Analytics for Better Health
About This Course
This course aims to introduce the data analytics techniques that are commonly needed for healthcare data and healthcare problems. We will investigate the unique challenges and considerations of implementing data analysis for healthcare problems. This will be followed by a focus on developing predictive models for various healthcare applications. We will cover the commonly used predictive machine learning models, their learning mechanisms and methods to picking the most suitable model. In the second half of the course, we will focus on the conduct of causal inference studies with retrospective healthcare data. Various causal inference techniques will be introduced. Students will gain hands-on experience of all the introduced concepts throughout the course.
What You'll Learn
1. Evaluate analytics results to determine their impact on business value in the healthcare sector
2. Elaborate the key elements to build a successful healthcare analytics solution.
3. Summarise the common data source in healthcare
4. Outline the commonly used data collection, data cleansing in healthcare analytics techniques.
5. Analyse the key problems and challenges in the healthcare clinical analytics area.
6. Apply the concepts and techniques to conduct causal inference studies with retrospective healthcare data
Entry Requirements
• Bachelor’s degree
• Relevant public health related work experience
• Prior knowledge of statistics/ biostatsitcs is required
• Candidates with other qualifications and experience may be considered on a case by case basis, subject to approval