Skip to content

Module Dependency Map

Guide Fit

flowchart TD
  family["Python Programming"] --> program["Python Object-Oriented Programming"]
  program --> pressure["A concrete learner or reviewer question"]
  pressure --> guide["Module Dependency Map"]
  guide --> next["Modules, capstone, and reference surfaces"]
flowchart TD
  question["Name the exact question you need answered"] --> skim["Skim only the sections that match that pressure"]
  skim --> crosscheck["Open the linked module, proof surface, or capstone route"]
  crosscheck --> next_move["Leave with one next decision, page, or command"]

Read the first diagram as a timing map: this guide is for a named pressure, not for wandering the whole course-book. Read the second diagram as the guide loop: arrive with a concrete question, use only the matching sections, then leave with one smaller and more honest next move.

This map exists to prevent a common failure mode: reading an advanced chapter without the earlier concept that gives it meaning.

What depends on what

  • Module 01 is the semantic floor. Nothing later makes sense without it.
  • Module 02 depends on Module 01 because object roles only matter after object semantics are clear.
  • Module 03 depends on Modules 01 and 02 because lifecycle and typestate need stable ownership.
  • Module 04 depends on Modules 01 to 03 because collaboration only works if single-object contracts are already explicit.
  • Modules 05 to 07 depend on Module 04 because persistence and runtime pressure should preserve the earlier domain boundaries.
  • Modules 08 to 10 depend on all earlier modules because verification, API governance, and operational mastery are audits of the whole design.

How to use this map

  • If a module feels abstract, move one step left and review its prerequisite.
  • If a module feels repetitive, ask which new pressure it is adding to the same design.
  • If the capstone feels confusing, match the confusion to the earliest module that explains that boundary.

Honest interpretation

The course is not linear because learning should be rigid. It is linear because later trade-offs become cheap slogans when the earlier ownership model is missing.