B.Sc. (Data Science) Healthcare Analytics Practical Syllabus - Mumbai University

This is the TY BSc Data Science syllabus under NEP 2020, in force from the academic year 2026-27. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.

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Syllabus for Healthcare Analytics Practical

Major elective · Semester 5 · TY BSc Data Science · 2 credits · 50 marks

Module I: Foundations of Healthcare Analytics & Machine Learning

Applications Descriptive & Predictive Healthcare Analytics 1. Implementation of Descriptive Analytics on Electronic Health Record (EHR) Dataset 2. Development of a Predictive Model for ICU Admission Risk 3. Comparative Study of Descriptive vs Predictive Models in Healthcare Disease Prediction using Machine Learning 4. Diabetes Prediction using Classification Algorithms 5. Overweight Status Prediction using Supervised Learning Models 6. Patient Readmission Prediction using Logistic Regression Medical Imaging & Deep Learning Applications 7. Pneumonia Detection using X-ray Image Classification 8. COVID-19 Early Detection using Convolutional Neural Networks 9. Deep Learning Model for Endodontic Disease Diagnosis Group 4: Healthcare Monitoring & Smart Devices 10. Human Activity Recognition using Neural Networks 11. Smartwatch Health Data Analysis for Senior Citizens 12. Telemedicine Dataset Analysis for Remote Patient Monitoring Healthcare Data Insights & Statistical Modeling 13. Correlation Analysis between Patient Attributes and Disease Outcomes 14. Waiting Time Impact Analysis on Patient Satisfaction 15. Exploratory Data Analysis (EDA) on Large Healthcare Dataset

Module II: Advanced Computational Intelligence & Smart Healthcare Systems

Computational Intelligence Techniques 16. Fuzzy Logic-based Medical Decision Support System 17. Genetic Algorithm for Healthcare Resource Optimization 18. Swarm Intelligence for Hospital Scheduling Optimization Neural Networks & Hybrid Intelligence Models 19. Artificial Neural Network for Disease Risk Prediction 20. Hybrid AI Model (Fuzzy + Neural Network) for Diagnosis 21. Predictive Maintenance Model for Healthcare Equipment Big Data & EHR Analytics 22. AI Integration with Electronic Health Records (EHR Data Modeling) 23. Big Data Processing of Structured & Unstructured Healthcare Data 24. Development of Healthcare Analytics Dashboard with Visualization Healthcare Risk & Monitoring Systems 25. Real-Time Patient Vital Monitoring Simulation 26. ICU Risk Alert System 27. Sepsis Early Warning Prediction Model Healthcare Data Security & Reliability 28. Secure Medical Record System using Hashing 29. Anomaly Detection in Patient Records 30. Failure Prediction using Healthcare Equipment Dataset 10 Text Books 1. Big Data Analytics and Intelligent Applications for Smart and Secure Healthcare Services, edited by Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir, and Debabrata Samanta, published by CRC Press (Taylor & Francis Group), 2025, First Edition. 2. Data-Driven Analytics for Healthcare: Artificial Intelligence and Machine Learning for Medical Diagnostics, edited by Meghna Sharma, Priyanka Vashisht, A. V. Senthil Kumar, Chitra Singh, and Abdelmalek Amine, published by Apple Academic Press (CRC Press), 2025, First Edition. 3. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, by Eric Siegel, published by Wiley, 2016, Revised and Updated Edition. 4. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, by Hui Yang and Eva K. Lee, published by John Wiley & Sons, 2016, First Edition. 5. Practical Healthcare Analytics: Leveraging Data for Improved Outcomes, by Jason Burke, published by Productivity Press (CRC Press), 2013, First Edition. 11 Reference Books 1. Health Informatics: Practical Guide for Healthcare and Information Technology Professionals, by Robert E. Hoyt and Ann Yoshihashi, published by Lulu Press, 2021, Seventh Edition. 2. Healthcare Data Analytics, edited by Chandan K. Reddy and Charu C. Aggarwal, published by Chapman & Hall/CRC Press, 2015, First Edition. 3. Big Data in Healthcare: Statistical Analysis and Machine Learning Applications, by Farrokh Alemi and David A. Talley, published by CRC Press, 2018, First Edition. 4. Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes, by Arjun Panesar, published by Apress (Springer Nature), 2019, First Edition. 5. Artificial Intelligence in Healthcare, edited by Adam Bohr and Kaveh Memarzadeh, published by Academic Press (Elsevier), 2020, First Edition. 12 Internal Continuous Assessment: 40% Semester End Examination: 60% 13 Continuous Evaluation through: 30 marks Semester End Students are expected to attend each practical and Examination submit the written practical of the previous session. Performing Practical and writeup submission will be continuous internal evaluation. 2.5 marks can be awarded for each practical performance and writeup submission totaling to 50 marks and can be converted to 20 marks. Total: 20 marks 14 Format of Question Paper: (Semester End Examination: 30 Marks. Duration: 1 Hr.) Q1 Practical based on Module 1 – 13 Marks Q2 Practical based on Module 2 – 12 Marks Q3 Journal and Viva - 05 marks

Text Books

1 Big Data Analytics and Intelligent Applications for Smart and Secure Healthcare Services, edited by Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir, and Debabrata Samanta, published by CRC Press (Taylor & Francis Group), 2025, First Edition.
2 Data-Driven Analytics for Healthcare: Artificial Intelligence and Machine Learning for Medical Diagnostics, edited by Meghna Sharma, Priyanka Vashisht, A. V. Senthil Kumar, Chitra Singh, and Abdelmalek Amine, published by Apple Academic Press (CRC Press), 2025, First Edition.
3 Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, by Eric Siegel, published by Wiley, 2016, Revised and Updated Edition.
4 Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, by Hui Yang and Eva K. Lee, published by John Wiley & Sons, 2016, First Edition.
5 Practical Healthcare Analytics: Leveraging Data for Improved Outcomes, by Jason Burke, published by Productivity Press (CRC Press), 2013, First Edition.
1 Health Informatics: Practical Guide for Healthcare and Information Technology Professionals, by Robert E. Hoyt and Ann Yoshihashi, published by Lulu Press, 2021, Seventh Edition.
2 Healthcare Data Analytics, edited by Chandan K. Reddy and Charu C. Aggarwal, published by Chapman & Hall/CRC Press, 2015, First Edition.
3 Big Data in Healthcare: Statistical Analysis and Machine Learning Applications, by Farrokh Alemi and David A. Talley, published by CRC Press, 2018, First Edition.
4 Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes, by Arjun Panesar, published by Apress (Springer Nature), 2019, First Edition.
5 Artificial Intelligence in Healthcare, edited by Adam Bohr and Kaveh Memarzadeh, published by Academic Press (Elsevier), 2020, First Edition.

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020, in force from the academic year 2026-27. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

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