Your guide to the workbench.
Use this page whenever you need a reminder. Every main workspace also has a short “How this works” walkthrough you can replay.
Find your way around
Choose a direction, then work through one learning loop at a time.
- Pick a module. Learning path groups the 32 modules into phases. Open a phase, search for a topic, or resume your most recent lesson.
- Understand the progress indicators. Lessons read, lab checks passed, and projects marked complete measure different activities. Project and lesson completion are self-reported.
- Keep your work. Sign in for cross-device saving. Your notebook gathers notes and review items; Your learning data handles export and deletion.
Use a lesson
Read, trace, check, explain, then apply.
- Read with the outline. Use the lesson outline and section shortcuts to move between explanation, worked code, the knowledge check, and notes.
- Check your understanding. Choose a quiz answer and check it. Read the explanation. Incorrect answers appear in the notebook review tab.
- Write your own explanation. Save a note beside the lesson. Bookmark material you want to revisit, then open the related lab and mini project. Mark the lesson complete when you have worked through it.
Work through a coding lab
A failing test tells you where to investigate.
- Read the task and checks. The brief defines inputs, outputs, and constraints. Start by running the starter so you can see an actual failure.
- Edit and run. Run code shows your program output. Run tests evaluates it against the lab checks. Python downloads once and runs on your device. Use Stop if needed.
- Use help deliberately. Reveal one hint before loading a solution. Reset and reference solution replacement ask for confirmation because they replace your code.
- Move beyond the pass. A pass records practice progress. Follow the mini project to test a larger example and document what the checks do not cover.
Turn a mini project into evidence
A working demo is stronger when someone else can reproduce it.
- Read the project brief. Each module has a build prompt, deliverables, constraints, and a self-review rubric. Plan a narrow working slice.
- Build outside or alongside the lab. Project briefs are guidance, not a hosted deployment environment. Keep your project code in your own repository.
- Write an evidence brief. Use Build evidence to record the problem, baseline, reproduction steps, checks, results, and limitations. Export Markdown with your project. Completion is self-reported, not a review.
Use the capstone studio
Plan a substantial system around explicit evidence.
- Choose a blueprint. Read the problem, scope, and expected artifacts. Pick one you can meaningfully investigate.
- Work milestone by milestone. Use the deliverable checklist to track your work. These checkmarks record your own assessment.
- Package the outcome. Create an evidence brief and attach it to your own code or portfolio. The site does not publish, verify, or certify your capstone for you.
Make a realistic study plan
A schedule should fit the time you can actually protect.
- Set a weekly pace. Choose between 3 and 30 hours a week. The schedule uses curriculum estimates, not a guarantee of how quickly you will learn.
- Start with the next module. Use the weekly plan to open a lesson. Adjust the pace when your available time changes.
- Review actual progress. Lesson and lab progress update separately. Revisit difficult material instead of chasing the completion percentage.
Use your notebook
Bring your explanations and weak spots back into view.
- Return to your notes. The Notes tab collects explanations written in lessons. Open a note to return to its context.
- Use bookmarks and review. Bookmarks collect saved lessons. Review gathers incorrect quiz answers so you can revisit the mechanism and try again.
- Export and keep a copy. Export notes from the notebook. For code, project evidence, support requests, and optional analytics, use the full account export in Your learning data.
Try first. Save when you are ready.
Guest work stays temporarily in your current tab. Signing in does not silently overwrite existing account data: you choose whether to import guest work. Existing saved fields take priority.
Code executes locally in Python. Signing in stores code, notes, progress, and evidence in your account. Optional usage analytics starts off. You can export and delete your data.
Open your learning data