Python for Health Data Processing and Analysis

Overview

This hands-on workshop builds on the skills introduced in Python Programming Fundamentals for Health Research by applying them to a real-world health research project. Participants will work through an end-to-end data processing and analysis workflow for coronary artery disease prediction. Participants will code alongside the instructor throughout the workshop, with support available as they work through the exercises.

Using Python, participants will clean, transform, combine, explore, analyze, and visualize health data while developing a reproducible workflow from raw data to publication-ready results. Good programming practices and testing techniques introduced in the fundamentals workshop will be reinforced throughout, with an emphasis on applying them when using generative AI to support data processing and analysis.

Learning Objectives

By the end of this workshop, participants will be able to:

• • Build a reproducible data-cleaning and analysis workflow
• • Identify and handle missing, duplicate, and invalid values
• • Transform raw health data into meaningful measures for analysis
• • Combine information from multiple datasets
• • Explore and summarize health data using descriptive analysis
• • Create effective data visualizations to present health research findings
• • Create publication-ready descriptive tables that can be automatically updated as the data or analysis changes
• • Work efficiently with large datasets
• • Use generative AI responsibly to support data processing and analysis while verifying the accuracy of generated code and results

Participants are expected to complete our Python Programming Fundamentals workshop first or have equivalent Python programming skills. Participants are also required to bring a Windows or Apple laptop and have a Google account to access the Google Colab cloud computing environment.