DS2002 — Data Science Systems (Spring 2026) — Workflow

Welcome to DS2002. This repo is your official record of work for DS2002.

• Do all coding/analysis in Kaggle Notebooks.
• Turn in links to your repos in Canvas.
• Use this GitHub repo to version, organize, and present your work professionally.

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Quick Start (What you do every week)

1. Open the week’s notebook in Kaggle (We will usually go over in class).
2. Do the work in Kaggle.
3. Download the notebook (.ipynb) from Kaggle when you’re ready to submit.
4. Put the file in the correct folder in this repo.
5. Commit + push.
6. Submit your GitHub repo link (and Kaggle link if asked) in Canvas.

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Non‑Negotiables (Read this once, save yourself pain)

Kaggle-only

Kaggle is the official environment for this course.
If you choose to run elsewhere, you’re on your own for debugging environment issues.

Canvas is the system of record

Grades, due dates, submissions, and feedback live in Canvas.

Teams is for conversation

Questions go to the Teams channels (perferable over email) since others can learn from you.
DMs are for private things you need to let me know about.

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File & Notebook Naming (Follow this exactly)

Every notebook in this course uses:

YYYY-MM-DD — Topic — Type.ipynb

Where Type is one of:

• Lecture (my examples in class)
• Studio (guided in-class build Usually on Wednesday)
• Lab (Friday hands-on)
• Homework (weekend extension)
• Template (project scaffold)

Example:

• 2026-02-04 — Pandas Core Ops (filter, groupby, join) — Studio.ipynb

Why we do this: it stays readable and sorts correctly everywhere.

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What to Submit (Most common question)

Unless Canvas says otherwise, you submit:

1. Your GitHub repo link
2. The .ipynb file(s) for the assignment in the correct folder
3. A short write-up in Markdown inside the notebook

For projects, you also submit:

• a project README (use the template in templates/)
• any small supporting artifacts (charts, exports, etc.)

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Notebook Hygiene (How to avoid “it worked on my machine”)

Before submitting any notebook:

• Restart kernel
• Run All
• Make sure the notebook runs top-to-bottom without errors
• Make sure outputs are present (plots, tables, printed results)
• Add short Markdown explanations between major steps

If you submit a notebook that doesn’t run, it will be graded as-is.

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Repo Structure (Where things go)

• notebooks/01-foundations/
  Notebook basics, Python fundamentals, GitHub workflow, submission standards

• notebooks/02-sql-databases/
  SQL + SQLite inside notebooks

• notebooks/03-pandas-json-apis/
  Pandas foundations, JSON normalization, API ingestion

• notebooks/04-etl-walmart/
  ETL templates + Walmart case framing

• notebooks/05-midterm-walmart-mini-project/
  Midterm project artifacts (starter, checkpoint, final)

• notebooks/06-capstone/
  Capstone artifacts (starter, checkpoint, final)

• notebooks/07-visualization-communication/
  Visualization for decisions + communicating uncertainty

• notebooks/08-wrap-up/
  Reflection + final takeaways

• templates/
  README templates, project scaffolds, submission checklists

• rubrics/
  Grading rubrics

• data/sample/
  Tiny sample data only (do not commit large datasets)

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How to Export from Kaggle (30 seconds)

In Kaggle:

1. Open your notebook
2. Click File → Download notebook
3. Move the .ipynb into the correct folder in this repo
4. Commit & push

Tip: keep a consistent commit message pattern, e.g.:

• Lab 2: API ingestion
• HW 3: Pagination + retries
• Midterm checkpoint

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Collaboration Rules (Short and clear)

• You may collaborate as allowed by the assignment.
• You may discuss approaches and debug together.
• You may NOT copy/paste someone else’s finished notebook or text UNLESS you are working in a group. I'll let you know when.

When in doubt: ask in Teams.

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Need help?

• Ask in Teams first (so others benefit)
• If it’s personal/private: DM the me

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Templates you should use

• Project README template: templates/PROJECT_README_TEMPLATE.md
• Submission checklist: templates/SUBMISSION_CHECKLIST.md

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Last updated: Dec 2025.