- Faculty
School of Social Sciences
- Department
Department of Data Sciences And Analytics
- Campus
Gnanagangothri Campus
- Engagement Mode
Full Time
- Study
4 Years
Overview
The B.Sc. (Hons.) in Data Sciences and Analytics equips students with the core competencies needed to collect, process, analyze, and model data for real-world decision-making. Combining strong foundations in mathematics, statistics, and computing with hands-on exposure to modern data science tools, the programme emphasizes practical learning in data mining, exploratory analysis, machine learning, and predictive analytics through capstone research projects or internships. Students graduate with the skills to extract insights, build data-driven solutions, and adapt to rapidly evolving data-centric industries.
Program Objectives
- To impart high-quality interdisciplinary education through teaching and academic activities, preparing students to meet the evolving needs of industry, business, and society.
- To generate new knowledge through rigorous, ethical, and impactful research that addresses contemporary and emerging challenges across disciplines.
- To foster human well-being by advancing holistic healthcare practices through integrated education, research, and community engagement.
- To offer scientific, technical, analytical, and creative solutions to real-life problems through applied research, consultancy, and innovation.
- To foster entrepreneurial thinking by nurturing innovation, supporting technology-based ventures, and enabling sustainable careers and societal impact.
- To develop ethical, socially responsible leaders with strong leadership and interpersonal skills.
- To strengthen collaborations with academic, industrial, and international partners to enhance teaching, research, and development.
Curriculum Details
| Sl. No. |
Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | SSF101A | Compulsory Foundation Course 1 (CFC 1) | 4 | 4 | 100 | ||
| 2 | SSF102A | CompulsoryFoundation Course 2 (CFC 2) | 4 | 4 | 100 | ||
| 3 | DSC103A | Data Visualisation (CC) | 4 | 2 | 100 | ||
| 4 | DSC107A | Introduction to Programming (CC) | 4 | 2 | 5 | 100 | |
| 5 | DSU101A | Ability Enhancement Course 1 (AEC) | 3 | 3 | 100 | ||
| 6 | SSF109A | Compulsory Foundation Course 3 (CFC 3) | 2 | 2 | 100 | ||
| Total | 17 | 6 | 20 | 600 | |||
| Total number of contact hours per week | 23 | ||||||
| Sl.No. | Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credit s | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | DSC101A | Maths for Data Science (CC) | 5 | 5 | 100 | ||
| 2 | SSF104A | Compulsory Foundation Course 4 (CFC 4) | 4 | 4 | 100 | ||
| 3 | DSC104A | Inferential Statistics (CC) | 5 | 5 | 100 | ||
| 4 | DSC106A | Advanced Programming (CC) | 3 | 4 | 4 | 100 | |
| 5 | DSO101A | Open Elective | 3 | 3 | 100 | ||
| 6 | DSU101A | Skill Enhancement Course 1 (SEC) | 2 | 2 | 100 | ||
| Total | 22 | - | 4 | 23 | 600 | ||
| Total number of contact hours per week | 26 | ||||||
| Sl. No. |
Code | Course Title | Theory (h/W/S) | Tutorials (h/W/S) | Practical (h/W/S) | Total Credits | Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | DSC105A | Regression Techniques and Time series Analysis (CC) | 4 | 2 | 5 | 100 | |
| 2 | DSC202A | Data base Management Systems (CC) | 4 | 2 | 5 | 100 | |
| 3 | DSC203A | Data Pre-processing (CC) |
4 | 2 | 5 | 100 | |
| 4 | DSO201A | Open Elective | 3 | 3 | 100 | ||
| 5 | DSU201A | Ability Enhancement Course 2 (AEC) | 3 | 3 | 100 | ||
| Total | 18 | - | 6 | 21 | 500 | ||
| Total number of contact hours per week | 24 | ||||||
| Sl. No. |
Code | Course Title | Theory (h/W/S) |
Tutorials (h/W/S) |
Practical (h/W/S) |
Total Credits |
Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | DSC201A | Multivariate Analysis(CC) | 4 | 2 | 5 | 100 | |
| 2 | DSC205A | Data Warehousing & Mining (CC) | 4 | 2 | 5 | 100 | |
| 3 | DSC207A | Artificial Intelligence (CC) | 5 | 5 | 100 | ||
| 4 | DSO202A | Open Elective | 3 | 3 | 100 | ||
| 5 | DSU202A | Skill Enhancement Course – 2 (SEC-2) |
2 | 2 | 100 | ||
| 6 | BAU201A | Entrepreneurial Mindset and Action | 3 | 0 | 3 | 100 | |
| Total | 21 | 4 | 23 | 600 | |||
| Total number of contact hours per week | 24 | ||||||
| Sl.No. | Code | Course Title | Theory(h/W/S) | Tutorials(h/W/S) | Practical(h/W/S) | TotalCredits | Max.Marks |
|---|---|---|---|---|---|---|---|
| 1 | DSC301A | Machine Learning (CC) | 4 | 2 | 5 | 100 | |
| 2 | DSC204A | Operation Research & Optimization Techniques ion Techniques (CC) | 4 | 2 | 5 | 100 | |
| 3 | DSE301A | Advanced Time Series and Regression Techniques (DSE) (Track 1) | 3 | 2 | 4 | 100 | |
