M.Tech. in Biomedical Data Science

What is Biomedical Data Science?

Biomedical Data Science spans a range of biological and medical research challenges that are data intensive and focus on the creation of novel methodologies to advance biomedical science discovery. The term “Data Science” describes the expertise associated with taking (usually large) data sets and annotating, cleaning, organizing, storing, and analyzing them to extract knowledge. It merges the disciplines of Statistics, Computer science, and Computational engineering” (Annual Review of Biomedical Data Science).

The past decade has seen major advances in the individuals ability to acquire data on human health across multiple spatiotemporal scales. This wealth of data poses challenges that have never before been confronted. At the heart of these is understanding how massive biomedical data sets are best analyzed to discover new knowledge about the function of living systems in health and disease, and how this knowledge can be harnessed to provide improved, more affordable health care (Johns Hopkins University).

The M.Tech program in Biomedical Data Science provides an educational curriculum that will train students on solving complex problems by analysing massive and diverse biomedical data from diverse sources. We are creating a common research and teaching space where students and faculty from the Centre for Life Sciences work together to develop novel technologies and data analysis methods that are needed to improve the individuals ability to diagnose and treat diseases more effectively while reducing costs.

Why an M.Tech in Biomedical Data Science is required?

The vast amount of biomedical data being generated today has created a tremendous need for highly skilled data scientists who can use this information to advance care. (Xinxin (Katie) Zhu, MD, PhD, executive director of the Yale Center for Biomedical Data Science)

The postgraduate program (MS program) in Biomedical Data Science is already being offered by top universities around the world, however, there is no such program offered by any Indian University yet. Therefore, this would be the first such program to be offered by any Indian University.

 There has been a great increase in the amount of biomedical data over the past decade. Along with the expanding application of large-scale genomic sequencing, other modalities such as mobile health (mHealth) data and imaging have added to the rise. At the same time, computing power and storage capacity have continued to increase, allowing us to mine and model biomedical data with unprecedented ability. Together, these trends have given rise to the new field of Biomedical Data Science. Computations in biomedical data science range from simple statistical associations to complex machine learning models, to simulations of molecular, cellular, and organismic systems. From basic biological research to clinical investigation, the need for the tools of biomedical data science is ever-expanding. The Centre for Life Sciences taps the broad expertise of researchers of the Centre to make this program successful. The Centre for Life Sciences further aims to enhance research in the broad area of biomedical data science in the coming years. The Centre for Life Sciences offers this program with the goal of helping to train students to become the next generation of biomedical research pioneers who are well-trained for job opportunities in the industry and academia.

Organizations that would be interested in hiring M.Tech. Biomedical Data Science graduates ?
Core Research Companies* Services Companies* ITES*
  • Eli Lilly
  • Astra Zeneca
  • Takeda
  • Pfizer
  • Merck
  • Johnson & Johnson
  • Novartis
  • Corteva Agrisciences
  • Sanofi
  • Bristol Myers Squibb
  • Novotech
  • GSK
  • Novo Nordisk
  • Abbott
  • Siemens Healthineers
  • Boehringer Ingelheim
  • Clarivate
  • Elucidata
  • Nimble Clinical Research
  • IQVIA
  • Evalueserve
  • LabCorp
  • Quantium
  • WNS
  • Cardinal Health
  • Syneos Health
  • US Pharmacopeia
  • Axtria
  • Ingenious Insights
  • Caidya
  • Providence India
  • Northwell Health
  • Tech Mahindra
  • Wipro
  • TCS
  • Infosys
  • Cognizant
  • Persistent
  • Accenture
  • HCL Tech

*Non-exhaustive

Qualifying Degree

B.E./ B.Tech./ B.Sc. (Engineering) / B.Sc. (4-year program) / M.Sc. / M.C.A. / MBBS / BDS / B.Pharm./ B.V.Sc.

Marks / CGPA / CPI in Qualifying degree

A minimum of 60% marks in aggregate, OR (2) a First Class as specified by the University, OR (3) a minimum Cumulative Grade Point Average (CGPA) / Cumulative Performance Index (CPI) of 6.0 on the scale of 0-10; OR (4) an equivalent to 6.0 on other corresponding proportional requirements when the scales are other than 0-10.

Course Structure: Total Credits – 62
Semester Course Credits
Ist Mathematics for Biomedical Science (for Biosciences background)
Or
Introduction to Biological Sciences (for Mathematics background)
3
Biostatistics for Biomedical Science 3
Python Programming 3
Linux Workshop 1
Introduction to Databases & SQL 3
Research Seminar in Biomedical Data Science 2
Total: 15
IInd Machine Learning with Python 4
NGS & Genome Data Analysis 3
Clinical Trial Data Analysis 3
Computational Drug Discovery 3
Systems Biology & Network Modelling 3
Total: 16
IIIrd Biomedical Image Analysis 3
Elective: Group-1 3
Elective: Group-2 3
Mini Research Project 6
Total: 15
IVth Major Research Project 16
Total: 16
Total Credits – 62
Tentative Electives:
Group-1 Group-2
  1. Precision Care Medicine
  2. Clinical Decision Support & Workflow
  3. Epidemiology & Public Health
  4. Clinical Informatics
  5. Computational Mass spectrometry & Proteomics
  6. Single Cell Gene Expression Analysis
  7. Pharmacogenomics
  8. AI in Healthcare
  1. Biomedical & Omics Data Integration
  2. Algorithms in Computational Biology
  3. Neuroimaging Data Analysis
  4. Introduction to Medical Software
  5. Machine Learning in Drug Discovery
  6. Deep Learning in Biomedical Science
  7. R programming
  8. Infectious Diseases Modeling