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

• 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

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