| Semester | Subjects |
|---|---|
| Semester 1 | Introduction to Data Science, Mathematics for Data Science, Programming Fundamentals, English Communication, Environmental Studies |
| Semester 2 | Database Management Systems, Data Structures, Object-Oriented Programming, Probability and Statistics, Digital Principles and Systems Design |
| Semester 3 | Operating Systems, Web Technologies, Python for Data Science, Linear Algebra, Minor Project I |
| Semester 4 | Machine Learning, Big Data Analytics, Data Visualization, Elective I, Minor Project II |
| Semester 5 | Deep Learning, Cloud Computing for Data Science, Elective II, Elective III, Major Project I |
| Semester 6 | Artificial Intelligence, Data Science Capstone Project, Elective IV, Elective V, Industrial Training/Internship |
| Topic | Description |
|---|---|
| Curriculum Overview | Details on courses covering programming, machine learning, statistics, and data management. |
| Faculty Expertise | Information on the qualifications and areas of specialization of the faculty members. |
| Practical Exposure | Insights into internships, projects, and industry collaborations that provide real-world experience. |
| Infrastructure | Details on the labs, libraries, and computing facilities available for students. |
| Placement Opportunities | Statistics and information on campus recruitment and job prospects in the field of data science. |
| Alumni Network | Information on the alumni community and how it supports current students and recent graduates. |
| Admission Criteria | Eligibility requirements, selection process, and important dates for applicants. |
| Scholarships and Financial Aid | Details on scholarships, grants, and other financial support available to students. |
