.png&w=1920&q=75)
B-TECH in Data Science And Artificial Intelligence at Indian Institute of Technology Roorkee
Haridwar, Uttarakhand
.png&w=1920&q=75)
About the Specialization
What is Data Science and Artificial Intelligence at Indian Institute of Technology Roorkee Haridwar?
This Data Science and Artificial Intelligence program at IIT Roorkee focuses on equipping students with a robust foundation in cutting-edge AI, machine learning, and big data technologies. It addresses the escalating demand for skilled professionals in India''''s rapidly growing digital economy. The program uniquely blends theoretical knowledge with practical applications, fostering innovation and problem-solving capabilities essential for the industry.
Who Should Apply?
This program is ideal for analytically inclined fresh graduates seeking entry into the high-demand fields of AI, data science, and machine learning. It also suits working professionals aiming to upskill for advanced roles or career changers transitioning into data-driven industries. Strong foundational knowledge in mathematics, statistics, and programming is beneficial for prospective students.
Why Choose This Course?
Graduates of this program can expect to pursue dynamic career paths as AI Engineers, Data Scientists, Machine Learning Specialists, or Business Intelligence Analysts in India. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals earning significantly more. The strong curriculum aligns with requirements for various professional certifications and advanced research opportunities.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate significant time to programming languages like Python and C/C++ by solving problems on platforms like HackerRank, LeetCode, and CodeChef. This builds a strong base for data structures and algorithms, crucial for all subsequent technical courses and placements.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks
Career Connection
Strong programming fundamentals are the bedrock for passing technical interviews and excelling in early career roles in AI/Data Science.
Cultivate Mathematical and Statistical Acuity- (Semester 1-2)
Focus intensely on Linear Algebra, Calculus, Probability, and Statistics. Utilize resources like Khan Academy, NPTEL courses, and specialized textbooks. A solid grasp of these concepts is indispensable for understanding core AI and ML algorithms.
Tools & Resources
NPTEL courses, Khan Academy, Standard Textbooks
Career Connection
A deep understanding of underlying math and stats enables better algorithm selection, model interpretation, and problem-solving in data-driven fields.
Engage in Peer Learning and Competitive Coding- (Semester 1-2)
Form study groups with peers to discuss concepts, solve problems, and prepare for competitive programming contests. This enhances problem-solving skills, fosters teamwork, and builds a strong network, vital for academic and career growth.
Tools & Resources
Study groups, Competitive programming platforms, IITR student clubs
Career Connection
Teamwork and competitive problem-solving skills are highly valued by recruiters for technical and collaborative roles.
Intermediate Stage
Apply Theoretical Knowledge through Projects- (Semester 3-5)
Actively seek out small projects related to Machine Learning, Databases, and Operating Systems. Use platforms like Kaggle for data science competitions and develop mini-projects using libraries like Scikit-learn, Pandas, and SQL. This practical exposure solidifies understanding and builds a portfolio.
Tools & Resources
Kaggle, Scikit-learn, Pandas, SQL, GitHub
Career Connection
A strong project portfolio demonstrates practical skills to potential employers and provides talking points during interviews.
Explore Electives and Industry Exposure- (Semester 3-5)
Strategically choose department electives that align with emerging AI/Data Science trends like NLP, Computer Vision, or Big Data. Attend workshops, industry talks, and summer internships to gain real-world insights and network with professionals, bridging the gap between academia and industry.
Tools & Resources
Department elective lists, IITR career fair, LinkedIn, Industry workshops
Career Connection
Specialized knowledge from electives and industry experience makes candidates more attractive for niche roles and competitive job markets.
Build a Strong LinkedIn Profile and Portfolio- (Semester 3-5)
Document all projects, internships, and skill developments on a professional LinkedIn profile and a personal portfolio website. Start connecting with alumni and industry leaders, showcasing your capabilities for future internship and job opportunities.
Tools & Resources
LinkedIn, GitHub Pages, Personal website builders
Career Connection
A professional online presence is crucial for networking, attracting recruiters, and showcasing your expertise effectively.
Advanced Stage
Specialize through Capstone Projects and Research- (Semester 6-8)
Undertake significant capstone projects (Project Part I & II) or research under faculty guidance, focusing on a niche area of AI/Data Science. Aim for impactful solutions or publications. Utilize advanced ML frameworks (TensorFlow, PyTorch) and cloud platforms (AWS, Azure, GCP). This demonstrates advanced expertise.
Tools & Resources
TensorFlow, PyTorch, AWS, Azure, GCP, Research papers
Career Connection
High-impact projects or research publications enhance credibility for advanced roles, research positions, or higher studies.
Intensive Placement Preparation- (Semester 6-8)
Focus on mock interviews, coding rounds, and technical aptitude tests for companies targeting AI/DS roles. Practice behavioral interviews and refine your resume and cover letter. Leverage the institute''''s placement cell and alumni network for guidance and referrals.
