Understand your source data
Inspect file structure, encoding, business grain and field meaning before calculating anything.
A method you can explain.
Work through the reasoning, attempt the task and compare the separate solution. Keep your source copy unchanged and save your own evidence locally.
Before you start: Basic spreadsheet skills; complete the earlier modules in this track. Python exercises introduce each operation and include a separate worked script.
The suggested study time includes exercises. Use a spreadsheet or text editor for introductory tasks; code lessons use a local Python environment with the supplied setup guide. This website does not run arbitrary Python or upload your files.
Three lessons. One connected workflow.
File formats and encodings
Detailed explanation, practical exercise and worked solution.
Data types and analytical grain
Detailed explanation, practical exercise and worked solution.
Build a source inventory and dictionary
Detailed explanation, practical exercise and worked solution.
Real tasks.
Inspectable evidence.
Download the complete pack or choose individual files. Start with the dictionary and worksheet. Templates are deliberately unfinished; the separate worked reference contains explanations and solutions.
All data are fictional. Downloadable code uses local files and fresh output paths. Read the setup guide before running it. The offline HTML reference is static and does not execute Python or save progress.
Open the worked reference ↗Can someone reproduce your work?
Use the evidence-based self-review to check your method, validation and explanation. Keep unresolved questions visible. This is guided practice, not certification or production approval.
Begin Module 01 →Return to the complete course ↗
Next module: Clean data without losing evidence →
Explore practical lessons, worked examples and projects. Follow a published path, practise the method and build work you can explain.