SQL DDL and DML Commands, Advanced SQL Queries, Database Design, PL/SQL Programming, Database Connectivity (JDBC/ODBC) |
| PE2001 | Physical Education & Yoga | Mandatory | 1 | Yoga Asanas, Pranayama and Meditation, Physical Fitness, Stress Management, Healthy Lifestyle |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS2001 | Principles of Management and Organizational Behaviour | Humanities & Social Sciences | 3 | Management Principles, Planning and Organizing, Leadership Theories, Motivation and Teamwork, Organizational Culture |
| AI2007 | Design and Analysis of Algorithms | Professional Core | 3 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| AI2008 | Artificial Intelligence | Professional Core | 3 | AI Agents and Search, Knowledge Representation, Logical Reasoning, Machine Learning Basics, Natural Language Processing Introduction |
| AI2009 | Introduction to Data Science | Professional Core | 3 | Data Science Life Cycle, Data Collection and Cleaning, Exploratory Data Analysis, Data Visualization, Basic Statistical Modeling |
| AI2010 | Computer Networks | Professional Core | 3 | Network Models (OSI/TCP-IP), Physical Layer Concepts, Data Link Layer Protocols, Network Layer (IP, Routing), Transport Layer (TCP/UDP) |
| AI2011 | Artificial Intelligence Lab | Professional Core | 1.5 | Python for AI, Search Algorithm Implementation, Constraint Satisfaction Problems, Logic Programming (Prolog), Basic ML Algorithm Implementation |
| AI2012 | Data Science Lab | Professional Core | 1.5 | Python for Data Manipulation (Pandas), Data Visualization (Matplotlib, Seaborn), Data Preprocessing Techniques, Basic Machine Learning Models (Scikit-learn), Statistical Analysis |
| EN2001 | Essence of Indian Traditional Knowledge | Mandatory | 1 | Indian Knowledge Systems, Traditional Sciences, Indian Arts and Literature, Yoga and Ayurveda, Indian Ethical Values |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS3001 | Professional Ethics | Humanities & Social Sciences | 3 | Ethical Theories, Professionalism in Engineering, Cyber Ethics, Intellectual Property Rights, Corporate Social Responsibility |
| AI3001 | Machine Learning | Professional Core | 3 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation and Validation |
| AI3002 | Big Data Analytics | Professional Core | 3 | Big Data Concepts, Hadoop Ecosystem, MapReduce Framework, Spark for Big Data, NoSQL Databases |
| AI3003 | Deep Learning | Professional Core | 3 | Artificial Neural Networks, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning |
| PE-I | Professional Elective - I | Professional Elective | 3 | Specific topics will depend on the chosen elective such as Cloud Computing, Information Security, or Computer Graphics |
| OE-I | Open Elective - I | Open Elective | 3 | Specific topics will depend on the chosen elective offered by other departments |
| AI3004 | Machine Learning Lab | Professional Core | 1.5 | Scikit-learn Implementation, Model Training and Testing, Hyperparameter Tuning, Feature Engineering, Ensemble Methods |
| AI3005 | Big Data Analytics Lab | Professional Core | 1.5 | Hadoop Cluster Setup, HDFS Operations, MapReduce Programming, Spark Applications, Hive and Pig Scripting |
| AI3006 | Deep Learning Lab | Professional Core | 1.5 | TensorFlow/Keras/PyTorch, CNN for Image Classification, RNN for Sequence Data, Autoencoders and GANs, Model Deployment |
| PE3001 | Project Based Learning | Project | 1.5 | Problem Identification, Literature Review, Project Design, Implementation and Testing, Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI3007 | Natural Language Processing | Professional Core | 3 | Text Preprocessing, Word Embeddings, Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis |
| AI3008 | Data Warehousing and Data Mining | Professional Core | 3 | Data Warehouse Architecture, ETL Process, OLAP Operations, Data Mining Techniques, Clustering and Classification |
| AI3009 | Computer Vision | Professional Core | 3 | Image Fundamentals, Feature Detection (SIFT, SURF), Image Segmentation, Object Recognition, Motion Analysis |
| PE-II | Professional Elective - II | Professional Elective | 3 | Specific topics will depend on the chosen elective such as Wireless Sensor Networks, IoT Analytics, or Cognitive Computing |
| OE-II | Open Elective - II | Open Elective | 3 | Specific topics will depend on the chosen elective offered by other departments |
| AI3010 | Natural Language Processing Lab | Professional Core | 1.5 | NLTK and SpaCy, Text Classification, Chatbot Development, Machine Translation Basics, Information Extraction |
| AI3011 | Data Warehousing and Data Mining Lab | Professional Core | 1.5 | SQL for Data Warehousing, ETL Tool Usage, Data Mining Algorithm Implementation, Data Preprocessing for Mining, Result Visualization |
| AI3012 | Computer Vision Lab | Professional Core | 1.5 | OpenCV for Image Processing, Image Filtering and Segmentation, Object Detection Techniques, Face Recognition Systems, Video Analysis |
| IN3001 | Internship / Project-I | Internship | 3 | Industry Exposure, Problem Solving in Real-world, Technical Skill Application, Professional Communication, Project Documentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI4001 | Reinforcement Learning | Professional Core | 3 | Markov Decision Processes (MDPs), Value and Policy Iteration, Q-Learning and SARSA, Deep Reinforcement Learning, Exploration vs. Exploitation |
| AI4002 | Ethical AI and Data Privacy | Professional Core | 3 | AI Ethics Principles, Bias and Fairness in AI, Explainable AI (XAI), Data Privacy Regulations (GDPR, DPDPA), Data Security Concepts |
| PE-III | Professional Elective - III | Professional Elective | 3 | Specific topics will depend on the chosen elective such as Blockchain Technologies, Cyber Forensics, or Game AI |
| OE-III | Open Elective - III | Open Elective | 3 | Specific topics will depend on the chosen elective offered by other departments |
| AI4003 | Reinforcement Learning Lab | Professional Core | 1.5 | OpenAI Gym Environment, Q-Learning Implementation, SARSA Algorithm, Deep Q-Networks (DQN), Policy Gradient Methods |
| AI4004 | Ethical AI and Data Privacy Lab | Professional Core | 1.5 | Bias Detection and Mitigation, Fairness Metrics, Privacy-Preserving AI Techniques, Anonymization and De-identification, Ethical AI Frameworks Application |
| AI4005 | Project - II (Mini Project) | Project | 3 | Advanced Problem Definition, Research Methodology, System Design and Development, Testing and Evaluation, Technical Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI4006 | Project - III (Major Project) | Project | 10 | Comprehensive System Development, Innovation and Research, Advanced Algorithm Implementation, Large-scale Data Handling, Final Thesis and Presentation |
| IN4001 | Internship / Project-II | Internship | 6 | Extended Industrial Training, Advanced Project Implementation, Corporate Environment Understanding, Professional Skill Enhancement, Career Preparedness |
