What has driven women out of Computer Science?

1116-sbn-webdigi-crop.gifCasey yesterday noted this article from the New York Times on the declining number of women who are pursuing computer science degrees. Declining as in “wow, weren’t the numbers too low already?” From the article’s introduction:

ELLEN SPERTUS, a graduate student at M.I.T., wondered why the computer camp she had attended as a girl had a boy-girl ratio of six to one. And why were only 20 percent of computer science undergraduates at M.I.T. female? She published a 124-page paper, “Why Are There So Few Female Computer Scientists?”, that catalogued different cultural biases that discouraged girls and women from pursuing a career in the field. The year was 1991.

Computer science has changed considerably since then. Now, there are even fewer women entering the field. Why this is so remains a matter of dispute.

The article goes on to explain that even though there is far better gender parity (since 1991) when looking at roles in technical fields, computer science still stands alone in moving backwards.

The text also covers some of the “do it with gaming!” nonsense. As someone who became interested in programming because I didn’t like games, I’ve never understood why gaming was pushed as a cure-all for disinterest in programming:

Such students who choose not to pursue their interest may have been introduced to computer science too late. The younger, the better, Ms. Margolis says. Games would offer considerable promise, except that they have been tried and have failed to have an effect on steeply declining female enrollment.

But I couldn’t agree more with the sentiment with regard to age. I know of two all-girls schools (Miss Porter’s in Connecticut and Nightingale-Bamford in New York) who have used Processing in courses with high school and middle school students, and I couldn’t be more excited about it. Let’s hope there are more.

Tuesday, November 18, 2008 | cs, gender, reading  

Visualizing Data Book CoverVisualizing Data is my book about computational information design. It covers the path from raw data to how we understand it, detailing how to begin with a set of numbers and produce images or software that lets you view and interact with information. Unlike nearly all books in this field, it is a hands-on guide intended for people who want to learn how to actually build a data visualization.

The text was published by O’Reilly in December 2007 and can be found at Amazon and elsewhere. Amazon also has an edition for the Kindle, for people who aren’t into the dead tree thing. (Proceeds from Amazon links found on this page are used to pay my web hosting bill.)

Examples for the book can be found here.

The book covers ideas found in my Ph.D. dissertation, which is basis for Chapter 1. The next chapter is an extremely brief introduction to Processing, which is used for the examples. Next is (chapter 3) is a simple mapping project to place data points on a map of the United States. Of course, the idea is not that lots of people want to visualize data for each of 50 states. Instead, it’s a jumping off point for learning how to lay out data spatially.

The chapters that follow cover six more projects, such as salary vs. performance (Chapter 5), zipdecode (Chapter 6), followed by more advanced topics dealing with trees, treemaps, hierarchies, and recursion (Chapter 7), plus graphs and networks (Chapter 8).

This site is used for follow-up code and writing about related topics.