README.md

Project #4: Executive Summary

DS | Unit Project 4

PROMPT

In this project, you will summarize and present your analysis from Projects 1-3. Your goal is to complete your ongoing iPython Notebook, combining prior deliverables from projects 1-3 with an appendix and executive summary. This will be a final overview of your analysis that incorporates past feedback and demonstrates your approach.

Goal: Write up an executive summary that outlines your findings and the methods used in projects 1-3.

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DELIVERABLES

iPython Notebook with Executive Summary

• Requirements:

  • “Specific aim”

  • Outlined methods and models

  • Articulation of datasets

  • Risks and assumptions

  • Goals and criteria for success

  • A exploratory data analysis overview with visualizations and statistical analysis

  • A writeup on the interpretation of findings including an executive summary with conclusions and next steps

• Bonus:

  • Always consider your audience - what do they already know vs. what should you define or explain?

  • Visualize your findings! Use charts, graphs, or other images as appropriate.

• Submission:

  • TBD by instructor.

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TIMELINE

Deadline	Deliverable	Description
Lesson 11	Project 4	Notebook with Executive Summary

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EVALUATION

Your project will be assessed using the following standards:

1. Present Results

2. Professional Development

Rubric: Click here for the complete rubric.

Requirements for these standards will be assessed using the scale below:

Score | Expectations
----- | ------------
**0** | _Incomplete._
**1** | _Does not meet expectations._
**2** | _Meets expectations, good job!_
**3** | _Exceeds expectations, you wonderful creature, you!_

While your total score may serve as a helpful gauge of whether you've met project goals, specific standards scores are more important since they can show you where to focus your efforts in the future!

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RESOURCES

Dataset

We'll be using the same dataset as UCLA's Logistic Regression in R tutorial to explore logistic regression in Python, as explained in yhat's blog. This is an excellent resource for using logistic regression and summary statistics to explore a relevant dataset. Our goal will be to identify the various factors that may influence admission into graduate school. It contains four variables- admit, gre, gpa, rank.

• 'admit' is a binary variable. It indicates whether or not a candidate was admitted admit =1) our not (admit= 0)

• 'gre' is GRE score

• 'gpa' stands for Grade Point Average

• 'rank' is the rank of an applicant's undergraduate alma mater, with 1 being the highest and 4 as the lowest

Dataset: Admissions.csv

Starter code

Open the iPython Notebook and follow the questions provided.

Suggestions for Getting Started

• Review your previous work and any prior instructor feedback.

• Look up sample executive summaries online.

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