GARY WHEELER

MAIA NGO

You have made it half way through the course work!

The goal of this project is to test your ability to gather information from a real-world database and use your knowledge of statistical analysis and hypothesis testing to generate analytical insights that are valuable.

• For this project, you may work with the Northwind database--a free, open-source dataset created by Microsoft containing data from a fictional company. You probably remember the Northwind database from our section on Advanced SQL. Here's the schema for the Northwind database: Schema
• You can also use data of your own finding that is interesting. This should be in the form of a database if possible, but please find a data set that will lead to interesting hypotheses(questions).

• Your team must prepare a 5-10 minute presentation detailing the statistical analysis performed.
• Be sure to specify both the null hypothesis and the alternative hypothesis.
• You should also specify if your hypothesis is a one-tail or a two-tail test.
• Your presentation must provide at least three hypotheses(questions) and outline the process you went through to test the hypotheses.
• Use at least 4 meaningful data visualizations to help illustrate your findings.
• Any additional statistial analysis used to reach conclusions(Power, sample size, effect size, sampling, and other statistical analysis)
• No more than 8 slides.

Be prepared to answer questions such as:

• "why did you select your data?"
• "why did you pick the question(s)?"
• "why are these questions important?"
• "What are type I and II errors associated with your hypotheses?"
• "how did you decide on the statistical analysis carried out by your group and what did you learn from them?"
• "how did you decide on the data cleaning options you performed?"
• "why did you choose a given method or library?"
• "why did you select those visualizations and what did you learn from each of them?"

• Use the data provided or one of your own
  • Establish naming conventions for variables and datasets
  • Clean dataset
    • You may use Pandas or Python functions
    • Document your data cleaning process
• Use Scipy and/or Statsmodels perform meaningful statistial analysis on your data set. You may also use your own or prewritten functions.
  • Carry out at least three hypothesis test using the statistical test from lectures and learn.co (ANOVA, t-test, etc).
  • Carry out any further statistical analysis such as power analysis, sampling, effect size, etc.
• Posted to git repository:
  • A README.md listing project members, goals, responsibilities, and a summary of the files in the repository
  • At least 10 commits
    • Must include short, descriptive commit messages
    • Each project member should commit at least once
  • A Jupyter notebook targeted to a technical audience that contains
    • Clean and commented code so an independent party can replicate your analysis and justify your analytical choices
    • Your final joined and cleaned dataset that was used for analysis.
    • The packages or methods used to perform stastistical analysis.
  • A narrative Jupyter notebook targeted to a non-technical audience that provides:
    • The purpose of your analysis and why it matters
    • 4 well annotated visualizations
    • Statement of your hypotheses and conclusions
  • A pdf of 8 - 10 slides used in a presentation targeting non-technical audience
    • Apply consistent and effective formatting to create a “professional” appearance
    • Write an abbreviated high-level overview of methodology and statistical analysis performed
    • Present at least 3 hypotheses and concrete recommendations from conclusions drawn from hypothesis testing
    • include exported visualizations from analysis
    • Target the presentation to a non-technical audience, avoid jargon
    • Take 5 - 10 minutes to present

• Group A: Gary Wheeler + Maia Ngo
• Group B: Thoa Shook + Christiaan Defaux
• Group C: Princess Otusanya + Patrick Kim
• Groups are to work independently without outside consulting

07/31 Wednesday - Project Assignment

• schedule Thursday check in with coach

08/01 Thurday - Check in with coach

• review data cleaning
• provide url of project repository
• review at least one table/chart
• one hypothesis and how you plan to test it.
• review work plan created for how teammates will approach and divide work

08/02 Friday - Demo presentation with feedback from instructors

• have polished draft completed
• have polished version of jupyter notebook completed

08/5 Monday

• afternoon project presentation

If any requirements are missing or if significant gaps in understanding are uncovered, be prepared to do one or all of the following:

• Perform additional data cleanup, data visualization, and statistical analysis
• Submit an improved version
• Meet again for another Project Presentation

What won't happen:

• You won't be yelled at, belittled, or scolded
• You won't be put on the spot without support
• There's nothing you can do to instantly blow it