Python Study Plan and Learning Roadmap

The foundation covers everything the later tracks assume. Study it in order, because each stage builds on the last:

Depth lessons, such as type casting and range() in depth, sit next to their main topic; read them when you reach it. Skipping prerequisites, for example jumping to classes before functions are comfortable, turns later topics into memorisation instead of understanding. With around five hours a week, most learners complete the foundation in two to four months.

Once the core is comfortable, learn the habits that make code reliable and shareable:

• Debugging and logging: find and explain problems.
• pytest in depth and mocking and fixtures: prove code works.
• PEP 8 style and type checking: keep code readable and catch mistakes early.
• Dependency management and Git: make projects reproducible and collaborative.

These skills grow best through projects rather than as separate definitions. Add tests and logging to your beginner and intermediate projects as you learn them, use Git from the first commit, and run a formatter and linter on everything. Within a few weeks the habits become automatic.

After the core and professional skills, choose one track based on your goal. You don't need all of them to build useful programs:

The machine learning track assumes the data science track: models are fitted on pandas and NumPy data, and evaluating them needs the statistics lessons. Approach it after those prerequisites. Automation and scripting need no separate track: the core, plus files, the standard library and environment configuration, covers most of it.

Measure progress by what you can do and explain, not by lessons completed:

• Projects: finished, tested, documented programs, ideally one per stage, in a public Git repository.
• Explanations: can you explain your design choices and the trade-offs you considered?
• Reproducible checks: tests that pass on a clean checkout, and quiz results by chapter that stay high a week later.

For a first portfolio, choose projects that match the role you want: an automation tool that saves real time, a small backend API with a database, or a data report with clear charts and documented cleaning. Two or three solid projects beat ten tutorial copies.

Completing a course or scoring well on quizzes doesn't guarantee expert status or a job. Employers look for evidence of building and maintaining real programs, communicating clearly and learning independently. The interview questions lesson and coding challenges help with the interview itself.

Task: write a realistic weekly study plan and choose one career-aligned specialisation and a demonstrable portfolio project. Fill in this template:

plan = {
    "hours_per_week": 5,
    "sessions": {"read": 2, "practise": 2, "build": 1, "review": 1},
    "current_stage": "Intermediate",
    "goal": "data analyst",
    "track_after_core": "Data science",
    "portfolio_project": "monthly sales report from CSV exports with cleaning notes and charts",
    "review_date": "2026-12-01",
}
assert sum(plan["sessions"].values()) <= 7, "plan more rest days"
print(f"{plan['goal']}: {plan['track_after_core']} track, project: {plan['portfolio_project']}")

Revisit the plan on its review date and adjust the pace honestly. For the worked check, the separate machine learning track should be approached after the core and relevant data science prerequisites.

The official Python tutorial complements this course. Use the Python exercises and Python quiz to check each stage, and return to the Python course page for the full lesson list.