As with Module 2, we’re providing a breakdown of our grading rubric for each assignment. This time, we also provide some examples for DE 3-2 - some from this assignment, some from previous years - along with the points they earn on the rubric. Ideally, this will clarify our intent with our grading scheme.

Design Exercise 3-1

90: (at least) meets our expectations for A level work on this assignment (i.e., above average for a graduate student)

For upcoming assignments (Module 5 and 6) we will work with a data set about airline flight delays provided by the Bureau of Transportation Statistics. This data set is quite big (there are thousands of flights each day). It is interesting for class because it can be viewed simply as tabular data, or as set or graph data.

The Data

The Bureau of Transportation Statistics has a nice website where you can download the data a month at a time. You can choose many different attributes of each flight to download, each one is well documented.

We’re publicizing the complete rubrics developed for Module 2 assignments to (hopefully) make our grading more transparent, and help you focus your efforts on the course objectives.

Task abstraction is an important concept - but a tricky one to define. Abstraction can mean many things when it comes to task. I realize that in class I have given multiple definitions - and here I want to clarify a bit. I have been using the term “abstraction” when discussing tasks in two different ways. Here I’ll review those two different senses of abstraction that I described in class (they come from the Task Cubes paper that was mentioned in lecture and was optional reading).

In assessing student design exercises, we face some of the same challenges as in general visualization evaluation. And there are some additional challenges: we need to be able to assign “absolute” measures (grades) in a consistent way, provide meaningful feedback, and do this at scale…

A few selected images from Seek and Find 1. These were selected by the TA to show the range of things that students have picked as a response to the prompt: pick any data visualization - but pick one that you encountered recently. Either something from your day to day life, your research, your hobbies, …

Tableau is a commercial data analysis and visualization tool. The company generously provides it for use in classes through the Tableau for Teaching Program. This allows us to provide you a license for the desktop client, as well as access to the web version.

Here, we will give you a brief guide on how to get started with it, and pointers to some resources. We expect students to figure out how to work with Tableau enough for class.

As an experiment, I’d like to offer an optional “reading club” as an opportunity for students to collaborate (asynchronously) around some of the readings.

Action item: by Friday, tell us if you want to participate using the survey: Reading Discussion 1: Opt-In for Reading Discussion 1 (Eyes Have It) (due Fri, Sep 19).

Over the next few weeks (3 modules) we will be working with two different data sets: US Census Data and Life Expectancy around the World. This page describes the data sets and provides access to them.