I’m currently participating in the O’Reilly Blogger Review Program – where bloggers are given ebooks of recent publications.
Data Science for Business fits an interesting gap in the market – managers who want to be able to understand what Data Science is, how to recruit Data Scientists or how to manage a data-oriented team. It says it is also for aspiring Data Scientists, but I would probably recommend Andrew Ng’s Machine Learning course and Codecademy’s intro Python course instead if you’re serious about getting your teeth into the field.
Somewhere between an introduction and an encyclopedia, it gives fairly comprehensive overviews of each sub-field, including distinctions that I hadn’t previously thought of so clearly. The authors are mostly unafraid to explain the maths behind the subjects. It dips into some probability and linear algebra – admittedly with simplified notation. There’s no real mention of implementation (i.e. programming the examples) as one would usually expect with O’Reilly; but most competent readers will now at least know what they’re “looking for” perhaps in terms of packages to install or if they want to try and implement a system from scratch. It is certainly designed for the intelligent, professional and far from popular science.
Whilst it is very thorough and interesting it could touch a nerve among Data Scientists, since should a manager of a Data Scientist really have to read a book such as this – surely in such a position of authority they should know of these techniques already? (an extreme example would be one footnote which even contains a description of what Facebook is, and what it is used for). Often, such unbalanced hierarchies are the cause of much unnecessary stress and complication in the workplace. However, this is often the case so perhaps this will be useful in that context.
I think, overall, I was hoping for a slightly different book – with more in-depth case studies of how to implement existing Data Science knowledge into Business scenarios. Nevertheless, it’s an interesting, intelligent guide in an encyclopedic sense and fairly unique in its clarity of explanation and accessibility – I highly doubt I could write a better guide in that respect. Existing Data Scientists will find many clear analogies to explain their craft to those less technical than themselves and I reckon that by itself justifies taking a look 🙂