Problem Solving — Computer Science & Data Science

The course handbook: how the course works, what is assessed, and how to navigate 32 lectures and 144 student case studies.

• New here? Read Course Overview first.
• Planning your semester? See the Course Outline — it links every lecture and lists the cases used in each.
• Teaching this course? Teaching Methodology and the Instructor Guide are for you.
• Looking for a term? Check the Glossary or FAQ.

The homepage of the built site additionally carries the 32-lecture grid and direct links to every material type.

What is public, and what is not

The public site publishes the handbook, all lecture plans and notes, all student-facing case studies, quizzes (without answer keys), and the student side of labs, assignments, projects, and activities. Instructor solutions and every answer-key directory are excluded at build time — the build fails loudly rather than publish them. Full instructor materials are distributed separately (see Instructor Guide).

Site map

• Course Overview
• Learning Outcomes
• Course Outline — all 32 lectures with case ranges
• Teaching Methodology
• Assessment Plan
• Instructor Guide
• Resources
• Glossary
• FAQ
• Deployment — how the site is built and published
• Handover Report — repository handover: stack, commands, and the instructor's remaining manual tasks
• Final Audit Report — pre-publication audit across curriculum, cases, security, site, and workflows
• Case Review — pedagogical audit of all 128 cases
• Topic-Coverage Heatmap — which topics the cases cover, lecture by lecture (with CSV and SVG heatmap)
• Case-study catalog — all 144 cases
• Lectures 01–32 — each folder holds the plan, notes, worked examples, and the lecture quiz (see the outline for per-lecture links)

Using this repository

Everything is plain Markdown validated by scripts/validate.py. To rebuild the site locally:

python scripts/build_site.py
python -m http.server 8000 --directory site

The site is dependency-free (plain HTML + one stylesheet); search.html runs entirely in the browser over pages.json.

License

Tooling is MIT-licensed; teaching content is CC BY 4.0. See LICENSE.

• Lecture 01Thinking in ProblemsCases cs-001–cs-004
• Lecture 02Pseudocode as PrecisionCases cs-005–cs-008
• Lecture 03Flowcharts and Control FlowCases cs-009–cs-012
• Lecture 04First Python: State and SequencesCases cs-013–cs-016
• Lecture 05Numbers, Remainders, and CyclesCases cs-017–cs-020
• Lecture 06Traces and Loop ReasoningCases cs-021–cs-024
• Lecture 07Debugging as a MethodCases cs-025–cs-028
• Lecture 08Testing Before TrustingCases cs-029–cs-032

• Lecture 09Aggregation PatternsCases cs-033–cs-036
• Lecture 10Strings Under the LensCases cs-037–cs-040
• Lecture 11Lists and SlicingCases cs-041–cs-044
• Lecture 12Debugging Lab: Real DefectsCases cs-045–cs-048
• Lecture 13Dictionaries and SetsCases cs-049–cs-052
• Lecture 14Functions and ReuseCases cs-053–cs-056
• Lecture 15Counting, Combinatorics, and ChoiceCases cs-057–cs-060
• Lecture 16Mid-Course ConsolidationCases cs-061–cs-064

• Lecture 17Searching and Linear ScansCases cs-065–cs-068
• Lecture 18Sorting and Pair ScansCases cs-069–cs-072
• Lecture 19Invariants and Two PointersCases cs-073–cs-076
• Lecture 20Brute Force and When It WinsCases cs-077–cs-080
• Lecture 21Binary Search EverywhereCases cs-081–cs-084
• Lecture 22Recursion and Divide-and-ConquerCases cs-085–cs-088
• Lecture 23Greedy and Dynamic ProgrammingCases cs-089–cs-092
• Lecture 24Graphs as ModelsCases cs-093–cs-096

• Lecture 25Thinking with DataCases cs-097–cs-100
• Lecture 26Counting and Probability in DataCases cs-101–cs-104
• Lecture 27Tables, Cleaning, and AnomaliesCases cs-105–cs-108
• Lecture 28Summaries That Don't MisleadCases cs-109–cs-112
• Lecture 29Simulation as a Way of KnowingCases cs-113–cs-116
• Lecture 30Optimization FormulationsCases cs-117–cs-120
• Lecture 31Strategy Under UncertaintyCases cs-121–cs-124
• Lecture 32The Whole Toolbox: Capstone CasesCases cs-125–cs-128