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Aster Health Academy > Technology > Post Graduate Diploma in Data Science & Analytics for Healthcare Professionals

Post Graduate Diploma in Data Science & Analytics for Healthcare Professionals

Transform your career with analytics built for healthcare professionals.

Course Outcomes:

  • Foundational Principles of Healthcare Data Science
  • Apply Core Statistical & Analytical Methods
  • Leverage Python for Data Operations
  • Build Predictive & Diagnostic Models
  • Create Dynamic Visual Dashboards
  • Understand Ethics & Compliance in Health Data
  • Explore Quantum Computing in Medicine
  • Develop Real-World Healthcare Solutions
  • Prepare for Career Advancement

About the course:

The healthcare sector is undergoing a digital revolution driven by data. This comprehensive program bridges the gap between clinical practice and advanced data analytics, equipping healthcare professionals, life science graduates, and technical experts with the tools to make data-driven decisions that improve patient care, operational efficiency, and research outcomes.

 

Spanning 140 hours across 14 structured modules, the course integrates real-world healthcare datasets, hands-on projects, and industry use cases. From data preprocessing and visualization to building predictive models and interactive dashboards, participants will learn to translate raw data into actionable insights.

 

The curriculum also emphasizes ethical handling of sensitive health data and introduces emerging areas like quantum computing and AI applications in healthcare analytics. With step-by-step access, module-wise assessments, and a capstone project, learners graduate job-ready for roles in health analytics, hospital IT, research, and public health intelligence. Plus, students receive 3 assured interview opportunities to help kickstart their careers in the healthcare analytics domain.

Curriculum:


Fundamentals of Data Science

1
Data Collection and Sources in Healthcare
2
Data Types and Structures: Structured vs. Unstructured Data
3
Introduction to Statistics for Data Science
4
Basics of Data Cleaning, Transformation, and Preprocessing

Core Data Science Applications in Healthcare

1
Predictive Modeling for Disease Risk and Patient Outcomes
2
Classification Techniques for Diagnostics

Data Manipulation and Visualization with Python

1
Introduction to Python Libraries: NumPy, Pandas, Matplotlib, and Seaborn
2
Working with Pandas for Data Cleaning and Analysis
3
Visualizing Data Trends with Matplotlib and Seaborn
4
DIY Project: Analyzing Patient Data with Python

Advanced Python for Healthcare Applications

1
Functions and Modules in Python
2
File Handling for Large Datasets
3
Introduction to Working with APIs for Healthcare Data Retrieval
4
Case Study: Fetchine and Analyzing Healthcare Data from an API

Core Concepts of Data Science

1
Data Science Workflow: From Collection to Insights
2
Exploratory Data Analysis (EDA) Identifying Trends and Patterns in Healthcare Data Tools for Summarizing Data
3
DIY Project: Conducting EDA on Hospital Readmissions Data

Predictive Analytics in Healthcare

1
Overview of Predictive Analytics
2
Regression Models: Linear Regression for Outcome Prediction
3
Multiple Regression Analysis
4
Case Study Predieting asetypeddieting Patient Recovery Time Using Regression Models

Digital Transformation in Healthcare

1
Role of Data Science in Digitizing Healthcare Systems
2
Telemedicine and Remote Monitoring Applications
3
Health Informatics and Decision Support Systems
4
DIY Project: Designing a Simple Healthcare Operations Dashboard

Introduction to Healthcare Data Ethics

1
Privacy and Security Challenges in Healthcare Data
2
Ethical Considerations in Al and Predictive Models
3
Regulations and Standards (HIPAA, GDPR, FDA Guidelines)
4
Case Study: Ethical Challenges in Al-Based Diagnostics

Introduction to Data Visualization Techniques

1
Storytelling with Data in Healthcare
2
Advanced Chart Types: Heatmaps, Treemaps, and Boxplots
3
Best Practices for Creating Eective Visualizations
4
DIY Project: Building an Interactive Patient Data Dashboard

Quantum Computing in Healthcare

1
Basics of Quantum Computing: Simplified Introduction
2
Applications in Drug Discovery and Genomic Analysis
3
Challenges and Future Potential in Healthcare
4
Discussion: How Quantum Could Revolutionize Medical Research

Practical Applications of Data Science in Healthcare

1
End-to-End Healthcare Case Studies: Hospital Readmission Analysis
2
Predicting Patient Length of Stay Disease Outbreak Prediction Models

DIY Projects for Hands-On Learning

1
Building a Predictive Model for Diabetes Risk
2
Segmenting Patients for Population Health Management
3
Creating an EHR Data Dashboard

