Programme Outcomes
PROGRAM OUTCOME (PO)
PO1: Apply the data science principles, tools and techniques to model and analyze various real world business problems, and suggest a suitable solution by communicating relevant findings and effectively presenting results using appropriate data visualization techniques.
PO2: Identify, formulate, research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
PO3: Understand the recent developments and applications of big data analytics in social and web media firms for prediction and recommendation.
PO4: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
PO5: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
PO6: Recognize various issues in everyday business; apply data science to understand and make well-reasoned, data-driven management decisions.
PO7: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
Curriculum & Syllabus
CURRICULUM & SYLLABUS
MINIMUM CREDITS TO BE EARNED: 140
Code No. | Course | Hours/Week | Maximum Marks | |||||
---|---|---|---|---|---|---|---|---|
Lecture | Tutorial | Practical | Credits | CA | SEE | Total |
||
LANG | Tamil I/ Hindi / French | 5 | 0 | 0 | 5 | 40 | 60 | 100 |
ENG | English I | 5 | 0 | 0 | 5 | 40 | 60 | 100 |
CORE | Programming in C | 4 | 1 | 0 | 5 | 40 | 60 | 100 |
CORE | Mathematics - I | 4 | 0 | 0 | 4 | 40 | 60 | 100 |
CORE | Programming in C Lab | 0 | 0 | 4 | 2 | 40 | 60 | 100 |
CORE | MS Office Lab | 0 | 0 | 4 | 2 | 40 | 60 | 100 |
18 | 1 | 8 | 23 |
Eligibility Criteria
Program | Eligibility | Criteria for Merit |
---|---|---|
B.C.A Data Science - II Shift | Pass in (10+2) or equivalent with Maths / Computer Science / Business Maths / Statistics / Computer Applications / Information Practice Lateral Entry: Pass in Diploma in Computer Science / ECE / Information Technology / Computer Technology | Merit - based on percentage of marks secured in the qualifying examination. |
Career Prospects
CAREER PROSPECTS
- Applications Architect
- Business Intelligence Developer
- Data Architect
- Data Analyst
- Data Scientist
- Data Engineer
- Enterprise Architect
- Infrastructure Architect
- Machine Learning Scientist
- Maching Learning Engineer
Fee Structure
Tuition Fee 2023 - 2024 (Per Sem) | Other Fee (Per Sem) | Total Fee (Per Sem) |
---|---|---|
55,000 | 6,500 | 61,500 |