Welcome to CSC444, Data Visualization. In this course, you will learn how, and why, to create data visualizations.
A “visualization” is simply a visual representation of an object of our interest. It’s visual: we consume them with our eyes, and so it is essential that we know how our eyes work — and, more importantly, the parts of our brains connected to our eyes. It’s also a representation; we get to choose what this representation will be, and different choices lead to different pictures, some good and some bad. We will learn how to tell those apart, and how to make pictures that are more good than bad.
Good data visualization involves perceptual psychology, mathematics, and computer science. This makes our subject uniquely challenging: sometimes the way our eyes work stands in way of applying some beautiful result from computer science. Sometimes it’s the other way around: something deep about the math in the data will help guide the design process and let us make a picture that is beautiful, informative, and truthful.
The content of the course is split roughly in three distinct aspects: mechanics, principles, and techniques.
Data visualization itself has existed for at least 200 years; we’ll learn about Playfair, Nightingale, Minard, and others. Statistics in the 1900s, computers in the 1950s; exploratory analysis. From the 1960s on, we started to realize that some things in visualization work better than others, and around 1980 scientists started seriously studying the effectiveness of data visualization as a medium itself. This program goes on to this day. To give a few examples, we know that using positions works better than using angles; we know that using length works better than using area. We know that, in some cases, using color intensity works better than color hue (and that in other cases, it’s the other way around).
We also know, since the 1960s, that interaction is a powerful idea. Back then people interacted with a data visualization by carefully rearranging bits of paper (we didn’t have supercomputer in our pockets then!), but many of the original thoughts are still valid. We will learn the basics of interactive visualizations.
Although much of what we know about visualization is finicky and specific, we have some general principles. We will spend about four weeks studying these principles.
In comparison to the relative paucity of principles, data visualization has an enormity of existing techniques. We will spend about six weeks in this course going over existing techniques, and what kinds of data they apply to.
Here, computer science has much to say about data visualization.
For example, not everything we want to do with data is efficient, and not everything that is efficient is worth doing with data. This means that the practice of data visualization needs to be informed by algorithmic constraints.
Data visualization also interacts with software engineering: not every visualization algorithm plays well with the rest of the code in your program and in your head.
When we talk about mechanics, we mean the practical things you will need to learn in order to create data visualizations. In this course, we will use the web software stack. This means making visualizations through web pages, using HTML, CSS, and Javascript. The main domain-specific tool we’ll learn is d3.
The modern web stack is good, bad, and ugly. We will spend about four weeks in this course learning how to use it to make visualizations.
In this course, we will handle data from many different sources. As we will see, data is dirty: standard formats are not really standard, sometimes there’s missing information in files, there’s weird data points that don’t belong (outliers), etc.
We will learn to do basic exploratory analysis in data: specifically, we will become comfortable with digging into a dataset and playing around with it to see what’s there. As we will see, data cleaning makes for better visualization, and visualization also makes for better data cleaning.
We will do one week of data cleaning and exploratory data analysis.
There is no required textbook for the course, but I strongly recommend Tamara Munzner’s book:
This book is available for electronic checkout at the UA library, and I have a copy I intend to keep in my office at all times, in case you want to browse it before deciding to buy it. Other excellent books I recommend include (again, ask me to take a look at them if you’re curious):
If you want to dive deeper into visualization, you should have read, at least once, the following books:
For the practical portions of this course, Scott Murray’s Interactive Data Visualization for the Web is excellent. The book used to be freely available online but, alas, that’s no longer the case. However, if you’re on campus, you should have access to an electronic version of it on O’Reilly Safari.
Sometimes we will need additional reading. When you’re expected to read material ahead of time, the material will be posted on the course web page, and will be discussed in class.
By the end of this course, you will have the skills to create many of these visualizations yourself, to tell whether they are a good or a bad design, and why.
The Periodic Table. You might not think of the Periodic Table as a good visualization, or even as a vis at all! Still, it’s a really great one. The spatial arrangement of the elements make your eyes think for you: electron affinity up and to the right; metallic character down and to the left; etc.
A hairball. We’ll learn in the Principles section why this is a terrible visualization, and in the Techniques section how to create something better.
Jonathan Corum’s Visualized 2014 keynote. See also this implementation of the exoplanet visualization.
Mike Bostock’s blocks collection.
Mike Bostock’s Visualizing Algorithms, for fantastic examples of how to use visualization to understand behavior, not data.
Steven Wittens’s how to fold a Julia fractal, for examples of how to use visualization to understand mathematics.
Bret Victor’s Up and Down the Ladder of Abstraction, for ideas on how to use interaction and visualization to understand behavior.
Grant Sanderson (“3blue1brown”) creates best-in-the-world explanations of mathematics, physics, and the world through data visualizations.
Observable is Bostock’s latest project: “Discover insights faster and communicate more effectively with interactive notebooks for data analysis, visualization, and exploration”.
