Mahindra École Centrale, [MEC] Hyderabad

M.Tech. in Biomedical Data Science 

Program Overview

The Master of Technology (M.Tech.) in Biomedical Data Science at Mahindra University is a two-year postgraduate program that combines principles of data science, computing, statistics, and biomedical engineering. It focuses on analyzing and interpreting complex biological and medical data using advanced computational and machine learning techniques. The program prepares students for careers in healthcare analytics, biomedical research, medical imaging, bioinformatics, and related fields.

Program Duration

• 2 years (4 semesters)

Eligibility Criteria

To be eligible for admission to the M.Tech. in Biomedical Data Science, candidates must meet the following requirements:

• Bachelor’s degree in Engineering/Technology, Sciences, or allied fields, typically in:

  • Computer Science Engineering

  • Electrical/Electronics Engineering

  • Biomedical Engineering

  • Biotechnology

  • Mathematics, Statistics, or related disciplines

• Minimum 60% aggregate marks or equivalent grade in the qualifying degree

• Final-year undergraduate students may apply, subject to fulfilling eligibility criteria at the time of admission

Admission Process

Admission to the program is usually based on:

• Valid GATE score followed by a personal interview, or

• University-conducted written test and interview for candidates without a valid GATE score

Shortlisted candidates are selected based on academic performance, entrance score (if applicable), and interview results.

Curriculum Structure

The curriculum is designed to provide a strong foundation in data science methods and their applications in biomedical contexts.

Core Areas of Study

Students typically study subjects such as:

• Biomedical Data Analytics

• Machine Learning and Deep Learning

• Bioinformatics and Computational Biology

• Medical Image Processing

• Statistical Methods for Biomedical Research

• Big Data Technologies and Databases

• Biostatistics

• Neural Networks and Pattern Recognition

• Signal Processing for Biomedical Systems

Tools and Technologies

Students gain hands-on experience with data science tools and platforms such as:

• Python/R for data analysis

• Machine learning libraries (TensorFlow, PyTorch, scikit-learn)

• SQL and NoSQL databases

• Software for bioinformatics and medical image analysis

Practical Training

• Laboratory assignments involving real biomedical datasets

• Projects based on healthcare analytics, genetic data, or diagnostic imaging

• Case studies from clinical and biomedical research settings

Project and Thesis

• Major project or research thesis in the final semester

• Opportunity for industry-linked or research-focused project work involving real data and practical applications

Learning Outcomes

Graduates of the M.Tech. in Biomedical Data Science program will be able to:

• Apply advanced data science techniques to analyze and interpret biomedical data

• Build predictive models using machine learning and deep learning methods

• Process and extract insights from medical imaging and genomic datasets

• Understand and implement statistical methods relevant to biomedical research

• Work across interdisciplinary teams bridging data science and healthcare

Career Opportunities

After completing this program, graduates can pursue careers such as:

• Biomedical Data Scientist

• Healthcare Data Analyst

• Machine Learning Engineer for Healthcare

• Bioinformatics Specialist

• Medical Imaging Analyst

• Clinical Data Scientist

• AI Researcher in Healthcare Technology

• Biostatistician

• R&D Engineer in Biomedical and Healthcare Firms

Employment opportunities exist in hospitals, healthcare analytics companies, biomedical research laboratories, pharmaceutical firms, medical imaging companies, public health organizations, and academic research institutions.

Academic Environment

The program emphasizes:

• Integration of data science, healthcare, and computational techniques

• Hands-on learning with real biomedical datasets and tools

• Research exposure, case studies, and project-based learning

• Faculty guidance with expertise in data science, biomedical computing, and analytics