Intermediate Python: Applied Problem Solving
This course is being redesigned around using Python to solve unfamiliar problems. The language features and development tools it assumes are now the required Python Foundations pathway. No Intermediate lessons or completion requirements are published yet.
Before you start
- Designed for: learners who know Python’s foundational tools and want to combine them without being told which feature to use.
- You will be ready when: you have completed Python Foundations and can use its collections, functions, files, tests, environments, and abstractions without being told which feature to choose.
- Practice setup: the planned challenges will run in Colab or locally and will include example inputs, tests, and space to compare solutions.
Planned learning promise
Intermediate will be defined by the work learners perform, not by a list of supposedly intermediate syntax. Learners will practice how to:
- turn a written problem and sample input into explicit requirements;
- parse input and choose useful data structures;
- decompose a solution into small functions and testable steps;
- build a simple correct solution before refactoring it;
- compare alternative models or algorithms;
- explain why a solution works, where it fails, and what it costs;
- debug unfamiliar cases using evidence rather than hints about which Python feature to use.
Planned challenge format
Each original challenge will follow the same applied cycle:
- Understand: restate the problem, constraints, inputs, and outputs.
- Model: select representations and write small examples by hand.
- Solve: implement a clear baseline without premature optimization.
- Validate: convert examples and edge cases into tests.
- Improve: refactor, compare approaches, and inspect complexity.
- Explain: document the reasoning and one important trade-off.
Possible themes include delivery-log parsing, grid navigation, inventory reconciliation, scheduling, text transformations, and small simulations. The problems will be original while using the progressive challenge style that makes puzzle-based practice effective.
Assessment and certificate status
The future assessment should measure complete problem-solving evidence: a working solution, tests, explanation, controlled refactoring, and reflection on trade-offs. Thresholds, a final applied project, curriculum version, and certificate requirements remain TBD. Nothing on this page currently represents a completed or verified credential.