Free short course
Data and measurement for AI
Explore missingness, leakage, class imbalance, synthetic data and causal claims.
This course is free in full: every module, knowledge check and the final task, with no sign-up.
Explore missingness, leakage, class imbalance, synthetic data and causal claims.
Basic percentages; no coding required. Each lesson includes a worked example, a practice task and an answer explanation. Allow about 12 minutes per lesson, five minutes for the knowledge check and 30 minutes for the final assignment. The reading is concise; the study time includes doing the exercises.
All scenarios are fictional. No paid subscription, specialist hardware or live AI integration is required to complete the written exercises. Advanced courses teach design and evaluation principles; they do not claim to provide a production implementation or professional accreditation.
By the end, you'll be able to…
- Write a data-quality and evaluation note that exposes misleading results.
- Choose a missing-value treatment that fits the meaning.
- Keep evaluation information out of model development.
- Explain why a high accuracy can hide a useless classifier.
- Use generated examples without treating them as ground truth.
- Identify alternative explanations for a pilot result.
What's in it
- Module 1: Data and measurement for AI in practice
Write a data-quality and evaluation note that exposes misleading results.
- Finishing the course
One final task that puts the whole method to work on something real.