Cyclistic Ridership Analysis
Python analysis artifacts, a dimensional BI model, and a financial scenario explorer.
Source figurePython analysis artifact
Question, contribution & scope
- The question
- How do casual and member usage patterns differ, and how could an analytical model support a membership-conversion decision?
- The approach
- Separate data preparation, exploratory analysis, dimensional modeling, and scenario assumptions so each can be inspected on its own.
A modular Python pipeline, analysis figures, a one-fact/six-dimension BI structure, DAX definitions, and a browser-based scenario model.
The BI export contains 61,973 demonstration records. The repositories expose the analytical workflow and modeling logic, without establishing realized commercial results.
Scope: Independent work, not a client engagement. Data-generation code is present in the source; historical provenance and the original run behind the Python figures are not independently established. Financial outputs are hypothetical.