Statistical Data Processing

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Homework 1 — GitHub Setup & First Report ​

Prerequisite Make sure you have registered on Moodle for this course. If you are not registered, we cannot grade your work and give you access to your repo.

Overview ​

In HW1 you will:

  • Use your pre-created GitHub repository username-hw-1.
  • Add a short, readable report (Markdown README.md or Jupyter notebook).
  • Commit any code and data instructions needed to reproduce your output.
  • Submit via a GitHub Issue.
  • Complete one peer review.

This establishes the workflow used for all later homeworks.

Learning Goals ​

  • Get comfortable with the course GitHub submission workflow.
  • Write a clear, concise, markdown-style report.
  • Make results reproducible (code + instructions).
  • Practice constructive peer review.

Tasks ​

  1. Prepare the repository

    • Accept access to your repo username-hw-1.

    • Clone it locally.

    • Create a minimal structure:

      username-hw-1/
├─ HW1/
│  ├─ README.md  (or HW1.ipynb)
│  ├─ src/       (your code, if any)
│  └─ data/      (optional; or instructions to obtain data)
├─ environment/  (optional: env files, e.g. requirements.txt or environment.yml)
└─ README.md     (optional repo-level notes)

  2. Write the report (Markdown or Notebook) Include, at minimum:

    • Title: “Homework 1”
    • About you (2–4 sentences): academic/programme context only (no personal contact details).
    • Prior experience with data science (3–6 sentences): tools/languages you’ve used, courses, interests.
    • One simple figure or table (optional): e.g., a tiny demo using any dataset (can be built-in, e.g., seaborn.load_dataset("tips")), purely to test your toolchain.
    • Reproducibility note: brief instructions to run your code (e.g., “pip install -r requirements.txt then open HW1/HW1.ipynb”).

  3. Make it reproducible

    • Commit the code that produces your outputs (plots/tables).
    • If you use any data, include download instructions or a small sample; do not commit large files.
    • Provide minimal environment instructions (requirements file or a short note).

  4. Submit

    • Push your work.

    • Open an Issue in the repo titled HW1 – Submission. (Optional) add the label ready-for-grading.

    • In the Issue body, include:

      • A link to your report file.
      • A 2–3 line summary of what’s included.
      • Any notes for the grader (e.g., where to find code).

Allowed Tools ​

Use any tools you like (Python/R/SQL/editors). The only requirements are:

  • The report is readable on GitHub (README.md or .ipynb).
  • All code used to generate outputs is included so results are reproducible.

Peer Review (after the deadline) ​

Review your assigned partner’s repository and comment under their HW1 – Submission Issue.

Checklist (copy into your comment):

  • Coverage: Are all HW1 tasks present? (repo structure, report, submission issue)
  • Reproducibility: Is it clear how to run the code and reproduce outputs?
  • Clarity: Is the report concise and readable on GitHub?
  • One highlight: Something done especially well.
  • One suggestion: A specific, actionable improvement.

Grading ​

Per-homework grading: U (Fail) / G (Pass) / VG (Pass with Distinction) based on:

  • Completeness: Required items present (repo, report, code, submission issue).
  • Clarity: Report is readable, well-structured, concise.
  • Correctness/Reproducibility: Code runs or is clearly explained; outputs are reproducible with provided notes.

Notes

  • Late submissions require an extra task and are graded Pass/Fail only (no VG).
  • Keep personal information minimal and professional.

Submission Summary (quick reference) ​

  • Repo: username-hw-1
  • Report: HW1/README.md or HW1/HW1.ipynb
  • Issue: HW1 – Submission (link to report; optional label ready-for-grading)
  • Deadline: Monday 23:59 (Europe/Stockholm)