| Educational Qualification | 12 pass Physics, chemistry and maths |
| Academic Requirements | Min 45% (Physics, chemistry and maths) |
| Entrance Exams | Qualifying In Entrance Exams Such As JEE Main, JEE Advanced, BITSAT, Or State-Level Engineering Entrance Exams, Depending On The Country And Institution. |
| Age Limit | 17-25 Years. |
| Nationality | Indian |
Our vision is to empower BTech students, nurturing them into innovative and socially responsible leaders within the dynamic technological landscape. They will be equipped to drive positive change and make meaningful contributions to the advancement of society.
The scope for BTech students transcends conventional bounds of technical education, encompassing a vast array of opportunities and challenges crucial for their holistic development. This includes both personal and professional growth, enabling them to navigate diverse spheres with confidence and competence.
| Eligibility Criteria | Description |
|---|---|
| Educational Qualification | Bachelor’s degree in a related field such as Computer Science, Mathematics, Statistics, Engineering, or a related discipline. |
| Some programs may require a minimum GPA (Grade Point Average) or equivalent academic performance. | |
| Programming Skills | Proficiency in programming languages such as Python, R, or SQL may be required. |
| Applicants should have a solid understanding of data structures, algorithms, and object-oriented programming concepts. | |
| Mathematical Background | Strong mathematical foundation, including knowledge of calculus, linear algebra, and probability theory. |
Our vision is to empower BTech students, nurturing them into innovative and socially responsible leaders within the dynamic technological landscape. They will be equipped to drive positive change and make meaningful contributions to the advancement of society.
The scope for BTech students transcends conventional bounds of technical education, encompassing a vast array of opportunities and challenges crucial for their holistic development. This includes both personal and professional growth, enabling them to navigate diverse spheres with confidence and competence.
| Module | Subjects |
|---|---|
| 1. Introduction to Data Science | – Introduction to Data Science |
| – Data Exploration and Visualization | |
| – Data Preprocessing and Cleaning | |
| – Descriptive and Inferential Statistics | |
| 2. Programming for Data Science | – Python Programming |
| – R Programming | |
| – SQL for Data Science | |
| 3. Machine Learning Fundamentals | – Supervised Learning |
| – Unsupervised Learning | |
| – Model Evaluation and Validation | |
| 4. Big Data Technologies | – Hadoop and MapReduce |
| – Spark and Apache Hadoop Ecosystem | |
| – NoSQL Databases | |
| 5. Data Analytics and Mining | – Predictive Analytics |
| – Text Mining and Natural Language Processing | |
| – Web Mining and Social Media Analytics | |
| 6. Data Visualization and Reporting | – Data Visualization Tools |
| – Storytelling with Data | |
| 7. Capstone Project | – Project Proposal and Planning |
| – Data Collection and Analysis | |
| – Model Development and Evaluation | |
| – Presentation and Documentation |
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