Program Review
The Bachelor of Science (B.Sc.) in Data Science at Atharva University, Mumbai is a comprehensive and future-ready undergraduate program designed to develop highly skilled professionals capable of transforming data into actionable intelligence. The program offers a strong interdisciplinary foundation by integrating mathematics, statistics, computer science, data engineering, and artificial intelligence, enabling students to solve complex real-world problems through data-driven approaches.
In an era where organizations rely on big data, predictive analytics, automation, and AI-enabled decision systems, this program emphasizes both theoretical rigor and extensive practical exposure. Students work with real-world datasets, industry-oriented case studies, advanced analytics laboratories, and capstone projects, ensuring readiness for professional roles and higher academic pursuits. The curriculum is continuously updated to align with emerging technologies, industry practices, and global standards.
Duration & Eligibility
Requirements and selection process for the B.Sc. Data Science Program
Duration
3 Years
Eligibility
Candidate should have passed Std XII examination from any recognized board with minimum 45% marks (40% for reserved category)
Selection Process
Valid Score of JEE Main 2026 / MH CET 2026/ PERA CET 2026/ CUET 2026 / Atharva University All India Entrance Test and Personal Interview
Program Highlights
Industry-driven and future-oriented
curriculum aligned with current trends in analytics, machine learning, and AI
In-depth training
in Python, R, SQL, Power BI, TensorFlow, Hadoop, cloud platforms, and advanced Data Visualization tools
Strong conceptual foundation
in Statistics, Linear Algebra, Programming Paradigms, Database Management Systems, and Data Engineering
Experiential learning
through hands-on laboratories, live datasets, industry case studies, hackathons, and analytics challenges
Exposure to
Big Data Technologies, Cloud Computing Environments, and AI-enabled Analytical Frameworks
Regular Expert Talks
, Workshops, and Mentorship sessions by data scientists, industry leaders, and academic researchers
Structured Internships
, Mini-Projects,  Project Day – Every Friday (Hands -on – Learning & Portfolio Building) and a final-year Capstone Project to bridge academia and industry
Access to
state-of-the-art analytics labs and computational resources ( AR/VR Lab, Robotic Lab, Simulation Lab, Innovation Labs and Technology Incubation Centre
Atharva Cutting-Edge Labs:
Data Science Laboratory
Programming & Statistical Computing Lab
Machine Learning & AI Lab
Big Data Analytics Lab
Data Visualization & Business Intelligence Lab
Advance AR/ VR Lab
Innovation Lab
IOT Lab
Robotcs Center
3 D Printing Lab
Career Support & Opportunities
Graduates of the B.Sc. Data Science program are well-equipped to pursue dynamic and high-growth career roles across multiple sectors, including:
Employment opportunities exist in IT and software services, consulting firms, banking and financial institutions, e-commerce and retail organizations, healthcare and life sciences, marketing and digital media agencies, manufacturing industries, and government analytics departments. Graduates may also opt for postgraduate studies and research in Data Science, Artificial Intelligence, Analytics, Computer Science, or Management.
- Data Analyst
- Business Analyst
- Associate / Junior Data Scientist
- Machine Learning Engineer
- Big Data Engineer
- Research Analyst
- AI & Analytics Associate
Program Outcomes
Upon successful completion of the program, graduates will be able to:
- Apply statistical, mathematical, and computational techniques to analyse and interpret structured and unstructured datasets
- Design, develop, and evaluate data-driven models and predictive analytics solutions for real-world applications
- Demonstrate proficiency in programming, data manipulation, visualization, and machine learning techniques
- Utilize modern analytics tools, platforms, and frameworks for efficient data processing and decision support
- Understand and practice ethical, legal, and responsible use of data, including data privacy and governance principles
- Collaborate effectively in multidisciplinary teams and manage analytical projects independently
- Communicate complex analytical findings clearly and professionally to both technical and non-technical stakeholders
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