Course Outline

Thirty-two lectures of two hours each (64 contact hours), organised in four blocks. Core cases are mandatory; the case pages also contain optional extensions. Every lecture owns its four base cases cs-4N−3 … cs-4N (lecture N), and fourteen lectures additionally carry one or two applied cases (cs-129 – cs-144) that integrate data science, systems, and communication scenarios — see the case-study catalog for each lecture's full list.

Week numbers assume one lecture per week plus the scheduled lab/quiz weeks of your calendar. The generated Case-Study Catalog is the single source of truth for case titles, difficulty levels, and links.

Most lectures follow the standard case-cycle rhythm; seven workshops, two debugging clinics, one review session, and the capstone deliberately use different structures (see Teaching Methodology). Every plan carries prerequisite knowledge, a topic outline, worked examples, activities, instructor notes, practice exercises, exit questions, and an explicit learning-outcome mapping.

Block I — Foundations (Lectures 01–08)

Computational thinking, pseudocode, flowcharts, first Python, traces and debugging, systematic testing.

Block II — Core Techniques (Lectures 09–16)

Iteration patterns, strings, lists, dictionaries and sets, functions, counting, mid-course consolidation.

Block III — Algorithms (Lectures 17–24)

Search, sorting, binary search, recursion, greedy, dynamic programming, graph basics — with analysis woven through.

Block IV — Data Science & Strategy (Lectures 25–32)

Data-oriented reasoning, probability, simulation, optimisation, strategy, capstone.

Assessment calendar