edX
edXAdvanced Topics in Healthcare Data Analysis
99 studentsADVANCEDEnglish
Korshub's take
Beyond Descriptive Statistics
Healthcare data demands causal thinking, not just correlation. This advanced course moves into matching, fixed effects, random effects, and longitudinal data—the techniques that separate defensible findings from statistical mirages.
Six modules build sequentially: causal inference tools, Simpson's Paradox, repeated measures, and handling missing data. You'll use R for hands-on application. For epidemiologists, health informaticists, and researchers ready to strengthen their analytical rigor.
About this course
In this course, you will learn about some of the advanced skills you will need for real-world healthcare data analysis. You will continue to practice these skills using the statistical programming software called R and examples from the healthcare industry. The topics covered in this course will help you to engage in the more advanced data wrangling that is often necessary for data analysis and to make data-informed decisions in the healthcare field. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods.
This self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only).
The course is comprised of 6 modules that you should complete in order, as each subsequent module builds on the previous one.
- Module 1: Causal Inference and Tools for Model Specification
- Module 2: Matching to Reduce Model Dependence
- Module 3: Simpson's Paradox and Fixed Effects
- Module 4: Random Effects
- Module 5: Repeated Measures and Longitudinal Data
- Module 6: Missing Data and Bootstrapping
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