Data Science for Business: A Guide to Unlocking Value with Foster Provost and Tom Fawcett

Michael: I work in analytics and have some familiarity with some of the techniques and discussions in the book, but it does an excellent job going through at a good depth, several of the critical areas of data science. Really helpful for me and my career.

United States on Dec 23, 2023

Julio Manzoli: I really appreciate this kind of book, that is able to elaborate on complex topics without loosing the reader presenting only technical aspects of if. I recommend Data Science for Business to every person working in the Business Analysis area or with any Data-oriented area.

Brazil on Feb 14, 2023

Vizzy: Detailed information on business related problems and how to solve them. It gives an overview of basic machine learning algorithms and how to use them. Good problem solving ideas like why should credit card companies look at people who are most likely to leave and instead why not calculate people who are more likely to leave and cause more damage, when to calculate lift or leverage, etc. In a nutshell, it gives an overview of applying data science techniques to real world problems as the name suggests.

Germany on Jan 15, 2022

2501: Peut-être le livre le plus intéressant que j'ai pu lire sur le machine learning. Livre non destiné au débutants, car si vous ne maîtrisez pas déjà le sujet, vous n'en tirerez pas grand chose, mais si vous avez déjà une certaine expérience sur le sujet, il vous fera comprendre pas mal de subtilités habituellement jamais évoquées.

France on Nov 11, 2021

Luna: Simple de entender

Spain on Mar 23, 2020

Derecj J Toker: This book has become invaluable to me and my company. We refer to it as 'the bible'.
Has amazing coverage of topics, is very clear and thorough, and most importantly offers very inspiring and helpful examples of how various data science techniques can be applied that is not easy to find in any other book. The book also has great discussions on the field of data science in business and how to structure/manage data science teams.

Canada on Mar 10, 2020

Orange Monkey: Context: I'm an MD, needing to communicate with data scientist to build a product.

I've this far only read two chapters. My pattern-recognition ;) this far however, with an assessment that this will be applicable to the rest of the book is two-fold:

1) Too verbose!
Too much stuff on explaining the structure and purpose of the book. Could've been said way more succinctly, and therefore more clearly. The effect is that I start skimming.

2) Not 'sharp' enough.
The best non-fiction written for non-expert manages to reduce the complex into explaining the essence. Not making it simpler, and reducing crucial comprehension. But reducing the complex into its crucial essence.
When going over different types of tasks; classification, regression, similarity matching, clustering, co-occurence grouping - the way they are described, there is essentially no difference between i.e. clustering, similarity matching and clustering; they're all classifications - yes, there is a difference between regression.

In order for this to be truly helpful even for an absolute layman as myself, it needs to add enough crucial, essential distinctions to make...

United States on Sep 05, 2017

Geoffrey R. Anderson: It's an excellent, even mandatory book for your Data Science shelf. I am glad I bought it. I am 67% of the way through reading this book. It has nowhere near enough material on some areas, though, and is just missing some material that you need for DS. That's actually OK because of course no single book is enough to cover everything you need to know in a field. Look how many books you may have bought just to get an undergrad degree, and I bet it was not just one book.

So here is a list of good and bad about this excellent book.

Its good points:

The profit curve. After reading this book, I will never use Accuracy to select a model any more, as that's nearly a worthless metric especially when there are marginal costs and marginal profits involved in an application scenario. The book is just amazingly good on describing how to select models based on estimated profit, and foremost the profit curve, and selected other supporting curves like ROC area under curve.

The expected profit computation and the cost-benefit matrix as a partner to the confusion matrix. This is great stuff. It's not even described in other data science courses...

United States on Oct 14, 2015