Data Science: Capstone Rubric for Part 2

Data Science | Capstone, Part 2

Your project criteria were derived from the following standards, taken from our data science lifecycle:

• Frame The Problem
• Prepare and Explore Your Data

Acceptable performance for these standards is based on how well you’ve performed the specific requirements listed below.

Performance Evaluation

RUBRIC

| Score | Interpretation | | — | — | | 0 | Project fails to meet the minimum requirements for this item. | | 1 | Project meets the minimum requirements for this item, but falls significantly short of portfolio-ready expectations. | | 2 | Project exceeds the minimum requirements for this item, but falls short of portfolio-ready expectations. | | 3 | Project meets or exceeds portfolio-ready expectations; demonstrates a thorough understanding of every outlined consideration. |

REQUIRED

1. Find and Clean Your Data: Source and format the required data for your project.
   • Create a data dictionary
2. Perform preliminary data munging and cleaning of your data: organize your data relevant to your project goals.
   • Review data to verify initial assumptions
   • Clean and munge data as necessary
3. Describe your data: keep your intended audience(s) in mind.
   • Document your work so far in a Jupyter notebook

BONUS

1. Document your project goals (revise from your initial pitch)
   • Articulate “Specific aim”
   • Outline proposed methods and models
   • Define risks & assumptions
2. Create a blog post of at least 500 words that describes your work so far. Link to it in your Jupyter notebook.