B.Sc. (Data Science) Public Healthcare Analytics Practical Syllabus - Mumbai University
This is the Fourth Year BSc Data Science Honours syllabus under NEP 2020, phased in one year at a time, from the academic year 2024-25. 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 Public Healthcare Analytics Practical
Module I
- 1. EHR data modeling, data mining, exploratory data analysis using tableau or power BI. 2. Medical expenditure prediction 3. A Twitter Healthcare data extraction, processing and sentiment analysis use keywords related to health (e.g., COVID-19 vaccine). 4. Using classification algorithms in prediction of diseases 5. Clinical Text Data Categorization and Feature Extraction
Module II
- 1. Medical Image classification (x-rays) 2. Image object detection using deep learning. 3. Health forecasting using time-series. 4. Heart sound classification using signal processing. 5. AI conversational chatbot for primary healthcare diagnosis. 10 Text Books 1. Healthcare Business Intelligence + Website - A Guide to Empowering Successful Data Reporting and Analytics Hardcover by LB Madsen (Author),2012 11 Reference Books 1. Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence (Marketing Science) 1st Edition by Dr. Steven Struhl,2016 12 Semester End Examination: 100%
Text Books
- 1 Healthcare Business Intelligence + Website - A Guide to Empowering Successful Data Reporting and Analytics Hardcover by LB Madsen (Author),2012
- 1 Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence (Marketing Science) 1st Edition by Dr. Steven Struhl,2016
Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020. 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.