Python, Notebooks & Reproducible Analysis
Python, Notebooks & Reproducible Analysis groups the core ideas a learner needs at the foundations stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Python objects and variablesPython objects and variables is a practical concept within Python, Notebooks & Reproducible Analysis. It helps turn the broader workflow stage “Foundations” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02Lists, dictionaries and arraysLists, dictionaries and arrays is a practical concept within Python, Notebooks & Reproducible Analysis. It helps turn the broader workflow stage “Foundations” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
03Functions and reusable codeFunctions and reusable code is a practical concept within Python, Notebooks & Reproducible Analysis. It helps turn the broader workflow stage “Foundations” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
04Jupyter notebook workflowJupyter notebook workflow is a practical concept within Python, Notebooks & Reproducible Analysis. It helps turn the broader workflow stage “Foundations” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Virtual environments and dependenciesVirtual environments and dependencies is a practical concept within Python, Notebooks & Reproducible Analysis. It helps turn the broader workflow stage “Foundations” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
06Random seeds and reproducibilityRandom seeds and reproducibility is a practical concept within Python, Notebooks & Reproducible Analysis. It helps turn the broader workflow stage “Foundations” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.