1A Quantitative detective work
Chapter 1 · pages 20–21 · 377 words
This opening section sets up the guiding metaphor for the whole book: exploratory data analysis (EDA) is detective work. Just as a detective looks for clues, the data analyst looks for numerical, counting, or graphical clues hidden in data. The section insists that doing this well takes two separate ingredients — tools and understanding — and that neither alone is enough.
The detective analogy is spelled out concretely. A detective without fingerprint powder simply cannot find fingerprints, no matter how sharp his instincts; a detective without a sense of where a criminal is likely to have touched things won't know where to dust for prints even if he has the powder. In the same way, an analyst needs concrete techniques (tools) to extract information from numbers, and also a feel for where in the data the interesting information is likely to be hiding (understanding). The book promises to supply some of each, though only for a deliberately narrow selection — a few of the most general and powerful simple tools, not necessarily the objectively "best" ones, since the author doubts a unique best even exists.
The section then draws a distinction about the limits of "understanding." Detective skill needed in a London slum, a Welsh village, among Parisian aristocrats, in the cattle-raising American west, or in the Australian outback are all different — a Scotland Yard officer would not automatically excel at trailing cattle thieves, nor a Texas ranger at working the streets of Birmingham. Likewise, data about earthquakes, chemical manufacturing, firm profits, human hearing, suicide rates, population growth, fossil dinosaurs, fruit-fly genetics, or molecular biology each demand their own specialized, detailed understanding — far more than one book could teach across all fields.
Yet the analogy has an optimistic side too: a Scotland Yard detective, dropped into the wild west or the outback, would still not be useless, because he carries general understandings of conventional detective work that transfer anywhere. The book's ambition is to hand the reader the data-analytic equivalent of that transferable general understanding — a handful of broadly useful habits of mind that work across earthquake data, business data, biological data, and beyond, even without field-specific expertise.
Exhibits and original pages (1)
The next screen shows one question at a time, with the material hidden. You can always bring it back, and doing so before you answer is recorded.