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Milestone 8: Archive

For your final project, you will create 2 things:

1. an artifact that decision-makers (your intended audience) could use to inform a decision you define.
2. a technical description which other data scientists could use to understand and build off of your analysis

Deliverable 1: Artifact

The target audience for the artifact will be the decision makers in your decision context. They should be able to interpret and engage with your artifact to help them make more informed decisions. The challenge to making an effective artifact, much like making an effective visual, is about ensuring expressiveness and effectiveness by balancing what detail to show. Your target audience should know just enough to be more confident they are making a decision that better optimizes what they want. They may be confused or misled by excessive detail or make incorrect conclusions with insufficient detail. Finding the appropriate balance is dependent on your decision context and target audience. Your artifact might be a Shiny app, a web page made from an R markdown document, or another kind of artifact to communicate your results to decision makers.

Deliverable 2: Technical Description

The target audience for the technical description is other data scientists who wish to interpret, critique, and build off of your work. It will be a written report in the form of an R markdown document; you should link to a compiled html version in your Github repository, from a page on your GitHub wiki and should provide sufficient detail so somebody can understand your analysis and interpret it.

Grading Criteria

From the perspective of a decision-maker (your intended audience), we will your artifact as follows:

• 1 point: I understand the decision this artifact is supposed to inform.
• 0.5 points: I understand the method for supporting the decision and why it is appropriate.
• 0.5 points: I understand limitations for the method.
• 2 points: I understand how to use the results/method to inform the decision.
• 1 point: The design and presentation has a high quality feel (formatting, aesthetics, information design).
• 1 point: Visualizations are expressive and effective to intended audience.
• 1 point: I understand the impact of different choices on potential outcomes for the decision.
• Extra credit (up to 2 points): This artifact is complete and polished enough to inform real decision-makers.

From the perspective of another data scientist, we will grade your technical description as follows:

• 1 point: I understand who the target audience and decision context is for this artifact.
• 1 point: Dataset and variables (predictive, outcome) are described.
• 0.5 points: Data cleaning process is described.
• 0.5 point: Team justifies use of method for supporting the decision.
• 4 point: Analysis is high quality and thorough.
• 0.5 point: Assumptions and limitations clearly discussed.
• 1 point: Project is well-documented (comments in code, wiki).
• 0.5 points: Potential future work is described.
• Extra credit (up to 1 point): Team used a method not taught in class to improve their analysis and justified their choice to use this method

Submission

Create a wiki page with links to your final artifact and technical descriptions (or instructions about how to access them). Then submit two links on canvas in the Milestone 8 assignment (only one person per group needs to do this) : 1) link to the file in your github repo to open and run to run your artifact, and 2) link to the file in your github repo that is your technical description. You should add a link to a pdf version of the technical description in case it doesn't run when we clone your repo.