Capstone Project

1
Goal: Develop a Data Science Solution for a Real Healthcare Challenge
2
Examples: Chronic Disease Management Dashboard Predictive Analytics for Surgery Outcomes
3
Peer Feedback and Evaluation

Future Trends and Career Opportunities

1
Emerging Trends: Al, Quantum Computing, and Advanced Analytics
2
Career Pathways in Data Science for Healthcare Professionals
3
Resources for Continuous Learning and Networking

Who this course is for:

  • Healthcare Professionals seeking digital upskilling.
  • Medicine, Nursing  or Pharma Candidates/Professionals exploring industry roles.
  • IT or Data Enthusiasts looking to transition into healthcare analytics or AI.
  • Researchers and Academicians aiming for interdisciplinary application.
  • Students preparing for regulatory, clinical, or pharmacovigilance roles.

 

Eligibility –

  • Minimum qualification: Graduate degree in life sciences, medicine, pharmacy, biotechnology, nursing, allied health sciences, or computer science.
  • Final-year students and working professionals from healthcare, IT, or pharma sectors can apply.
  • Good understanding of English and basic digital literacy is required

Sample Certificate:

FAQ:

This course is ideal for: • Healthcare professionals (doctors, nurses, health admins) • Graduates in life sciences, pharmacy, biotechnology, or public health • IT and data professionals interested in transitioning to healthcare analytics • Students aspiring for a career in digital healthcare, research, or clinical analytics
You must have a minimum of a graduate degree in life sciences, medical sciences, pharmacy, biotechnology, or related fields. Basic knowledge of computers and English is required. Final-year students and working professionals are also eligible.
No prior programming experience is necessary. The course includes foundational training in Python and data handling, starting from basics and gradually building up to advanced analytics and model building.
You will learn how to: • Clean, process, and visualize real healthcare data • Build predictive models for disease detection and treatment outcomes • Use Python, Pandas, Seaborn, and other tools • Create interactive dashboards and data storytelling • Apply data ethics and privacy principles • Understand future technologies like AI and quantum computing in healthcare
Upon successful completion of all modules, MCQ tests, projects, and the capstone dissertation, you will receive a Post Graduate Diploma Certificate jointly certified by NEMI Powered by Aster Health Academy.
The program is delivered online through: • Live expert sessions (scheduled weekly) • Platform access to recorded videos, eBooks, MCQs, and projects • Step-by-step module unlocking after each assessment • Hands-on DIY projects and capstone work
Yes. This course is aligned with job roles such as: • Healthcare Data Analyst • Clinical Data Manager • Predictive Modeler • Public Health Analyst • Medical Informatics Officer Career guidance and interview prep are also provided during and after the course.
You should plan for 6–8 hours per week, which includes live sessions, practice, project work, and self-study. The total duration is 140 hours, and you can pace it flexibly over a few months.
Pre recorded videos, ebooks and module overview videos will be uploaded to the Platform. You can access them and complete the related assignments at your own pace.
The total fee is ₹1,50,000 + 18% GST. This includes all live sessions, study materials, LMS access, assessments, and certification. No additional charges apply.
+ GST as applicable

Academic Team

Dr. Hemant
Alumni IIT Guwahati | Expert in Data Science & Blockchain
Aradhana Maurya
Health care Educator| PhD in Pharmaceutical Sciences
Dr. Mahendra Dwivedi
Head of Course Formulations & AI Expert
Dr. Piyush Mittal
Pharmacologist | Expert in Life Science & Technology
Dr. Shalendra Kumar Yadav
Pharmacovigilance & Clinical Research Expert

Curriculum:


Fundamentals of Data Science

1
Data Collection and Sources in Healthcare
2
Data Types and Structures: Structured vs. Unstructured Data
3
Introduction to Statistics for Data Science
4
Basics of Data Cleaning, Transformation, and Preprocessing

Core Data Science Applications in Healthcare

1
Predictive Modeling for Disease Risk and Patient Outcomes
2
Classification Techniques for Diagnostics

Data Manipulation and Visualization with Python

1
Introduction to Python Libraries: NumPy, Pandas, Matplotlib, and Seaborn
2
Working with Pandas for Data Cleaning and Analysis
3
Visualizing Data Trends with Matplotlib and Seaborn
4
DIY Project: Analyzing Patient Data with Python

Advanced Python for Healthcare Applications

1
Functions and Modules in Python
2
File Handling for Large Datasets
3
Introduction to Working with APIs for Healthcare Data Retrieval
4
Case Study: Fetchine and Analyzing Healthcare Data from an API

