Day 1: Foundations & ML Concepts
Monday, February 2, 2026
Overview
Day 1 establishes the foundation for the entire workshop. We’ll cover fundamental concepts in data science, introduce machine learning principles, and get everyone set up with the computing environment—including a hands-on demonstration of overfitting.
Learning Objectives
By the end of Day 1, you will be able to:
- Distinguish between machine learning, statistics, and econometrics approaches
- Understand the difference between prediction and causal inference
- Explain the bias-variance tradeoff and why overfitting is dangerous
- Set up a Google Colab environment for Python-based analysis
- Visualize overfitting using a sine wave demonstration
- Understand the role of data science in health policy decisions
Schedule
| Time | Session | Lead |
|---|---|---|
| 8:30-9:30 | Registration, Welcome, Introductions | Ozawa |
| 9:30-10:30 | Fundamentals of Data Science | Sylvia |
| 10:30-10:50 | Break | |
| 10:50-12:30 | Data Science in Public Health Policy | Ozawa |
| 12:30-1:30 | Lunch | |
| 1:30-3:00 | ML Foundations: Core Concepts | Sylvia |
| 3:00-3:20 | Break | |
| 3:20-5:00 | Hands-on: Setup + ML Demo | Sylvia |
Materials
Slides
Other
Hands-on Exercise
Key Concepts
ML Foundations (Afternoon)
The afternoon lecture introduces core machine learning concepts:
- Supervised learning: Learning patterns from labeled data
- Prediction vs. causation: When prediction is enough
- Bias-variance tradeoff: Understanding model complexity
- Cross-validation: Preventing overfitting
- Preview of Day 2: LASSO, Ridge, Trees
Preparation
Before this session:
- Complete the Computing Setup guide
- Ensure you can access Google Colab
- Review the pre-workshop readings