Assignment I

Visualization basics

The purpose of this assignment is to learn how to create visualizations using ready-made tools. One of the tools for this class will be the Tableau software package. Tableau automates the creation of visualizations and uses many of the proper visual encoding techniques that we will discuss during the course by default. A free alternative to Tableau is Lyra. You are welcome to use this instead, but we will not provide any support/help.

ToDos before March 3rd (tutorial in class)

You will need to bring a laptop with Tableau installed on it in order to participate in the tutorial exercises during class. Therefore, please have Tableau installed on your machine before the tutorial on March 3.

1. Install Tableau

Tableau content that is published to Tableau Public on the web can be viewed in your web browser regardless of your operating system. However, to author and publish views and workbooks, you use Tableau Desktop Public and Professional Editions.  

2. Please read

3. Connect to data

To make sure you are all set for the tutorial try to connect to an excel sheet (e.g. the assignment dataset, see link below).

It works? -> Perfect

It didn’t? -> Try to fix it – maybe a driver is missing (see link above)

Homework (due March 7)

Create a Tableau dashboard, containing at least 2 to 3 views of the data, using the auto recall dataset from the NHTSA website. A view is a single graphical representation of the data (i.e. a chart). These views should ideally be linked and show different aspects of the data.

Some background: Cars are recalled for a variety of reasons. Some are more serious than others. It would be interesting to see if there are any trends with regards to the manufacturing date, manufacturer, model, or components. If you want to incorporate additional defect datasets. These data and their descriptons are on the NHTSA downloads page.

Use Tableu to explore the NHTSA/ODI dataset and find more questions and answers. You could also try and correlate the recalls to, for example, complaints and defect investigations to recalls or the manufacturer's stock price. Feel free to incorporate any additional datasets to help tell your story.

The recall dataset can be downloaded from here. It is a tab-delimited file. The field descriptions from ODI are located here.


We will evaluate the effectiveness of your visualization for communicating the fundamental aspects of the data set. Does it give the viewer a good understanding of the different characteristics of the data? Here, we are looking for both effectiveness and creativity. We do realize that people have differing levels of design ability and experience. Here, we are looking for a good effort, not necessarily some conference paper-worthy new idea. The purpose of this assignment is to provide you with experience in the analysis of data like this and the design of visualizations to present the data.

Please also include a short writeup (2-3 pages) that describe the reasoning process that led to your final visualizations. This writeup should discuss alternative ideas you had, insights you gained from the data, and why you went with your final design choices. This writeup will be a major part of the grade on the assignment so take care to clearly explain your ideas. Please also include screenshots or sketches where necessary to make your point. You can also include a short reflection on your experiences with Tableau itself. For example, any difficulties you had with making exactly the view you wanted or the ease of prototyping.

Please save your visualization as a "Packaged Workbook." There is an option in the save menu. This will save the data with the workbook so that both visualizations and data are packed up in one file that you submit. Failure to do this will result in a 10 point penalty.

We will grade your submission and presentation with the following scheme:

The report and worksheet should be uploaded to Moodle by 23:59 on the day the assignment is due.

Late submission

Late Submissions are possible, yet they will be penalized. Academic Honesty