Master of Science in Health Data Science

The rapid digital transformation of healthcare has fundamentally changed the way health data is generated, managed, analysed, and translated into evidence for clinical practice, public health, and healthcare policy. Advances in electronic health records, medical imaging, wearable technologies, genomics, biomedical sensors, and artificial intelligence have created unprecedented opportunities to improve disease prevention, diagnosis, treatment, and population health. These developments have also increased the demand for skilled professionals with strong analytical and computational capabilities, as well as a deep understanding of health systems, epidemiology, and healthcare organisations.

The Master of Science in Health Data Science at Bagchi School of Public Health addresses this emerging need. It prepares professionals who can bridge health sciences with statistics, computing, and data-driven analytical methods.

Health Data Science is an inherently interdisciplinary field that brings together expertise from public health, epidemiology, biostatistics, computer science, artificial intelligence, biomedical engineering, medicine, and health informatics. The programme enables students to integrate knowledge from these disciplines within a unified analytical framework to address complex clinical, public health, and biomedical challenges.

The programme aligns with national initiatives such as the Ayushman Bharat Digital Mission while addressing the growing international demand for professionals who can apply advanced analytical methods across healthcare, research, pharmaceutical and biotechnology industries, digital health organisations, government agencies, and international public health institutions.

The programme will commence in the Monsoon Semester of 2027. It aims to prepare graduates pursuing careers in healthcare, biomedical research, digital health, and health technology sectors.

Programme Objectives

Provide strong foundational grounding in public health, health systems, statistics, and programming, particularly for students from diverse academic backgrounds.
Deliver rigorous training in epidemiological methods, statistical methods, bias assessment, and evidence-based health data analysis.
Equip students with knowledge and practical skills in artificial intelligence, deep learning, biomedical signal processing, clinical natural language processing, computer vision, genomics, and multimodal health data analytics.
Enable students to apply advanced analytical and computational methods to real-world healthcare and public health challenges, including electronic health records, medical imaging, wearable devices, precision medicine, and population health.
Promote interdisciplinary thinking through the integration of public health, epidemiology, artificial intelligence, health informatics, biomedical sciences, and responsible data-driven decision making.
Provide structured opportunities for experiential learning through industry projects, collaborative research, and a mandatory one-year industry internship.
Prepare graduates for careers in healthcare analytics, digital health, pharmaceutical and biotechnology industries, hospitals, public health organisations, research institutions, and advanced doctoral training in Health Data Science and related disciplines.

Programme Must Knows

Minimum Programme Credits: 80

DOWNLOAD BROCHURE

Apply Now

Curriculum Structure

80 credits

Programme Requirements	Credits
Term Zero	4
Basic Mathematics (PBH501)	1
Health Science Fundamentals (PBH502)	1
Introduction to Programming (PBH503)	1
Professional Communication (PBH504)	1
Programme Core	33
Quarter I: Foundations	9
Foundations of Public Health (PBH505)	1.5
Applied Statistics I (STA501)	1.5
Principles of Epidemiology I (EPI611)	1.5
Environmental Health (EPI610)	1.5
Health Data Systems and Computing (HDS542)	1.5
LLM Prompt Engineering (HDS540)	1.5
Quarter II: Core Analytics and Infrastructure	9
Biomedical Signal Analysis (HDS550)	1.5
Applied Statistics II (STA501)	1.5
Principles of Epidemiology II (EPI611)	1.5
Healthcare Data Engineering and Cloud Infrastructure (HDS640)	1.5
Machine Learning for Public Health I (HDS541)	1.5
Data Science for Health (EPI706)	1.5
Quarter III: Advanced AI and Analytics	7.5
Deep Learning Foundations for Healthcare (HDS642)	1.5
Epidemiological Data Analysis I (EPI703)	1.5
Biomedical Analytics and AI Applications I (HDS551)	1.5
Machine Learning for Public Health II (HDS641)	1.5
Responsible AI, Ethics and Governance in Healthcare (HDS553)	1.5
Quarter IV: Specialised AI and Applications	7.5
Clinical Natural Language Processing and Generative AI (HDS651)	1.5
Computer Vision and Medical Imaging (HDS652)	1.5
Epidemiological Data Analysis II (EPI703)	1.5
Biomedical Analytics and AI Applications II (HDS650)	1.5
Genomics and Computational Biology (HDS654)	1.5
Programme Electives	3
Quarter III: Advanced AI and Analytics	1.5
Quarter IV: Specialised AI and Applications	1.5
Industry Seminar (Year 1)	Required
Industry Internship/ Thesis (Year 2)	40

Eligibility

Applicants must hold a recognised Bachelor's degree or an equivalent professional qualification, with a minimum of 55 per cent marks or an equivalent CGPA. Relaxation, where applicable, will be provided in accordance with Ahmedabad University regulations and statutory norms.
Applicants should demonstrate aptitude for quantitative reasoning, computational thinking, and scientific problem-solving. Prior knowledge of elementary mathematics and basic programming or computing is desirable.
Applicants from the following academic disciplines, but not limited to, are encouraged to apply: Statistics, Biostatistics, Mathematics, Computer Science, Computer Science and Engineering, Computer Applications, Information Technology, Artificial Intelligence and Data Science, Engineering disciplines with a Quantitative Computing, Electronics, Instrumentation, Biomedical, or Data Science Orientation, Biology, Bioinformatics, Computational Biology, Biomedical Engineering, Health Informatics, Public Health, and other quantitative and other allied disciplines with strong analytical aptitude.

Application and Admission Process

Eligible candidates must submit the completed online application form and pay a non-refundable application fee of INR 700.
Applicants may submit any one of:
- CUET-PG 2027, GATE (2025 to 2027), IIT JAM (2026 and 2027), GRE (2025 to 2027)
- Applicants without a sufficient score in any of the above need to appear for the Ahmedabad University Graduate Aptitude Test (Health Data Science).
Shortlisted candidates will be required to appear for an in-person Written Examination. Exceptions to in-person attendance may be considered for international students or candidates who are unable to comply due to valid medical or personal circumstances.
Candidates who qualify in the Written Examination will be invited for a Personal Interview.

Faculty

The programme is delivered by distinguished faculty members at the Bagchi School of Public Health alongside interdisciplinary experts across computational and statistical domains at Ahmedabad University, as well as industry experts.

Learning Outcomes

By the end of the programme, graduates will be able to:

Formulate health and biomedical research questions and design data-driven analytical solutions grounded in scientific evidence.
Design reproducible data analysis workflows and apply appropriate epidemiological, statistical, and machine learning methods to health research problems.
Acquire, manage, integrate, analyse, and interpret diverse health data, including electronic health records, medical imaging, biomedical signals, clinical text, wearable sensor data, and population health databases.
Apply statistical modelling, predictive analytics, machine learning, deep learning, and quantitative methods to complex health datasets.
Develop, validate, interpret, and responsibly deploy AI models for healthcare and public health while considering ethics, privacy, fairness, and governance.
Work effectively in interdisciplinary teams and communicate data-driven findings to diverse stakeholders.