Coding practice built around solving the whole problem.

Choose a problem by topic and difficulty

The coding catalogue separates foundations from scenario-based problem categories. Search for a problem, use difficulty and completion filters, or move through the problems in a category. The practice editor supports Python, Java and JavaScript. Start with a language you can read comfortably so that the algorithm remains your main focus.

Before writing code, identify the required input, the exact output format and the constraints. A solution can use the right idea and still fail because it prints an extra label, assumes the wrong index base or ignores a permitted empty input.

Turn a scenario into a small algorithm

Suppose a task asks you to count purchases whose value is greater than a given limit. Decide whether equality should count, walk through a small list by hand, and then write a loop that maintains the count. For values 120, 80 and 120 with a strict limit of 100, the expected count is 2.

That small example checks your interpretation before the compiler checks your syntax. Next consider a boundary: if the list is empty and the task permits it, the count should be 0. For larger problems, the same habit helps expose incorrect assumptions about duplicates, ordering or ties.

Use sample runs and final submissions differently

Run samples to compare your output with the visible examples. When you are ready, submit the solution to check all configured cases, including hidden tests where available. The final checked submission records progress; a sample run alone does not mark a problem complete.

• Match spaces, line breaks and numeric formatting to the problem statement.
• Check the smallest and largest allowed inputs before submitting.
• If some cases fail, revisit the assumptions rather than only changing syntax.

Track progress without losing sight of understanding

Saved completion shows which available problems you have solved against all their configured checks. Check accuracy reflects passed test cases across final submissions, including unsuccessful attempts. These measures answer different questions: how much have you completed, and how consistently have your submissions passed?

Saved positions help you return to a selected problem, while device drafts let you continue editing. Reattempting a solved task can be useful revision, but it does not add another distinct completed problem. Explain the approach, complexity and important edge cases after solving; passing the configured tests alone is not proof of mastery.