Data Science: Capstone Rubric for Part 4

Data Science | Capstone, Part 4

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

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. Begin with an executive summary:
    • What is your goal?
    • What are your metrics?
    • What were your findings?
    • What risks/limitations/assumptions affect these findings?
  2. Walk through your model step by step, starting with EDA.
    • What are your variables of interest?
    • What outliers did you remove?
    • What types of data imputation did you perform?
  3. Summarize your analysis, including:
    • model selection
    • implementation
    • evaluation
    • prediction/inference
  4. Clearly document and label each section
    • Logically organize your information in a persuasive, informative manner.
    • Include notebook headers and subheaders, as well as clearly formatted markdown for all written components.
    • Include graphs/plots/visualizations with clear labels.
    • Comment and explain the purpose of each major section/subsection of your code.
      • Document your code for your future self, as if another person needed to replicate your approach
  5. Host your notebook and any other materials in your own public Github Repository.
    • Include a technical appendix, inlcuding links and explanations to any outside libraries or source code used.
    • Host a local copy of your dataset or include a link to a remotely hosted version.

BONUS

  1. Describe how this model could be put into production. Consider:
    • How could you continue to validate your model’s performance over time?
    • What steps might you need to take to productionize your model for an enterprise environment?
    • How would you deploy your model publicly? What could you do to setup your model and share it online right now?
  2. Create a blog post of at least 1000 words summarizing your approach in a tutorial format and link to it in your notebook.
    • In your tutorial, address a slightly less technical audience; think back to Day 1 of the program - how would you explain and walk through your capstone project to your earlier self?