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Final Rubric 30 pts total (out of 32 available)

  • 2 pts proposal.md turned in on time and with team member names, background, dataset, and clearly outlines 3 plausible research questions.

  • 2 pts: Reproducibility check passes, badge included in README

  • 3 pts: Demonstrates use of GitHub (commits, customized README, readable ipynb notebooks)

  • 2 pts: Demonstrates reproducible access raw data directly into python. (e.g. data is read from original sources by URL)

  • 5 pts: Demonstrate clean, concise, readable code. Avoids unnecessary, unused or commented-out blocks. Uses meaningful variable names. Avoids repeatedly re-assigning different values to the same variable. Takes advantage of high-level libraries (ibis, seaborn.objects) in place of more verbose legacy methods (matplotlib, pandas).

  • 5 pts: Demonstrates effective data visualization. Figures are appropriately labeled (axes, titles, captions). Demonstrate delibrate choice of layout, avoids clutter or overplotting that makes visuals hare to read.

  • 5 pts: Demonstrates clear and detailed prose. Clearly presents Scientific background, description of methods, results, discussion, conclusion. Cites references when appropriate. Shows clear understanding of scientific context and implications. Discusses uncertainty and unresolved issues appropriately.

  • 3 pts: Statement on AI use. At the end of your writeup, please document and reflect upon any use of AI (language models) in your final project. Did you try asking models for help with code? If so, which models? Was it helpful or not helpful? Did you use LLMs for any other component of the project? Why or why not?

5 pts: Demonstrates at least two of the following deeper expertise areas

  • A spatial data visualization with at least 2 map layers
  • demonstrates the effective use of at least 5 methods from ibis (e.g. .select(), .filter(), .group_by(), .aggregate(), .mutate(), .order_by() etc).
  • demonstrates familiarity with advanced seaborn.objects plots (use of facet, plots with custom layouts, multiple layer types (points, lines, bars, etc)
  • demonstrates use of AI function calling / tool use
  • Publishes to an additional output type:
    • Web map published to github-pages
    • Streamlit application published to huggingface
    • PDF output
    • Website published to github-pages

Extra credit may be awarded for exceptional work in any of the above categories.