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README.md

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Guided Project: Visualizing Real World Data

Overview

The goal of this project is to practice creating and interpreting different types of visualizations using real world data.

You will be working individually for this project, but we'll be guiding you along the process and helping you as you go.

Technical Requirements

The technical requirements for this project are as follows:

  • Select a dataset from a public source.
  • Create a Jupyter noteboosk to analyze the data
  • Using your data, create a minimum of one scatter plot, one histogram, one box plot and one bar graph (you can add more than one visualization of each type of you choose). Graphs should contain the proper labeling of the x and y axis when appropriate as well as a title for the graph.
  • Explain what insight or information is inferred from these visualizations. The explanation should be in the notebook in markdown cells.

Necessary Deliverables

The following deliverables should be pushed to your Github repo for this chapter.

  • A Jupyter notebook containing your analysis and the code you used to obtain this analysis.
  • A data folder containing your data set.

Suggested Ways to Get Started

  • Find a data set to process - a great place to start looking would be Awesome Public Data Sets, Kaggle Data Sets, or the UCI Machine Learning Repository. A great new source is the Google dataset search.
  • Perform Preliminary Analysis - use functions like describe to help guide you to the correct insight and data visualization.
  • Use the tools in your tool kit - your knowledge of the different types of visualizations and when to use them should come in handy with this assignment.
  • Consult documentation and resources provided to better understand the tools you are using and how to accomplish what you want.

Project Feedback + Evaluation

  • Technical Requirements: Did you deliver a project that met all the technical requirements? Given what the class has covered so far, did you build something that was reasonably complex?

  • Creativity: Did you add a personal spin or creative element into your project submission? Did you incorporate domain knowledge or unique perspective into your analysis.

  • Code Quality: Did you follow code style guidance and best practices covered in class?

  • Total: Your instructors will give you a total score on your project between:

    Score Expectations
    0 Does not meet expectations
    1 Meets expectations, good job!
    2 Exceeds expectations, you wonderful creature, you!

This will be useful as an overall gauge of whether you met the project goals, but the more important scores are described in the specs above, which can help you identify where to focus your efforts for the next project!

Presentation Guideline and Criteria

Format

  • Presentation Time: 6 minutes
  • Q & A: 3 minutes
  • Total Time: 9 minutes

Attire

Outputs

  • Present your findings using the Jupyter notebook.
  • The presentation and demo will be executed on a class computer (instead of your own).
  • Be prepared to explain your entire notebook and thought process.

Things you might want to talk about

  • Short presentation of yourself:
    • Who are you?
    • Your favorite thing about this course so far
    • Note: we are getting you ready for final presentation!
  • Introduction
    • Describe the dataset you chose
    • Why did you choose that dataset?
    • The most important thing you learned.
  • The code
    • Be prepared to explain your code as well as markdown comments
  • The visualizations
    • What Python library you chose for each visualization and why
    • How did you implement the visualization
  • Your insight
    • Explain what insight you derived from the visualizations and what this tells us about the data