| 4 | DSE303A | Advanced Statistics (DSE) (Track 2) | 4 | 4 | 100 | ||
| 5 | SEC5 | Cyber Security | 4 | 2 | 3 | 100 | |
| 6 | DSO301A | Open Elective | 3 | 3 | 100 | ||
| Total | 18/19 | 8/6 | 23 | 500 | |||
| Total number of contact hours per week | 26/25 | ||||||
| Sl. No. |
Code | Course Title | Theory (h/W/S) |
Tutorials (h/W/S) |
Practical (h/W/S) |
Total Credits |
Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | DSC303A | Dissertation/Project (CC) | 12 | 6 | 100 | ||
| 2 | DSE303A | Deep Learning (DSE) (Track 1) | 3 | 2 | 4 | 100 | |
| 3 | DSE304A | Supply-Chain Management (DSE) (Track 2) | 4 | 4 | 100 | ||
| 4 | DSC302A | Big Data (CC) | 4 | 2 | 5 | 100 | |
| 5 | DSO301A | Open Elective | 3 | 3 | 100 | ||
| Total number of contact hours per week | 10/11 | 16/14 | 18 | 400 | |||
| Total number of contact hours per week | 26/25 | ||||||
| Sl. No. |
Code | Course Title | Theory (h/W/S) |
Tutorials (h/W/S) |
Practical (h/W/S) |
Total Credits |
Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | DSE401A | Cloud Computing (DSE) | 3 | 2 | 4 | 100 | |
| 2 | DSE403A | Natural Language Processing (DSE) | 3 | 2 | 4 | 100 | |
| 3 | DSE404A | Applied Econometrics (DSE) | 3 | 2 | 4 | 100 | |
| 4 | DSE405A | Health Care Analytics (DSE) | 3 | 2 | 4 | 100 | |
| 5 | DSO401A | Open Elective | 3 | 3 | 100 | ||
| Total | 12 | 6 | 15 | 400 | |||
| Total number of contact hours perweek | 18 | ||||||
| Sl. No. |
Code | Course Title | Theory (h/W/S) |
Tutorials (h/W/S) |
Practical (h/W/S) |
Total Credits |
Max. Marks |
|---|---|---|---|---|---|---|---|
| 1 | DSI401A | Research Project | 40 | 20 | 400 | ||
| Total | 40 | 20 | 400 | ||||
| Total number of contact hours per week | 40 | ||||||
Eligibility Criteria
- 60% above in 10+2 / 2nd PUC or equivalent examination from a recognized board.
- Minimum aggregate of 45% marks for General/OBC candidates.
- Minimum aggregate of 40% marks for SC/ST candidates.
- Candidates with valid scores in RUAS-AT, CUET-UG, CET, or IIT JEE may be considered as per university norms.
Structure
Fee Structure
| Course | Fee |
|---|---|
| BSc (Hons.) Data Science and Analytics | Rs.2,00,000 (or as per latest approved fee) |
Intake
120 Seats
Career Path
Graduates of the B.Sc. (Hons.) Data Science and Analytics programme can pursue careers as:
Core Data & Analytics Roles
- Data Analyst Analyze data, generate insights, and support decision-making across business functions.
- Data Scientist Build predictive models, perform advanced analytics, and derive strategic insights using statistical and machine learning techniques.
- Business Intelligence Analyst Develop dashboards, reports, and visual analytics to track KPIs and business performance.
- Machine Learning Engineer Design, implement, and optimize machine learning pipelines and intelligent systems.
Technology & Engineering Roles
- Data Engineer
- Database Administrator
- Applications Architect / Data Architect
Business & Research-Oriented Roles
- Market Research Analyst
- Business/Data Consultant
- Operations Research Analyst
Emerging Domain-Specific Roles
- AI/ML Research Assistant
- Data Product Analyst
- Risk & Financial Analyst
Entrepreneurial and Academic Pathways
- Start-up Founder in Tech/Data Domain
- Higher Education & Research (M.Sc., M.Tech, MBA, or PhD)
FAQs
A four‑year honours undergraduate programme focused on data mining, predictive modeling, mathematics, statistics and computer science. It emphasises hands‑on, application‑oriented learning to equip students with analytical, critical thinking and communication skills.
Yes. The course is tailored through interactions with industry and financial institutions, aiming to produce graduates trained in problem-solving, communication and practical skills, qualities sought after in real-world data roles .
Graduates can pursue roles such as Data Scientist, Business Intelligence Analyst, Data Analyst, Machine Learning Engineer, Database Administrator, Market Research Analyst and Applications Architect, indicating broad scope across sectors
Yes. The university’s outcome‑based curriculum, industry‑aligned design and focus on practical exposure via projects and internships aim to prepare students well for placement opportunities .
Absolutely. With rigorous theoretical grounding, applied learning in high-demand tools/techniques, industry engagement and diverse career options, the degree offers strong value for students aiming for data-driven fields .
Contact
Start your journey with MSRUAS
MS Ramaiah University of Applied Sciences
Heritage Building, Gnana Gangothri Campus , New BEL Road, MSR Nagar, Bengaluru – 560054
Contact
Phone 080 4536 6616
Mobile +91 80100 04444
ADDRESS
