Data Structures and Algorithms Without the Fear
Data Structures & Algorithms: leave with a before/after refactor with complexity analysis and a benchmark on growing inputs.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why data structures & algorithms matters — the common problem: Code works on small inputs but slows to a crawl on real data because of the wrong data structure.
- 2Core concepts in plain language: Arrays, linked lists, stacks, queues, hash tables, trees
- 3Going further: heaps, graphs, tries, searching, sorting, recursion
- 4Framework walkthrough: Big-O Complexity Analysis, Computational Thinking, Worked Problem Sets
- 5Practical workflow, built live: A before/after refactor with complexity analysis and a benchmark on growing inputs.
- 6How to measure it: Correctness on test cases, Time and space complexity, Problems solved independently
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A before/after refactor with complexity analysis and a benchmark on growing inputs.
- A working understanding of Big-O Complexity Analysis and Computational Thinking
- A short list of measures to track: Correctness on test cases, Time and space complexity, Problems solved independently
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