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YStrano
GitHub Repository: YStrano/DataScience_GA
 Path: blob/master/april_18/projects/unit-projects/project-1/README.md
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Project #1: Research Design Write-Up

DS | Unit Project 1

PROMPT

"A problem well-stated is half-solved" -- Charles Kettering

Welcome to Data Science! In this first project you will create a framework to scope out data science projects. This framework will provide you with a guide to develop a well-articulated problem statement and analysis plan that will be robust and reproducible.

Goal: Create a structured iPython notebook using markup

Note: You'll be working with two scenarios in this project and one dataset.

DELIVERABLES

Completed iPython Notebook

  • Requirements:

    • Identify features of the dataset, including the outcome and covariates/predictors

    • Create a data dictionary with classification of available variables

    • Write a high quality problem statement

    • State the risks and assumptions of your data

    • Outline exploratory analysis methods

  • Bonus:

    • Practice writing an alternative problem statement for your dataset

    • Articulate the risks and assumptions of this alternative model

  • Submission:

    • TBD by Instructor

TIMELINE

DeadlineDeliverableDescription
Lesson 3Project 1Research Design Write-Up

EVALUATION

Your project will be assessed using the following standards:

  1. Identify the Problem

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 is a helpful gauge of whether you've met overall project goals, specific scores are more important since they'll show you where to focus your efforts in the future!

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

For this project we will be using an iPython notebook. Notebooks are a handy way to communicate your research with your team and share your analysis. Using markup syntax will allow you create more visually appealing notebooks.

Sample Deliverables

Check out the example notebook in our starter code, which includes a data dictionary and responses to questions. Wonder how to format your notebook the same way? Simply double-click on any section to view the markdown.

Example Notebook

Suggestions for Getting Started

  • Get used to the iPython Notebook layout. Play around with keyboard shortcuts.

  • Try out basic markdown for commonly used formats; look up commands for headers, bold, italic, tables.

  • Read the docs for iPython Notebooks. Most of the time, there is a tutorial that you can follow, but not always, and learning to read documentation is crucial to your success as a data scientist!