Data Science for business [book review]

Data Science for Business: What you need to know about data mining and data-analytic thinking Foster Provost and Tom Fawcett O'Reilly Media  Data science is the new best thing, but like Aristotle’s elephant people study to define  exactly what data science is and what the skills required are. When we see data science we tend to recognise what it is, that mixture  of analysis, inference and logic  that pulls information out of numbers, be it social  network analysis, plotting interest in a topic over time, or predicting the impact of the  weather on supermarket stock levels. This book serves as an introduction to the topic. It’s designed for use as a  college textbook and perhaps  aimed at business management courses. It starts at a very  low level, assuming little or no knowledge of statistics or of any of the more advanced  techniques such as cluster analysis or topic modelling. If all you ever do is read the first two chapters you’ll come away with enough  high level knowledge to fluff your way through a job interview as long as you’re  not expected to get your hands dirty. Chapter three and things get a bit more rigorous. The book noticably changes  gear and takes you through some fairly advanced mathematics, discussing  regression, cluster analysis and the overfitting  of mathematical models, all of  which are handled fairly well It’s difficult to know where this book sits. The first two chapters are most  definitely ‘fluffy’, the remainder demand some knowledge of probability theory  and statistics of the reader, plus an ability not to be scared by equations embedded  in the text. It’s a good book, it’s a useful book. It probably asks too much to be ideal for the  general reader or even the non numerate graduate, I’d position it more as an  introduction to data analysis for beginning researchers and statisticians more than  anything else, rather than as a backgrounder on data science. [originally written for LibraryThing]