I have just started reading this book: "Freakonomics" by Steven D. Levitt and Stephen J. Dubner. Although Levitt is a famous economist, this book is not about Economics. It has generated a lot of interest because it gives a very original, simple and friendly view about what pragmatic economics could be.
More precisely, the authors give a lot of real-world examples of questions one may ask about everyday life (society, politics, education,...) and how a proper and careful analysis of the available data can provide (sometimes surprising) answers to these questions. In my opinion, this book is very valuable to people interested in practical aspects of data analysis. Indeed, I see it more as a hands-on approach to data analysis than as a new approach to Economics.
What is most important to me is that all the examples given in the book concur to show that, in order to extract relevant information from data, one needs to think a lot about how the data was collected, what the data means, and what are precisely the questions to be answered using this data. This may seem disappointing to many Machine Learning researchers who tend to think that a good algorithm can solve most practical learning problems, but when it comes to actually helping people solve a practical problem (in a real-world situation), this never happens. One needs a lot of careful, rigorous, timely and possibly boring investigations that cannot be automated and require significant knowledge and understanding of the data and what this data is about.
Anyway, this book is fun to read and I recommend it!