The Maths Developers Actually Use
Mathematics & Statistics for Computing: leave with a notebook applying descriptive statistics, probability and one hypothesis test to real data.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why mathematics & statistics for computing matters — the common problem: Developers avoid maths-heavy topics like ML and performance analysis because the basics feel shaky.
- 2Core concepts in plain language: Discrete mathematics, Boolean algebra, logic, sets, relations, combinatorics
- 3Going further: graph theory, linear algebra, calculus, probability, distributions, descriptive statistics
- 4Framework walkthrough: Big-O Complexity Analysis, Computational Thinking, Worked Problem Sets
- 5Practical workflow, built live: A notebook applying descriptive statistics, probability and one hypothesis test to real data.
- 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 notebook applying descriptive statistics, probability and one hypothesis test to real data.
- 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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