Beginner–Intermediate · 5 modules · 15 lessons
Data Literacy
Explore Data Literacy through 5 modules and 15 approachable lessons, with examples, practice, feedback, and a final hands-on project.
Written and edited by . Updated 2026-08-24.
What you will learn
- Turn broad questions into measurable data questions.
- Interpret probability, prediction, and model errors responsibly.
- Document data quality, provenance, limitations, and decision impact.
Course outline
Module 01
Asking Questions with Data
Turn a broad question into a precise data question, then define the unit, quality, and source of the data.
- Questions and Metrics · 12 minutes
- Unit of Analysis · 16 minutes
- Quality and Provenance · 20 minutes
Practice output: Data question brief
Module 02
Probability for Decisions
Use distributions, base rates, and conditional probability to update a decision when new evidence arrives.
- Data Distribution · 12 minutes
- Base Rates · 16 minutes
- Updating with Evidence · 20 minutes
Practice output: Evidence update note
Module 03
Prediction and Classification
See how features, targets, regression, classification, and error types shape a model's result.
- Features and Targets · 12 minutes
- Regression · 16 minutes
- Classification and Similarity · 20 minutes
Practice output: Model interpretation card
Module 04
Text Data and Representation
Compare tokens, word counts, TF-IDF, embeddings, and similarity to see how models process text.
- Tokens and Counts · 12 minutes
- TF-IDF · 16 minutes
- Embeddings and Similarity · 20 minutes
Practice output: Text data map
Module 05
Evaluation and Overfitting
Split data correctly, set a baseline, find error patterns, and prevent data leakage.
- Training, Validation, and Test Data · 12 minutes
- Data Leakage and Overfitting · 16 minutes
- Error Slices · 20 minutes
Practice output: Data decision memo
