Diploma in Data Science for Healthcare
About the
programme
https://techmedbuddy.
Why Choose
School of Biotechnology and Bioinformatics Navi Mumbai
Programme
Objectives
The primary objectives of the Programme are to:
- Provide interdisciplinary knowledge integrating healthcare and data
- Develop proficiency in analytical tools and programming languages used in biomedical data
- Equip students with skills to handle, analyze, and interpret complex healthcare
- Foster an understanding of ethical and regulatory aspects of digital health
- Prepare learners for careers in research, healthtech, pharma, and clinical
Programme
Highlights
Learning
Outcomes
Upon successful completion of the programme, students will be able to:
- Apply statistical, computational, and machine learning techniques to healthcare
- Analyze omics, clinical, and imaging datasets for biomedical
- Design and implement AI-based solutions for healthcare
- Navigate regulatory frameworks and ethical considerations in health data
- Contribute to the development and deployment of digital health technologies and
Who should opt for the
Programme
Undergraduate Students : who have completed a minimum of 2 Years in Medical Sciences, Dental Sciences, Pharmacy, Nursing, Life Sciences, Biomedical Science, Biotechnology,Bioinformatics, Microbiology, Molecular Biology, or any other life sciences-related discipline or in Computer Science or IT
Postgraduate Students : Who are currently pursuing or having completed a Master’s degree in Medical Sciences, Dental Sciences, Pharmacy, Nursing, Life Sciences, Biomedical Science, Biotechnology, Bioinformatics, Microbiology, Molecular Biology, any other life sciences-related discipline or in Computer Science or IT
Researchers : PhD scholars and postdoctoral fellows seeking interdisciplinary exposure
Professionals: Working individuals in healthcare, pharma, research, or tech aiming to upskill in healthcare data
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Duration of
Programme
10 Months
Career
Pathways
Graduates of the programme will be prepared for diverse roles in the following domains:
- Health Data Analyst / Clinical Data Scientist
- Bioinformatics Specialist
- AI/ML Engineer in Healthcare
- Digital Health Product Developer
- Research Associate in Translational Medicine
- Regulatory & Compliance Analyst
- Personalized Medicine Analyst
They may find employment opportunities in hospitals, pharmaceutical companies, digital health startups, research institutes, public health organizations, and government health departments.
Programme Course/Credit
Structure
| Semester I | |||
| Paper Code |
Course |
Hours per week |
Course Type |
| Foundations of Data Science in Healthcare | 4 | PCC | |
| Biomedical Data Ecosystem | 3 | PCC | |
| Applied Machine Learning for Health | 4 | PCC | |
| NLP & LLM for Clinical Applications | 3 | PEC/ELEC | |
| Biomedical Imaging & Computer Vision | 3 | PEC/ELEC | |
| Omics & Genomics Data Science | 3 | PEC/ELEC | |
| Total | 17 hours per week | ||
| Semester II | |||
| Paper Code |
Course |
Hours per week |
Course Type |
| Multi-Modality in Healthcare Applications | 3 | PCC | |
| Generative AI & Innovation in
HealthTech |
3 | PCC | |
| AI for CADD | 3 | PCC | |
| Responsible AI, Ethics & Policy in
Healthcare |
3 | PCC | |
| Model Development, MLOps & Capstone
Project |
3 | PCC | |
| Biomedical Time Series & Sensor Data | 3 | PEC/ELEC | |
| Project I | 3 | PEC/ELEC | |
| Project II | 3 | PEC/ELEC | |
| Total | 15 hours per week | ||
Course Syllabus
Admission
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