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B-TECH in Data Science And Artificial Intelligence at Indian Institute of Technology Bhilai
Raipur, Chhattisgarh
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About the Specialization
What is Data Science and Artificial Intelligence at Indian Institute of Technology Bhilai Raipur?
This Data Science and Artificial Intelligence program at Indian Institute of Technology Bhilai focuses on building strong foundations in mathematics, statistics, computer science, and core AI/ML principles. The curriculum is designed to meet the rapidly growing demand for skilled professionals in India''''s booming digital and AI sectors, differentiating itself through a rigorous, research-oriented approach and hands-on laboratory experiences.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics and logical reasoning, seeking entry into cutting-edge technology fields like AI, ML, and data analytics. It also suits those passionate about solving complex problems using data, aspiring to become data scientists, AI engineers, or machine learning specialists in India''''s leading tech companies or startups.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding careers as AI engineers, data scientists, machine learning specialists, or research analysts in India. Entry-level salaries typically range from INR 8-15 LPA, growing significantly with experience. The program equips students with skills aligned with certifications in cloud AI platforms and enables strong growth trajectories in technology, finance, and healthcare sectors.
Student Success Practices
Foundation Stage
Master Core Mathematics and Programming- (Semester 1-2)
Dedicate significant effort to understanding fundamental concepts in Calculus, Linear Algebra, Probability, Statistics, and Introduction to Computing. These form the bedrock of Data Science and AI. Regularly solve problems and practice coding to solidify understanding.
Tools & Resources
NPTEL courses, Khan Academy, GeeksforGeeks, Coding platforms like HackerRank/CodeChef
Career Connection
Strong fundamentals enable grasping advanced AI/ML concepts easily, crucial for interview readiness and building robust models in future roles.
Develop Strong Problem-Solving Skills- (Semester 1-2)
Actively participate in programming contests and logic puzzles to sharpen analytical and problem-solving abilities. Focus on understanding algorithm design and data structures early on, as these are critical for efficient AI solutions.
Tools & Resources
Competitive programming sites, LeetCode, Data Structures and Algorithms textbooks
Career Connection
Enhances logical thinking, which is highly valued by recruiters for technical roles in AI/ML engineering and research.
Engage in Peer Learning and Group Study- (Semester 1-2)
Form study groups to discuss complex topics, clarify doubts, and work on assignments collaboratively. Teaching peers reinforces your own understanding and exposes you to different problem-solving approaches.
Tools & Resources
Study groups, Academic clubs, Online collaboration tools
Career Connection
Fosters teamwork and communication skills, essential for working in cross-functional teams in the tech industry.
Intermediate Stage
Build Applied Machine Learning Projects- (Semester 3-5)
Beyond coursework, actively seek out and complete practical machine learning projects. Utilize open datasets to build, train, and evaluate models, focusing on understanding the entire ML pipeline.
Tools & Resources
Kaggle, GitHub, Python libraries (scikit-learn, pandas, numpy), TensorFlow/PyTorch
Career Connection
Creates a strong project portfolio, demonstrating practical skills to potential employers and preparing for real-world data challenges.
Seek Early Industry Exposure through Internships/Workshops- (Semester 3-5)
Look for summer internships or workshops related to data science, AI, or software development. Even short-term engagements provide invaluable insights into industry practices and networking opportunities.
Tools & Resources
Internshala, LinkedIn, IIT Bhilai Career Development Cell
Career Connection
Gains practical experience, helps in career path clarification, and builds professional networks that can lead to placements.
Specialize and Explore Advanced AI Concepts- (Semester 3-5)
Begin exploring advanced topics like Deep Learning, NLP, or Computer Vision through online courses or personal study. Understand the underlying theories and their applications, aligning with personal interests.
Tools & Resources
Coursera/edX (DeepLearning.AI), Specialized journals, Research papers
Career Connection
Develops a specialized skillset, making you a more attractive candidate for niche AI roles and research opportunities.
Advanced Stage
Focus on Placement-Oriented Preparation- (Semester 6-8)
Intensively prepare for technical interviews, focusing on DSA, core CS concepts, and AI/ML algorithms. Practice mock interviews and aptitude tests regularly, leveraging campus resources.
Tools & Resources
InterviewBit, Glassdoor, Company-specific interview guides, IIT Bhilai Placement Cell
Career Connection
Maximizes chances of securing top placements in leading AI/ML and data science companies.
Undertake a Comprehensive Major Project- (Semester 7-8)
Invest deeply in your Major Project, aiming for novel contributions or significant real-world impact. Collaborate with faculty, publish research if possible, and build a demonstrable, robust system.
Tools & Resources
Research labs, Faculty mentors, arXiv, Conference proceedings
Career Connection
Showcases advanced skills, research capabilities, and can be a significant differentiator for higher studies or specialized roles.
Network Actively and Attend Tech Events- (Semester 6-8)
Attend industry conferences, workshops, and seminars in AI/ML, both on-campus and off-campus. Network with professionals, recruiters, and alumni to explore opportunities and gain insights.
Tools & Resources
LinkedIn, Tech events calendars, Alumni network
Career Connection
Expands professional connections, leads to referrals, and keeps you updated on industry trends for informed career decisions.
Program Structure and Curriculum
Eligibility:
- Candidates must have passed 12th class or equivalent in 2023 or 2024 with Mathematics, Physics, and Chemistry (or any other subject). Admission is primarily through the Joint Entrance Examination (Advanced) and Joint Seat Allocation Authority (JoSAA), meeting specified age limits.
