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Learning Objectives

  • LO1: To define the term “Analytics” for healthcare
  • LO2: To be proficient with analytics tools for healthcare data preparation and analysis

Course Outcomes

  • CO1: Ability to understand the steps involved in the data mining process (e.g., pre-processing, classification, regression, clustering, and visualization) and apply them for analysis of healthcare data
  • CO2: Ability to describe different methods of predictive analytics and their applications in the healthcare domain
  • CO3: Ability to evaluate the data from diverse sources to create meaningful presentations

Course Contents

Getting to know your data – data pre-processing – exploring data – Probability and Uncertainty, Regression analysis – Mining patterns, associations, and correlations – classification and prediction, clustering, and outlier analysis.

The course will also include a practical component to implement theoretical concepts learnt via coding platforms such as Python and R.


  1. Business Analytics: Data Analysis and Decision Making by Christian Albright and Wayne Winston
  2. Data Mining Concepts and Techniques by Jiawei Han and Micheline Kamber

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