Every module includes three substantial lessons, downloadable source files, practical exercises, explained solutions and a knowledge check. Modules marked free are available under the current public access rules. Protected self-learning modules are free after registration and email verification. Publication, prerequisites and explicit private-access rules still apply.
01Free module
Python Fundamentals and Data Structures
Use types, collections and iteration to implement a small analytical summary.
- A type/field map
- A standard-library monthly summary
- Boundary tests and an interpretation note
Overview & practice files ↗View lesson outline
- Choose types that preserve meaning
- Iterate with a clear population rule
- Test boundaries and explain the result
02Free module
Functions, Modules, and File Handling
Separate responsibilities, preserve file evidence and expose expected errors.
- A tested function contract
- An explicit local file/module layout
- An error-disposition test log
Overview & practice files ↗View lesson outline
- Design a function contract
- Read files with explicit context and encoding
- Handle expected errors without hiding defects
03Free · verified account
NumPy and Numerical Computing
Reason about array shape, indexing, broadcasting and numerical units.
- An annotated array/axis map
- A loop/vectorisation reconciliation
- A numerical and missingness note
View access requirements ↗View lesson outline
- Understand shape and dtype · Verified account
- Vectorize multiplication and apply masks · Verified account
- Check numerical results and missingness · Verified account
04Free · verified account
Pandas and Data Manipulation
Use labelled transformations and validate relationships before grouping.
- A schema/profile record
- A validated merge and failure case
- A reproducible grouped analysis
View access requirements ↗View lesson outline
- Read a DataFrame with an explicit schema · Verified account
- Merge with validation and visible unmatched rows · Verified account
- Group, derive and preserve provenance · Verified account
05Free · verified account
Visualization and Exploratory Data Analysis
Generate a labelled Matplotlib figure and interpret the observed comparison.
- An EDA question and checked summary
- A reproducible labelled PNG
- A caption and changed-input review
View access requirements ↗View lesson outline
- Choose an exploratory question and population · Verified account
- Build an explicit Matplotlib figure · Verified account
- Explain change without inventing causes · Verified account
06Free · verified account
Complete Python Data Analysis Project
Deliver an independently reproducible analysis and evidence-led explanation.
- A scoped clean-process project
- Mutation and failure-test evidence
- A reproducible report and honest handover
View access requirements ↗View lesson outline
- Plan a clean-process analytical project · Verified account
- Implement, test and challenge the analysis · Verified account
- Write a report and handover another analyst can use · Verified account
07Free · verified account
Customer behavior, RFM features and segmentation
Analyze customer behavior without inflating session metrics: use an original fictional source to implement, verify and explain three connected professional assignments.
- Source/metric/access contract
- Independent implementation and exact run instructions
- Unchanged control and changed-input/failure evidence
- Decision memo with limitations and next evidence
View access requirements ↗View lesson outline
- Analyze customer behavior without inflating session metrics · Verified account
- Build reproducible RFM-style features at an analysis cutoff · Verified account
- Evaluate customer segments as descriptive hypotheses · Verified account
