The goal of this assignment is to understand better the iterative process of analyzing a dataset, and creating a visualization that tells a story.

Here is one way to start.

• Step 1. Pick a domain that you are interested in.
  This can be any freely available data set. This can be something you got from scraping websites, you derived from an API (e.g. Twitter) or anything that you compiled yourself and you feel passionate about. Feel free to use an example from your own research, but do not pick an example that you already have created visualizations for. A good rule of thumb for the size of the data set is between 100 and 1000 data points (or rows in a spreadsheet). It can be more than that, but less is probably too low for performing an exploratory data analysis.

• Step 2. Pose an initial question that you would like to answer.
  This can be a hypothesis that you have. For example, “The best coffee venues are located in the Mission neighborhood of San Francisco”.

• Step 3. Assess the fitness of the data for answering your question.
  Inspect the data–it is invariably helpful to first look at the raw values. Does the data seem appropriate for answering your question? If not, you may need to start the process over. If so, does the data need to be reformatted or cleaned prior to analysis? Perform any steps necessary to get the data into shape prior to visual analysis. Use the tools we’ve looked at in class.
• Step 4. Apply a level of visual polish
  The final iteration of your visualization is the one you want to edit a bit to get your point across. Remove extraneous information from your visualization, add legends, format any axes, change the typography, etc. The visualization does not have to be polished for a media publication, but it has to go beyond what Tableau and other tools already do for you.

You will need to iterate through steps 1-3 a few times. It may be challenging to find interesting questions and a dataset that has the information that you need to answer those questions.