Foster Provost

This is an excellent introduction to Data Science for the person who wants to gain a good understanding of the subject and what it can do for business. The authors’ language is straightforward and they have attempted to simplify statistical nomenclature to avoid losing their less statistically qualified readers.
Provost and Fawcett have put together a very accessible guide that explains what Data Science is and what it is not. Their work is realistic and practical. The book presents the show more theory behind the subject and includes practical applications that enhance the reader’s understanding. Provost and Fawcett are excellent at swiping away myths on the subject, myths that some commercial promoters of the subject may like to maintain. The power of Data Science is amazing enough without reliance on myth. The authors clearly state that Data Science is not the panacea for all ills, and that it cannot succeed without the understanding of people.

Not only does this book deal with the theoretical and technical concepts of the subject, but it discusses how the discipline can work within an organisation and with such issues as data privacy.

It is seldom one finds a book that is so clear and comprehensive. show less

This book demonstrates the statistical pattern known as the long tail: a few things I understand relatively well and a much larger proportion that are beyond my grasp. Despite years working with information systems this makes me starkly aware that I can't claim to be a data scientist!

I think this work would come into its own near the top of a reading list for a course on the subject. Without that, it seems short on worked examples and exercises to develop skills while I can see that, on a show more course, those would be provided and the information contained here would act as an excellent (albeit still challenging primer). Outside of that context, it didn't fulfil my hope of developing my skills although I did find the chapter on text mining (perhaps closest to my own areas of expertise) particularly fascinating. show less

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 show more 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. show less

Data Science is a good overview on how to mine the prodigious amount of digital data available these days. It describes the techniques involved, goes beyond the basics with many real-world examples, and takes the reader into the formulas and algorithms used to draw conclusions. There are numerous sidebars that discuss some of the common errors that can occur in the process.

The book's value will be based on the reader's expertise in this growing area - one of its strengths for this newcomer show more is the discussion of the types of problems data mining can solve and how the questions are formed so that the data can be analyzed for an answer. It also has a useful chapter on how text can be mined to yield results in the business world. Be prepared to go beyond Data 101 if you sit down with this book. show less