Tools & Resources
IITR Placement Cell, Mock interview platforms, Resume builders, Alumni network
Career Connection
Thorough preparation directly translates to successful placements in top-tier companies and desired roles.
Develop Communication and Leadership Skills- (undefined)
Participate in technical presentations, seminars, and group discussions to hone communication skills. Take on leadership roles in student chapters or project teams. These soft skills are critical for career progression into managerial or team lead positions in Indian tech companies.
Tools & Resources
Public speaking workshops, Student organizations, Project team leadership
Career Connection
Strong communication and leadership are essential for career advancement, client interaction, and leading technical teams in the industry.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 40% (for theory courses), 100% (for lab/project courses), External: 60% (for theory courses), 0% (for lab/project courses)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HUL-101 | Technical Communication | Core | 2 | Communication Process, Oral Communication Skills, Written Communication, Technical Report Writing, Presentation Skills |
| MAL-101 | Linear Algebra and Differential Equations | Core | 4 | Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors, First Order Ordinary Differential Equations, Higher Order Ordinary Differential Equations |
| ECL-101 | Principles of Electrical Engineering | Core | 3 | DC Circuit Analysis, AC Circuit Analysis, Transformers and Motors, Diode Characteristics, Transistor Fundamentals |
| CSP-101 | Programming for Problem Solving | Core | 3 | Programming Language Fundamentals, Data Types and Operators, Control Flow Statements, Functions and Modular Programming, Arrays and Strings |
| ECP-101 | Electrical Engineering Lab | Lab | 1 | Basic Electrical Measurements, Verification of Circuit Laws, Characteristics of Diodes, Transistor Amplifier Circuits, Transformer Operations |
| CSP-102 | Programming for Problem Solving Lab | Lab | 1 | Basic C/Python Programs, Conditional and Looping Constructs, Function Implementation, Array Manipulation, Debugging Techniques |
| MPP-101 | Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Introduction to CAD |
| GEL-101 | Environmental Science & Engineering | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Water and Energy Resources, Climate Change, Sustainable Development |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HUL-102 | Ethics and Indian Society | Core | 2 | Foundations of Ethics, Professional Ethics, Indian Philosophical Traditions, Social Issues in India, Human Rights and Values |
| MAL-102 | Probability, Statistics and Stochastic Processes | Core | 4 | Axioms of Probability, Random Variables and Distributions, Statistical Inference, Regression and Correlation, Markov Chains and Queuing Theory |
| PYL-101 | Introduction to Quantum Mechanics | Core | 3 | Wave-Particle Duality, Schrodinger Equation, Quantum Operators, Atomic Structure, Introduction to Solids |
| PYP-101 | Physics Lab | Lab | 1 | Optical Experiments, Electronic Device Characterization, Magnetic Field Measurements, Oscillations and Waves, Error Analysis |
| CHL-101 | Material Chemistry | Core | 3 | Chemical Bonding and Structure, Thermodynamics, Electrochemistry, Polymer Chemistry, Nanomaterials |
| CHP-101 | Chemistry Lab | Lab | 1 | Volumetric Analysis, pH Titrations, Spectrophotometric Analysis, Synthesis of Organic Compounds, Water Quality Testing |
| CSL-101 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms, Algorithm Analysis |
| CSP-103 | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Stacks and Queues, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Implementations, Recursion Practice |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAL-201 | Discrete Mathematics | Core | 4 | Mathematical Logic, Set Theory and Relations, Functions and Combinatorics, Graph Theory, Boolean Algebra |
| CSL-201 | Computer Organization and Architecture | Core | 4 | Digital Logic Circuits, CPU Organization, Memory Hierarchy, Input/Output Organization, Pipelining and Parallelism |
| CSL-202 | Object-Oriented Programming | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Exception Handling, Object-Oriented Design Principles |
| CSP-201 | Object-Oriented Programming Lab | Lab | 1 | C++/Java Programming, Class and Object Implementation, Inheritance Examples, Polymorphism Usage, File Handling and Exceptions |
| CSL-203 | Database Management Systems | Core | 4 | Relational Model, SQL Query Language, Entity-Relationship Modeling, Normalization, Transaction Management |
| CSP-202 | Database Management Systems Lab | Lab | 1 | SQL DDL and DML Commands, Database Schema Design, Joining Tables, Stored Procedures and Functions, Transaction Control |
| CSL-204 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression and Classification, Model Evaluation and Validation, Feature Engineering |
| CSP-203 | Machine Learning Lab | Lab | 1 | Python for ML, Scikit-learn, Data Preprocessing, Implementing ML Algorithms, Hyperparameter Tuning |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HUL-20X | Humanities Elective - I | Elective | 2 | Critical Thinking, Literary Analysis, Social Psychology, Public Speaking, Cultural Studies |
