This assignment will familiarize you with the commercial information visualization system Tableau.

The goals of the assignment are for you to learn the capabilities provided by the system, learn the visualization methods it provides, and assess its utility in analyzing information repositories. You will work with some provided data sets in the assignment. Think about the kinds of questions that an analyst would be asking about the data sets.

The assignment has four parts:

1. Gain familiarity with the system
Familiarize yourself with the visualization techniques and the user interface of Tableau. It has a helpful tutorial video you should watch (stored in t-square). Watch the video to help become familiar with the system, its interface and its capabilities.

2. Examine the sample data sets
Each tool includes a few sample data sets, but often it's best to learn with something new. Three data sets are supplied the Resources page of t-square for you to consider: foods' nutritional data (5976 items, 32 variables), baseball statistics (322 items, 24 variables), and computer science professors (2195 items, 9 variables). You are free to pick the one set that is most interesting to you. Briefly scan the text of the file and familiarize yourself with the variables. Generate and write down (you will need to turn them in) a few hypotheses to be considered or questions to be asked about the data elements. Think about all the different kinds of analysis tasks that a person might want to perform in working with data sets such as these. For instance, someone working with a data set about breakfast cereals might have tasks like:

• Do the different companies producing cereals have different styles of cereal that they favor?
• Does high fat mean high calories?
• What cereals would you recommend to someone on a diet who still wants some good taste?
• Does the nutritional value of cereals vary a lot? If so, how?
• etc.

3. Load and examine the data set into the Tableau
Load your chosen data set into Tableau, then consider your hypotheses, tasks, and questions. Also use the system to explore the data set and see if you can discover other interesting or unexpected findings in the data. Put yourself in the shoes of a data analyst, and consider questions that such a person would confront.

4. Write a report on your findings
Your report should consist of three parts.
Part 1 - List your three questions and provide answers to each, along with a screen shot showing the visualization you used to answer each question. Allow one page per question, including screen shot and narrative. Each question should be answered with a different visualization, so that makes three different visualizations.
Part 2 - List one unexpected finding, insight, or discovery made about the data while exploring the dataset with the tool. Explain how the system helped to facilitate the finding. This should take about a paragraph.
Part 3 - Critique the system. What are the system's strengths and weaknesses? For what kinds of user tasks is the system particularly well suited? Focus more here on the visualization techniques as opposed to the particular user interface quirks, though you should feel free to comment on UI aspects when they are particularly good or bad. Describe characteristics of the UI using the concepts and terminology you have learned in class. Together, the second and third parts should be close to 2 pages.

Submission: Your document should be in PDF format and is limited to a maximum of 5 pages, no cover sheet. Use Times Roman 11 point type with normal margins, 1.5 line spacing. Remember to put your name on it. Submit the paper via T-Square. It is due at 1 pm on Friday October 6.

Acknowledgments: Tableau's data visualization software is provided through the Tableau for Teaching program. We thank Tableau for making the system available to students in class.