Data Science for Business: Foster Provost and Tom Fawcett

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

Amazon カスタマー: 英語として、そう難しくはありませんが、簡単な表現で豊かなニュアンスを表しています。翻訳もありますが、ニュアンスのところが、逆になっていたりと、言葉として読んで場合に難があるので、英語で読まれることをお勧めします。

Japan on Oct 15, 2022

San_Diego_Brit: This book is so good I've added it to my Top 10 technical/business books ever read. My son gave me his copy and said "I kept seeing this book on the desk of people I respect". I'm currently on my second reading of the book in order to reinforce the concepts, and I bought a copy of the book for someone at work.
So what's so good? The book is really well thought out and the grammatical style is top notch. The book leads the reader into some fairly complex topics without overwhelming you. That being said, some of the algorithms an statistical models can be a little daunting, but the book is designed to introduce the reader to a range of tools and models and not to make you an expert overnight. I found the book to be both pragmatic and concise, while covering a lot of ground. An excellent text for someone who is interested in the background behind many AI and machine learning algorithms, and are looking for guidance on when to consider different approaches. Highly recommended, particularly for folks like me, who have dabbled in ML already and are looking to take their knowledge of Data Science to the next level.

United States on Jun 02, 2022

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

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 that I have...

United States on Oct 14, 2015

T. Bond: Data Science for Business by Foster Provost and Tom Fawcett is a very important book about data mining and data analytic thinking. In 1971, Abbie Hoffman shocked the world when he demanded hippie readers (at the time, a likely oxymoron) "Steal This Book". While I wouldn't go so far as to encourage current and future data scientists to shoplift, I will demand that they READ THIS BOOK!

Not long ago, data was difficult and expensive to come by. Today, we're living in a world of far too much data, vast amounts of cheap computing power, and way too many poorly defined questions. Mix them all together and you're guaranteed to make a mess.

Going from data dearth to plethora presents substantive issues. In business, the balance between gut feel decision-making and analysis paralysis is changing, rapidly. Whether it moves too far from gut to paralysis, only time will tell. Through Data Science for Business, Provost and Fawcett offer practitioners a guide to equilibrium.

Read this book and you'll find yourself moving briskly down the road towards data analytic enlightenment. While not highly technical, the authors covers each topic with enough rigor to appreciate the...

United States on Mar 07, 2015

Daniel Bryant: In a nutshell: If you are looking for a simple (but not simplistic) introduction to nearly all of the underlying data science fundamentals then look no further, because this is the book for you!

I work primarily as a software developer, and like to consider that I have good general knowledge and experience of what data science ('data analytics', 'big data' etc.) is through College/Uni education and also the modern press and blog posts etc. However, I often struggled at times to fully understand, and perhaps more importantly knit together and apply, the core fundamentals of the topic. This book has provided exactly the explanations and 'glue' that I required, in that it delivers a very well structured (and paced) introduction and overview of data science, and also how to think in a 'data-analytics' manner.

If you preview the book with the 'look inside' feature then what you see in the table of contents is exactly what you get. Every chapter delivers upon its title (and promised 'fundamental concepts'), and frequently builds superbly upon topics introduced in early chapters. You'll move seamlessly from understanding how to frame data science questions, to learning...

United Kingdom on Jan 16, 2014