It’s Banned Book Week! The American Library Association observes Banned Book Week the last week of September to celebrate intellectual freedom. Thanks to author Neil Gaiman (@neilhimself) for retweeting, or I wouldn’t have heard of it.
I’ve decided to spread the joy with a little celebration of my own. We’re having a book review party here at MidnightDBA! Now, this is a technical blog, and I don’t know of any tech books that are in danger of being banned, but we won’t discriminate.
We begin with Beautiful Data: The Stories Behind Elegant Data Solutions. It’s 20 different stories about data – gathering, planning, interpreting, storing, visualizing, etc. I’d like to go through and comment on every story in the book, but then this would be a Cliffs Notes, not a review. Let’s have some highlights:
In “Seeing Your Life in Data”, Nathan Yau tells about developing two projects: “the Personal Environmental Impact Report (PEIR), a tool that allows people to see how they affect then environment… and your.flowingdata (YFD), and in-development project that enables users to collect data about themselves via Twitter”. That in itself is cool; users simply sent formatted tweets (“ate salad”) to track mood, eating, or what have you, and then interact with the data on the site. The difference in the data collection for the two systems is also an interesting discussion, and I liked the insight into the process for choosing the best PEIR visualization.
I think my favorite chapter is titled “What Data Doesn’t Do”, by Coco Krumme. To break away and talk about me for a moment (and isn’t everything, in the end, about me?), I subscribed for some time to a LSAT Logic in Real Life podcast, which explored the fallacies behind our reactions to common or current events. I really enjoyed learning the names and methods of misplaced logic and biases. “What Data…” struck me in a very similar vein. I’ve been trying hard not to quote this chapter, for fear that I’ll just type it out. Still, I can’t resist my absolute favorite paragraph. It begins with the header “Data Alone Doesn’t Explain”
People explain. Correlation and causality, you may have heard, make strange bedfellows. Given two variables correlated in a statistically significant way, causality can work forward, backward, in both directions, or not at all. Statisticians have made a hobby … of chronicling the abused of correlation, like old ladies clucking at the downfall of traditional values in the modern world.
Beautiful. Again, I’d love to give a review of each chapter, but then you’d fall in love with my writing instead of Beautiful Data. Yeah, of course you would.
Finally, and most shallowly, it’s a really pretty book. Check out the cover art! And that’s without considering the 70 color plates, including everything from user surveys and line charts, to laser data and DNA. One, two…that’s 33 words to effectively say, “Pretty pictures!”
Let’s be serious for a moment, though. This book was, to me, truly extraordinary and truly entertaining. I read it in pieces over the course of a few weeks, and it was lovely to take in one story – one angle on data problems or applications – and muse on it on and off until I had a few minutes to read the next. It’s a book that lends itself to piecemeal reading, jumping around, and rereading at will. And it’s one I recommend not just to IT pros, but to everyone.
– Jennifer McCown, http://www.MidnightDBA.com