CS 725/825 - Information Visualization
Spring 2018: Wedesdays, 9:30am-12:15pm, E&CS 2120

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In-Class Work 1 (ICW1)

The goals of this exercise are

  • to learn how to recognize data set and attribute types, and
  • to learn how to generate data analysis questions and transform data in ways that enable you to answer them.

(This assignment is based on the Data Abstraction exercise from Enrico Bertini, http://enrico.bertini.io/teaching/)

How do you know if you are on the right path? You can easily generate questions out of data, figure out how data needs to be transformed in order to answer the question, and identify what data set type and attribute types your data transformation has generated.

Gather into groups of 2-3 students to work this exercise.

  • suggestion: use this as an opportunity to meet someone who you don't already know

Choose a group representative who will present your findings to the class when we gather back together.


For this exercise we will use data from AidData, an organization co-founded by the College of William & Mary that tracks $40 trillion in funding for development. We'll be looking at a 500-line sample of the data (Google Sheets document, available through your ODU MIDAS Google account). You can find more information on the dataset in the user guide and Data README. The high-level purpose codes come from the OECD CRS purpose codes.

Here is an excerpt from the data set userís guide that summarizes the meaning of the fields:


Step 1

Look over the user guide and Data README to familiarize yourself with the data set

  • What is the meaning of each field in our dataset?
  • Is there any other important information you learned?
  • Do you have any doubts/questions?

Step 2

Perform an initial data abstraction step on the provided data set

  • What is the data set type?
  • What is the attribute type for each column/field?

Step 3

Write 5 questions you would like to answer with this data set.

Step 4

For each question you generated in Step 3, write the following information:

  • Do you need a chart in order to answer this question?
    • If not, the question does NOT count.
  • Which fields do you need to use to answer the question?
  • Do you need to transform the data in order to answer the question? If yes, what transformations are needed?
  • Do data set type and attribute type change when you need to transform the data? If yes, how do they change?


  • What did you learn in this exercise?
  • How is this going to be useful in visualization design?