| CSL-205 | Operating Systems | Core | 4 | Process Management, Memory Management, File Systems, I/O Management, Concurrency and Deadlocks |
| CSP-204 | Operating Systems Lab | Lab | 1 | Shell Scripting, System Calls, Process Synchronization, Memory Allocation Simulation, File System Operations |
| CSL-206 | Design and Analysis of Algorithms | Core | 4 | Asymptotic Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms and Complexity Classes |
| CSL-207 | Deep Learning | Core | 4 | Neural Network Architectures, Backpropagation and Optimization, Convolutional Neural Networks, Recurrent Neural Networks, Generative Models |
| CSP-205 | Deep Learning Lab | Lab | 1 | TensorFlow/PyTorch Implementation, Image Classification with CNNs, Sequence Modeling with RNNs, Transfer Learning, Model Deployment Basics |
| CSL-208 | Big Data Analytics | Core | 4 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Paradigm, Spark and its Components, NoSQL Databases |
| CSP-206 | Big Data Analytics Lab | Lab | 1 | HDFS Operations, Implementing MapReduce Jobs, Spark RDD and DataFrames, Hive Queries, Data Ingestion Tools |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HUL-30X | Humanities Elective - II | Elective | 2 | Humanities and Social Sciences topics, Literature and Society, Art History, Philosophy of Science, Psychology of Learning |
| CSL-301 | Computer Networks | Core | 4 | OSI and TCP/IP Models, Network Protocols (HTTP, FTP), Routing Algorithms, Transport Layer Protocols (TCP, UDP), Network Security Fundamentals |
| CSP-301 | Computer Networks Lab | Lab | 1 | Socket Programming, Packet Sniffing and Analysis, Client-Server Communication, Network Configuration, Simulating Network Protocols |
| CSL-302 | Natural Language Processing | Core | 4 | Text Preprocessing, Language Models, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation |
| CSP-302 | Natural Language Processing Lab | Lab | 1 | NLTK Library, Word Embeddings, Text Classification, Named Entity Recognition, Building Chatbots |
| CSL-303 | Computer Vision | Core | 4 | Image Formation, Image Processing Techniques, Feature Detection and Matching, Object Recognition, Deep Learning for Vision |
| CSP-303 | Computer Vision Lab | Lab | 1 | OpenCV Library, Image Filtering, Edge Detection, Object Tracking, Facial Recognition |
| DSX-30X | Department Elective - I | Elective | 3 | Advanced Machine Learning, Reinforcement Learning, Data Warehousing, Cloud Computing, Graph Neural Networks |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSL-304 | Artificial Intelligence | Core | 4 | Search Algorithms (Heuristic, Adversarial), Knowledge Representation, Logic Programming (Prolog), Planning and Reasoning, Expert Systems |
| CSP-304 | Artificial Intelligence Lab | Lab | 1 | Heuristic Search Implementations, Prolog Programming, Game AI Agents, Constraint Satisfaction Problems, Knowledge Representation Systems |
| DSX-30X | Department Elective - II | Elective | 3 | Explainable AI, Federated Learning, Bayesian Machine Learning, Time Series Analysis, Speech Processing |
| DSX-30X | Department Elective - III | Elective | 3 | Information Retrieval, Data Mining, Blockchain Technology, IoT and Edge AI, Generative AI |
| DEP-301 | Design Project | Project | 4 | Problem Definition and Analysis, Literature Survey, System Design and Architecture, Implementation and Testing, Project Documentation |
| OEX-30X | Open Elective - I | Elective | 3 | Interdisciplinary subjects, Management Principles, Entrepreneurship, Advanced Physics, Biological Sciences |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSX-40X | Department Elective - IV | Elective | 3 | Robotics, Game Theory, Bioinformatics, Data Visualization, Cyber Security for AI |
| DSX-40X | Department Elective - V | Elective | 3 | Quantum Computing, Advanced Data Structures, Embedded Systems, Human Computer Interaction, Digital Image Processing |
| OEX-40X | Open Elective - II | Elective | 3 | Advanced Management, Economics, Environmental Studies, Computational Finance, Operations Research |
| DEP-401 | Project Part-I | Project | 4 | Advanced Problem Formulation, Research Methodology, System Design Refinement, Prototype Development, Interim Report and Presentation |
| INP-401 | Industrial Training / Internship | Core | 2 | Industry Exposure, Real-world Problem Solving, Professional Skill Development, Project Documentation, Industry Best Practices |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSX-40X | Department Elective - VI | Elective | 3 | Cryptography and Network Security, Distributed Systems, Theory of Computation, Compiler Design, Wireless Sensor Networks |
| OEX-40X | Open Elective - III | Elective | 3 | Global Business Strategy, Intellectual Property Rights, Supply Chain Management, Advanced Material Science, Sustainable Engineering |
| DEP-402 | Project Part-II | Project | 8 | Final System Implementation, Comprehensive Testing and Validation, Performance Evaluation, Thesis Writing, Viva-Voce Examination |
