Data structures and complexity
Part of the Technology & Software Engineering learning and recruiting path. Read the explanation and example, attempt the task, then open the self-check.
Know what to learn for your role.
From foundations to a work sample, with sources, tasks and self-checks.
Understand
Arrays, maps, stacks, queues and trees suit different operations. Establish correctness before time/space complexity. Complexity describes scaling, not one machine timing.
Worked example
Pairwise duplicate checks take O(n²). A set usually gives expected O(n) traversal using extra space, subject to hashing behaviour.
Examples and numbers are educational, not employer questions, live market data or hiring criteria.
Practise and keep your output
Implement two duplicate-detection approaches with boundary cases, complexity and tradeoffs.
Keep inputs and sources, assumptions, steps, results and uncertainties in your own document. Attempt independently before checking below.
After attempting: answer and self-check
Distinguish worst-case from expected complexity; one benchmark does not prove universal superiority.
Official channels and further reading
Pathline authors these starter lessons. These supplementary field links do not imply provider review or endorsement. Confirm Hong Kong availability and eligibility separately.
- Microsoft · Early in profession ↗
Global student programmes; filter location, function and eligibility.
- JPMorganChase · Students and graduates ↗
Global programme directory. Filter by function, location and intake.
- Python · Official tutorial ↗
Programming-language reference. Complete this line’s basic exercises alongside it.