Core Concepts of Data Science

1
Data Science Workflow: From Collection to Insights
2
Exploratory Data Analysis (EDA) Identifying Trends and Patterns in Healthcare Data Tools for Summarizing Data
3
DIY Project: Conducting EDA on Hospital Readmissions Data

Predictive Analytics in Healthcare

1
Overview of Predictive Analytics
2
Regression Models: Linear Regression for Outcome Prediction
3
Multiple Regression Analysis
4
Case Study Predieting asetypeddieting Patient Recovery Time Using Regression Models

Digital Transformation in Healthcare

1
Role of Data Science in Digitizing Healthcare Systems
2
Telemedicine and Remote Monitoring Applications
3
Health Informatics and Decision Support Systems
4
DIY Project: Designing a Simple Healthcare Operations Dashboard

Introduction to Healthcare Data Ethics

1
Privacy and Security Challenges in Healthcare Data
2
Ethical Considerations in Al and Predictive Models
3
Regulations and Standards (HIPAA, GDPR, FDA Guidelines)
4
Case Study: Ethical Challenges in Al-Based Diagnostics

Introduction to Data Visualization Techniques

1
Storytelling with Data in Healthcare
2
Advanced Chart Types: Heatmaps, Treemaps, and Boxplots
3
Best Practices for Creating Eective Visualizations
4
DIY Project: Building an Interactive Patient Data Dashboard

Quantum Computing in Healthcare

1
Basics of Quantum Computing: Simplified Introduction
2
Applications in Drug Discovery and Genomic Analysis
3
Challenges and Future Potential in Healthcare
4
Discussion: How Quantum Could Revolutionize Medical Research

Practical Applications of Data Science in Healthcare

1
End-to-End Healthcare Case Studies: Hospital Readmission Analysis
2
Predicting Patient Length of Stay Disease Outbreak Prediction Models

DIY Projects for Hands-On Learning

1
Building a Predictive Model for Diabetes Risk
2
Segmenting Patients for Population Health Management
3
Creating an EHR Data Dashboard

Capstone Project

1
Goal: Develop a Data Science Solution for a Real Healthcare Challenge
2
Examples: Chronic Disease Management Dashboard Predictive Analytics for Surgery Outcomes
3
Peer Feedback and Evaluation

Future Trends and Career Opportunities

1
Emerging Trends: Al, Quantum Computing, and Advanced Analytics
2
Career Pathways in Data Science for Healthcare Professionals
3
Resources for Continuous Learning and Networking

FAQ:

This course is ideal for: • Healthcare professionals (doctors, nurses, health admins) • Graduates in life sciences, pharmacy, biotechnology, or public health • IT and data professionals interested in transitioning to healthcare analytics • Students aspiring for a career in digital healthcare, research, or clinical analytics
You must have a minimum of a graduate degree in life sciences, medical sciences, pharmacy, biotechnology, or related fields. Basic knowledge of computers and English is required. Final-year students and working professionals are also eligible.
No prior programming experience is necessary. The course includes foundational training in Python and data handling, starting from basics and gradually building up to advanced analytics and model building.
You will learn how to: • Clean, process, and visualize real healthcare data • Build predictive models for disease detection and treatment outcomes • Use Python, Pandas, Seaborn, and other tools • Create interactive dashboards and data storytelling • Apply data ethics and privacy principles • Understand future technologies like AI and quantum computing in healthcare
Upon successful completion of all modules, MCQ tests, projects, and the capstone dissertation, you will receive a Post Graduate Diploma Certificate jointly certified by NEMI Powered by Aster Health Academy.
The program is delivered online through: • Live expert sessions (scheduled weekly) • Platform access to recorded videos, eBooks, MCQs, and projects • Step-by-step module unlocking after each assessment • Hands-on DIY projects and capstone work
Yes. This course is aligned with job roles such as: • Healthcare Data Analyst • Clinical Data Manager • Predictive Modeler • Public Health Analyst • Medical Informatics Officer Career guidance and interview prep are also provided during and after the course.
You should plan for 6–8 hours per week, which includes live sessions, practice, project work, and self-study. The total duration is 140 hours, and you can pace it flexibly over a few months.
Pre recorded videos, ebooks and module overview videos will be uploaded to the Platform. You can access them and complete the related assignments at your own pace.
The total fee is ₹1,50,000 + 18% GST. This includes all live sessions, study materials, LMS access, assessments, and certification. No additional charges apply.

Duration: 140 hrs

Level: advanced

Price:
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    Post Graduate Diploma in Data Science & Analytics for Healthcare Professionals
    Price:
    INR 150,000
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