Duration: 8 semesters / 4 years
Credits: 179 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PH101 | Engineering Physics I | Core | 4 | |
| MA101 | Calculus | Core | 4 | |
| CE101 | Engineering Drawing | Core | 3 | |
| HS101 | English for Communication | Core | 3 | |
| HS102 | Values and Ethics | Core | 2 | |
| CP101 | Introduction to Computing | Core | 3 | |
| CP102 | Introduction to Computing Lab | Lab | 2 | |
| PH102 | Engineering Physics I Lab | Lab | 2 |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PH103 | Engineering Physics II | Core | 4 | |
| MA102 | Linear Algebra and Differential Equations | Core | 4 | |
| CY101 | Engineering Chemistry | Core | 4 | |
| EE101 | Basic Electrical Engineering | Core | 4 | |
| ME101 | Engineering Mechanics | Core | 4 | |
| CY102 | Engineering Chemistry Lab | Lab | 2 | |
| EE102 | Basic Electrical Engineering Lab | Lab | 2 |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA201 | Probability and Statistics | Core | 4 | |
| CP201 | Data Structures | Core | 4 | |
| CP202 | Data Structures Lab | Lab | 2 | |
| CP203 | Discrete Structures | Core | 4 | |
| CP204 | Digital Logic and Computer Architecture | Core | 4 | |
| CP205 | Digital Logic and Computer Architecture Lab | Lab | 2 | |
| EE201 | Principles of Electronics Engineering | Core | 4 | |
| EE202 | Principles of Electronics Engineering Lab | Lab | 2 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CP206 | Design and Analysis of Algorithms | Core | 4 | |
| CP207 | Design and Analysis of Algorithms Lab | Lab | 2 | |
| CP208 | Object-Oriented Programming | Core | 3 | |
| CP209 | Object-Oriented Programming Lab | Lab | 2 | |
| CP210 | Database Management Systems | Core | 4 | |
| CP211 | Database Management Systems Lab | Lab | 2 | |
| DS201 | Machine Learning | Core | 4 | |
| DS202 | Machine Learning Lab | Lab | 2 | |
| HSxxx | Humanity Elective I | Elective (Humanity) | 3 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CP301 | Operating Systems | Core | 4 | |
| CP302 | Operating Systems Lab | Lab | 2 | |
| CP303 | Computer Networks | Core | 4 | |
| CP304 | Computer Networks Lab | Lab | 2 | |
| DS301 | Artificial Intelligence | Core | 4 | |
| DS302 | Artificial Intelligence Lab | Lab | 2 | |
| DS303 | Data Mining | Core | 4 | |
| DS304 | Data Mining Lab | Lab | 2 | |
| HSxxx | Humanity Elective II | Elective (Humanity) | 3 |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS305 | Deep Learning | Core | 4 | |
| DS306 | Deep Learning Lab | Lab | 2 | |
| DS307 | Natural Language Processing | Core | 4 | |
| DS308 | Natural Language Processing Lab | Lab | 2 | |
| OExxx | Open Elective I | Elective (Open) | 3 | |
| DSExxx | Departmental Elective I | Elective (Departmental) | 3 | |
| DSExxx | Departmental Elective II | Elective (Departmental) | 3 | |
| DS401 | Summer Internship | Project | 2 |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS402 | Major Project - Part I | Project | 6 | |
| OExxx | Open Elective II | Elective (Open) | 3 | |
| DSExxx | Departmental Elective III | Elective (Departmental) | 3 | |
| DSExxx | Departmental Elective IV | Elective (Departmental) | 3 | |
| DSExxx | Departmental Elective V | Elective (Departmental) | 3 |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS403 | Major Project - Part II | Project | 6 | |
| OExxx | Open Elective III | Elective (Open) | 3 | |
| DSExxx | Departmental Elective VI | Elective (Departmental) | 3 |
Semester electives
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS351 | Introduction to Cyber Security | Elective (Departmental) | 3 | |
| DS352 | Introduction to IoT | Elective (Departmental) | 3 | |
| DS353 | Blockchain Fundamentals | Elective (Departmental) | 3 | |
| DS354 | Computer Vision | Elective (Departmental) | 3 | |
| DS355 | Reinforcement Learning | Elective (Departmental) | 3 | |
| DS356 | Big Data Analytics | Elective (Departmental) | 3 | |
| DS451 | Speech and Audio Processing | Elective (Departmental) | 3 | |
| DS452 | Graph Neural Networks | Elective (Departmental) | 3 | |
| DS453 | Time Series Analysis | Elective (Departmental) | 3 | |
| DS454 | Data Visualization | Elective (Departmental) | 3 | |
| DS455 | Explainable AI | Elective (Departmental) | 3 | |
| DS456 | Generative AI | Elective (Departmental) | 3 | |
| DS457 | Bio-inspired AI | Elective (Departmental) | 3 | |
| DS458 | AI for Robotics | Elective (Departmental) | 3 | |
| DS459 | AI in Healthcare | Elective (Departmental) | 3 | |
| DS460 | Quantum Machine Learning | Elective (Departmental) | 3 | |
| DS461 | Federated Learning | Elective (Departmental) | 3 | |
| DS462 | Algorithmic Trading | Elective (Departmental) | 3 |
