Grading Breakdown

[tbrown122387]

STAT 3250 Final Project Checklist

Git/GitHub Workflow

• GitHub repository created and shared with all group members
• Every student has a GitHub account
• Every student has added their GitHub handle to the class Google Sheet
• Every student has made at least 3 meaningful commits
• Not all commits are direct to main — some work done on feature branches
• Everyone has at least one change merged via pull requests
• Some pull requests include peer review/approval before merging
• At least 20 issues created across the class
• Issues cover project planning, task assignments, bug reports, feature requests, and questions
• Students self-assigned to issues based on interests/skills

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Repository Structure & Documentation

• Canonical Python package folder structure (data/, src/, notebooks/, docs/, tests/, etc.)
• Comprehensive and pretty README.md with:
  • Project title
  • Project goals / description are super clear
  • Package tools usefulness is readily apparent
  • Setup instructions (e.g., pip install -e ".[dev]")
  • Instructions for running demo code
• Docstrings on all major functions
• Inline comments throughout the code
• Package demo code and tests are reproducible on a fresh machine (installable and runnable)

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Code Quality

• Code is modular — broken into functions and/or separate files
• Code runs without errors end-to-end
• Consistent coding style following Python conventions (PEP 8)
• Unnecessary loops avoided — pandas/numpy methods used where appropriate
• Error handling included where appropriate
• Code organization is logical and easy to navigate

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Technical Requirements

Data Manipulation & Analysis (Required)

• Pandas DataFrames used for core data processing
• Data cleaning and preprocessing steps implemented
• Missing data handled appropriately
• At least one data merge, join, or concatenation performed

Numerical Computing (Required)

• NumPy arrays used for mathematical operations
• Statistical analysis or numerical computations performed
• Appropriate NumPy functions and methods used

Advanced Data Operations (at least 3 required)

• GroupBy operations and aggregation
• Pivot tables or data reshaping
• Time series analysis (if applicable)
• String manipulation for text data
• Multi-index operations
• Advanced merging techniques

Testing

• Tests written using pytest for core features
• Tests pass before pull requests are submitted
• Test coverage is displayed on README.md

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Analysis & Insights

• Dataset(s) are accessible, usable, and legal/ethical to use
• Initial data loading and sanity checks completed (columns, types, missingness)
• Exploratory analysis documented in demo code (plots, summaries)
• Analytical methods are appropriate for the research questions
• Meaningful insights drawn and clearly communicated
• Possible findings and methodology are clearly apparent

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Individual Reflection (per student)

• 1–2 pages submitted
• Describes your specific contributions to the project
• Discusses challenges faced and how you overcame them
• Reflects on what you learned about collaborative programming
• Describes technical skills developed
• Discusses frictions/problems experienced and lessons learned