Explore programming, mathematics, data engineering, machine learning and responsible AI subjects.
Course Subjects
Artificial Intelligence and Data Science combines core computing, mathematics, statistics and specialised data subjects.
Important subjects may include:
- Programming
- Data Structures
- Algorithms
- Database Management Systems
- Operating Systems
- Computer Networks
- Probability and Statistics
- Linear Algebra
- Calculus
- Data Mining
- Data Warehousing
- Machine Learning
- Artificial Intelligence
- Deep Learning
- Big Data Analytics
- Data Visualisation
- Natural Language Processing
- Computer Vision
- Cloud Computing
- Data Engineering
- Software Engineering
- Information Security
- Responsible Artificial Intelligence
The exact syllabus and subject sequence differ by university.
Artificial Intelligence and Data Science Course Subjects and Syllabus for UG Course
First Year
The first year generally establishes the foundation.
| Semester I | Semester II |
|---|---|
| Engineering Mathematics I | Engineering Mathematics II |
| Programming Fundamentals | Data Structures |
| Engineering Physics | Basic Electronics |
| Engineering Chemistry | Digital Systems |
| Communication Skills | Object-Oriented Programming |
| Engineering Graphics | Environmental Studies |
| Programming Laboratory | Data Structures Laboratory |
Second Year
| Semester III | Semester IV |
|---|---|
| Discrete Mathematics | Probability and Statistics |
| Database Management Systems | Design and Analysis of Algorithms |
| Computer Organisation | Operating Systems |
| Object-Oriented Programming | Introduction to Artificial Intelligence |
| Data Communication | Data Mining |
| Database Laboratory | Statistics and Analytics Laboratory |
Third Year
| Semester V | Semester VI |
|---|---|
| Machine Learning | Deep Learning |
| Data Warehousing | Big Data Analytics |
| Data Visualisation | Natural Language Processing |
| Software Engineering | Computer Vision |
| Cloud Computing | Data Engineering |
| Machine Learning Laboratory | AI Application Laboratory |
| Mini Project | Internship or Industry Project |
Fourth Year
| Semester VII | Semester VIII |
|---|---|
| Responsible Artificial Intelligence | Advanced Elective |
| MLOps | Entrepreneurship or Management |
| Information Security | Major Project |
| Domain Elective | Seminar |
| Research Methodology | Internship or Project Evaluation |
| Major Project Phase I | Major Project Phase II |
This is a representative structure. Universities may use different subject names and semester arrangements.
Artificial Intelligence and Data Science Course Subjects and Syllabus for PG Course
A postgraduate programme normally provides advanced study and research exposure.
| Semester I | Semester II |
|---|---|
| Advanced Mathematics for Data Science | Deep Learning |
| Statistical Learning | Natural Language Processing |
| Advanced Data Structures | Computer Vision |
| Machine Learning | Big Data Systems |
| Research Methodology | Data Engineering |
| Programming Laboratory | Advanced AI Laboratory |
| Semester III | Semester IV |
|---|---|
| Responsible and Explainable AI | Dissertation |
| Advanced Elective | Research Publication or Seminar |
| MLOps and Deployment | Project Defence |
| Research Project Phase I | Research Project Phase II |
Electives may include:
- Generative Artificial Intelligence
- Reinforcement Learning
- Healthcare Analytics
- Financial Analytics
- Robotics
- Edge Artificial Intelligence
- Information Retrieval
- Advanced Computer Vision
- Speech Processing
- Privacy-preserving Machine Learning
Course Curriculum for Artificial Intelligence and Data Science
The curriculum should develop knowledge in a planned sequence.
Foundation Stage
Students study mathematics, programming, communication and basic engineering. The aim is to build confidence with calculations, logic and computer applications.
Computer Science Stage
Students learn data structures, algorithms, databases, operating systems, computer networks and software engineering.
These subjects help them build dependable applications instead of treating Artificial Intelligence as an isolated tool.
Data Science Stage
Students study probability, statistics, data cleaning, data visualisation, data mining and data warehousing.
They learn how to convert raw records into useful and explainable information.
Artificial Intelligence Stage
Students study intelligent search, reasoning, Machine Learning, Deep Learning, Natural Language Processing and Computer Vision.
Data Engineering Stage
Students learn how information is collected, stored, transformed and delivered through databases, cloud platforms and data pipelines.
Application Stage
Students apply their knowledge through laboratories, mini-projects, internships and a final-year project.
A good project should contain:
- A clear problem
- Data description
- Baseline method
- Model or analytical approach
- Testing
- Error analysis
- Limitations
- Responsible-use considerations
- Final demonstration
Continue your AI and Data Science research
Course at a Glance
- Course AreaComputing and emerging technology
- Study PathwaysB.E./B.Tech, postgraduate and research pathways
- Primary FocusArtificial intelligence, statistics, data engineering, analytics, machine learning and responsible data products.