025-squirrel/docs/business-case.md

Business Case

Metadata

Key	Value
ID	BC-001
CrossReference
Language	en
Domain	it

Version History

Date	Status	Author	Reviewer	Change	Commit
2026-10-13	Deprecated	Jens Tirsvad Nielsen	AI as stand-in for S01	Initial version	f1b3c03
2026-10-13	Accepted	Jens Tirsvad Nielsen	AI as stand-in for S01	CrossReference: added UCD-001	259d017

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Executive Summary

Day 25 of the Udemy "100 Days of Code" bootcamp asks the participant to analyse the 2018 Central Park Squirrel Census with Pandas: count the gray, cinnamon and black squirrels by primary fur colour and export the counts to squirrel_count.csv. This project delivers that assignment as a small, readable, runnable Python repository with tests, source documentation, a README and a three-language wiki, so that the participant keeps a reviewed solution and other course participants and GitHub visitors can reuse it.

Methodological and Standards Foundation

The artifacts follow the software quality assurance (SQA) and quality control (QC) framework mounted at framework/ (Business Case, Stakeholder Analysis, Project Plan, milestones, tasks, code). Quality is judged against ISO/IEC 25010:2023 characteristics through the QC checklists of the framework; source code is reviewed against qc-programming-python.

Problem Statement

The lecture shows the analysis as a sequence of notebook-style steps. A participant who wants to keep, share and compare the solution has no structured repository: no repeatable set-up, no tests, no documentation and no instructions that another participant can run.

Business Opportunity

A single, well-structured example repository lets the participant practise professional project hygiene (virtual environment, tests, continuous integration, documentation) on a small assignment, and gives other participants a solution to compare against.

Objectives

1. Deliver a Python program that reads the squirrel census file (comma-separated values, CSV), counts squirrels by primary fur colour (gray, cinnamon, black) and writes squirrel_count.csv.
2. Cover the program with automated pytest tests that run without the full census file.
3. Document how to create a local .venv, upgrade pip, run the program and run the tests, for Windows PowerShell, Linux Debian and macOS.
4. Document the source with Doxygen comments and a Doxyfile.
5. Publish the repository with a description, topics, a README and a wiki in English, Danish and Bahasa Malaysia, with screenshots.

Scope

In Scope

• Reading the census file and counting squirrels by primary fur colour.
• Creating a summary table and exporting it as squirrel_count.csv.
• A command-line entry point, constants.py, pyproject.toml, a Python .gitignore, pytest tests and a continuous integration (CI) workflow.
• Doxygen comments, a Doxyfile and build instructions.
• README from the framework template, a three-language wiki and screenshots.
• Repository description and topics on the git host.

Out of Scope

• Analysis beyond the three fur colours of the assignment.
• Charts, notebooks or a graphical interface.
• Publishing a package to PyPI.
• Using or importing the .env file in the project; it holds personal tokens for publishing only.

Expected Benefits

Tangible Benefits

• A runnable solution with passing tests and a CI pipeline.
• A repository other participants can clone and run from the README alone.
• A published description, topics and wiki in three languages.

Intangible Benefits

• Practice in a plan-first, reviewed workflow on a small project.
• A reusable repository layout for later days of the bootcamp.

Strategic Alignment

The project supports the participant's goal of finishing the 100 Days of Code bootcamp with portfolio-quality results, and the framework's goal of showing the SQA workflow on a real, small project.

Success Criteria

#	Criterion	Target	Measure
1	Fur colour counts are correct	Gray 2473, Cinnamon 392, Black 103 on the 2018 census file	Run the program on the census file and compare with squirrel_count.csv
2	Tests pass	100 % of pytest tests pass on Python 3.13 or later	python -m pytest exit code 0 and CI green
3	Runtime dependencies	0 third-party runtime packages	pyproject.toml lists no runtime dependencies
4	Documentation present	README sections, Doxyfile and wiki pages in 3 languages	Files exist; doxygen Doxyfile completes without warnings
5	Repository metadata	Description and at least 5 topics set	Read back from the git host application programming interface (API)

Risks

Risk	Impact	Mitigation
The lecture uses Pandas but the repository must have no runtime dependencies	The solution differs from the lecture code	Use the standard library csv module; record the decision in an architecture decision record (ADR) and keep the lecture's step names as function names
The census file is large and not part of the repository	Others cannot run the program	Keep the census file under data/ and let the program accept a path argument
Tokens in .env leak into the repository	Credential exposure	.env is git-ignored, never read by the project and never printed
Wiki push is not available on the host	Wiki pages are not published	Keep the pages in the repository under docs/wiki/ and report the failure
Fur colour spelling differs in the data	Wrong counts	Count exact values Gray, Cinnamon, Black and test with fixture rows

Assumptions

• The participant (S01) is Product Owner, developer and reviewer of every artifact.
• The census file is the 2018 Central Park Squirrel Census file the participant downloaded.
• The git host git.tirsystem.com accepts the personal tokens held in .env.
• Python 3.13 or later is installed on the machines that run the project.
• The Product Owner's language is English; the wiki is the only multilingual deliverable.

Constraints

• Python 3.13 or later, virtual environment with venv, tests with pytest.
• Constants live in constants.py; configuration lives in pyproject.toml.
• Folder structure src/, tests/, docs/.
• Work is tracked by one branch and pull request per milestone.
• Tokens come from .env and are never tested, imported or committed.

Cost–Benefit Assessment

Costs	Benefits
About one working day of effort by S01 and the AI assistant	A reviewed, documented solution reusable by S02 and S03
Maintenance of three wiki languages	Broader reach for S02 and S03

The assessment is qualitative: the project is a personal learning exercise with no revenue, so a monetary return would not be meaningful.

Stakeholders

Stakeholder ID (SA)	Interest in this project
S01	Product Owner, developer and reviewer; wants a correct, reviewed solution
S02	Course participants; want readable, runnable code and run instructions
S03	GitHub viewers; want a clear description, topics and README with no runtime dependencies

Recommendation

Proceed — the scope is small, the risks are mitigated and the result is directly reusable by the other stakeholders.

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