Tutorials & guides
Hands-on walkthroughs and plain-English explainers built around the realistic datasets on this site. Every tutorial links the exact data so you can follow along — and reproduce it later from the same seed.
Step-by-step tutorials
Practical, end-to-end projects you can finish in under an hour with a free dataset.
Build a Power BI sales dashboard
Load realistic sample sales data, model it with a date table, write DAX measures (revenue, margin %, MoM growth), and design an interactive report.
Market-basket analysis in Python
Run Apriori and association rules with mlxtend on POS baskets that have real product affinities — and interpret support, confidence and lift.
NRR & cohort analysis in Python
Rebuild MRR from a movement ledger, compute net revenue retention, and build cohort retention curves that decay realistically.
Guides & explainers
Background reading on synthetic data and how to work with it.
What is synthetic data?
A plain-English guide to what synthetic data is, how it's generated, why teams use it, where it falls short, and how it differs from anonymized and mock data.
How to generate realistic test data
What makes test data realistic, why correlated fields matter, and a reproducible workflow for CSV, JSON or SQL.
CSV vs JSON vs Excel vs SQL
Strengths, weaknesses and exactly when to use each of the four common dataset formats.