Author: Christian Rudder
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Summary: This was a very light, accessible look at data analysis which answers some interesting, but often obvious, questions about how we date and how we describe ourselves online.
As one of the creators of the dating site OkCupid, author Christian Rudder has a fascinating dataset to play with. In combination with data acquired from other data-collecting websites (Facebook, Google, etc), he’s able to ask and answer some very interesting questions. For instance, who do people want to date? And, more interestingly, how does this compare to who they say they want to date? Does the way people describe themselves and the way that people respond to them vary by ethnicity? By age? Even questions that people might not answer accurately can begin to be answered here.
There were a lot of things I liked about this book. The author came across as personable, funny, and enthusiastic about the questions one can answer about big data. His humor was occasionally too negative for my tastes, involving putting one group or another down, but the rest of the time, his sense of humor worked for me. I liked that the author made sure to point out the limitations in his data set (most obviously, anyone involved must use the internet and is almost certainly single). The graphs didn’t work in my eARC, but his descriptions were clear enough that I could get by without them, which I think speaks well of the clarity of the author’s descriptions in general. I thought he did a great job explaining the challenges data analysts face in way that could be interesting and accessible to a general audience.
The downside to this book, for me, was that it was very light. Most of the data analysis seemed simple and obvious. It wasn’t any more complicated than making graphs and the answers seemed obvious too. For example, I didn’t find it at all surprising that most men would prefer to date women under 21 while on average women’s preferred partner’s age increases as they age. One exception was his comparison of words used most often by different ethnicities compared to people of other ethnicities, which I thought was sometimes surprising and also very clever. Overall though, in term of both data analysis techniques and questions addressed, this book felt like a very light version of The Signal and the Noise. This isn’t necessarily a bad thing. The questions addressed were interesting even if the answers were ofent intuitive and the lighter introduction to data analysis could be a great place to start if it’s a subject you’d like to know more about.
Do you ever read nonfiction related to the work you do? Do you find it hard to find books that strike that right balance between being too academic and too light for you?