One of my summer projects is to develop the curriculum for a new Certificate in Analytics and Data Visualization, offered at NYU (link). (If you are interested in teaching these courses, please contact me.) The program aims to give students a balanced training, covering datavis from the perspectives of statistics, graphical design and computer science.

Nathan Yau's new book, *Data Points*, landed on my desk at just the right time. It is a nice overview of the subject of data visualization, and it can serve nicely in our introductory course. The book sits closer to the statistical and design perspectives. Instructors will need to supplement the computer science topics such as interactivity, networks, and online graphics. It is of course difficult to teach interactive graphics from a static textbook. (Yau's previous book, *Visualize This*, has detailed tutorials of most of these techniques. My issue with that book is trying to be too many things at once.)

*Data Points* is a concepts and examples book. It's not a how-to book. There are figures on almost every page, and unlike *Visualize This*, most figures are actual published data visualization projects.

Just for fun, I classified the figures and plotted the result. (Some purely instructive figures are skipped.)

Running from left to right is the order of appearance of the chart within the book. I classified a total of 135 charts. For each chart, I considered whether one or more of 12 adjectives apply. I labeled about 40 charts "useful", "banal", "silly", and/or "engaging".

You can see from this graph that I enjoy the charts in the initial chapters. Up till chart number 50 or so, I find few "banal" charts, and many "engaging" or "amusing" or "artistic" charts. In the second part of the book, there are not many "surprising" or "amusing" charts.

As for "silly" and "baffling" charts, they appear at an even clip throughout. But that represents just my own bias. I also find "useful" charts throughout the book.

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PS. I received a review copy of *Data Points*. Nathan's blog is Flowing Data.

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