B.Tech Artificial Intelligence and Data Science
Course Overview
The B.Tech - Artificial Intelligence and Data Science specialization is a 4-year undergraduate course empowering students to use artificial intelligence and data science to address complex real-world challenges. It offers in-depth knowledge of machine learning, neural networks, data visualization, and predictive analytics. Practical exposure through projects and internships ensures students can build intelligent systems and make data-driven decisions. The curriculum focuses on innovation in AI tools and applications across various industries. Graduates are well-prepared for careers in AI research, data analysis, and automation in sectors like healthcare, finance, and technology.
Course Objectives
- To build a solid foundation in AI and data science fundamentals.
- To train students in machine learning and neural network applications.
- To provide practical experience in data visualization and analytics.
- To prepare students for innovation in AI-driven industries.
Learning Outcomes
- Proficiency in designing AI-based systems and tools.
- Ability to analyze and visualize data for informed decision-making.
- Expertise in predictive analytics and automation.
- Preparedness for careers in AI research and data science.
Curriculum
Semester 1
Practical
- Physics Lab
- Chemistry Lab
- Basic Data Science Lab
- Programming Lab (Python/C)
Theory
- Communication Skills I
- Mathematics I (Calculus and Linear Algebra)
- Physics for Computer Science
- Fundamentals of Data Science
- Introduction to Programming (Python/C)
- Engineering Graphics
Semester 2
Practical
- Data Structures Lab
- Object-Oriented Programming Lab
- Workshop Practices
Theory
- Communication Skills II
- Mathematics II (Probability and Statistics)
- Data Structures and Algorithms
- Object-Oriented Programming (OOP) in Java
- Environmental Studies
- Fundamentals of Artificial Intelligence
Semester 3
Practical
- AI Lab: Basic AI models (search algorithms, heuristic-based methods)
- DBMS Lab: SQL queries, database design
- Machine Learning Lab: Implementing regression, classification, clustering models
Theory
- Discrete Mathematics
- Database Management Systems (DBMS)
- Machine Learning Fundamentals
- Operating Systems
- Software Engineering
- Digital Logic and Computer Organization
Semester 4
Practical
- Deep Learning Lab: Basics of neural networks (using TensorFlow or PyTorch)
- Computer Networks Lab
- Data Mining and Warehousing
Theory
- Mathematics III (Graph Theory and Numerical Methods)
- Deep Learning Basics
- Computer Networks
- Data Mining and Warehousing
- Human-Computer Interaction
- Economics for Engineers
Semester 5
Practical
- Computer Vision Lab: Object detection, image classification
- Big Data Lab: Hadoop, Spark basics
- LP Lab: Sentiment analysis, text classification
Theory
- Operation Research
- Advanced Machine Learning
- Natural Language Processing (NLP)
- Big Data Analytics
- Computer Vision
- Web Technologies
Semester 6
Practical
- Machine Learning Lab
- Big Data Lab
- Software Testing Lab
Theory
- Machine Learning
- Big Data Analytics
- Software Testing and Quality Assurance
- Professional Elective II
- Open Elective II
Semester 7
Practical
- Major Project - Phase I
- Deployment Lab: Model deployment on web/mobile platforms
Theory
- Advanced Deep Learning (GANs, Transformers)
- AI in Healthcare, Finance, and Industry
- Real-Time Systems and AI
- Electives (e.g., AI in Robotics, Quantum Computing, Cybersecurity)
- AI Startups and Entrepreneurship
Semester 8
Project
- Project Phase II
Student Life
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