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 tools being presented and the insights being offered.
From the outset, the authors are clear about the book's objectives: "The primary goals of this book are to help you view business problems from a data perspective and understand principles of extracting useful knowledge from data. There is fundamental structure to data-analytic thinking, and basic principals that should be understood. There are also particular areas where intuition, creativity, common sense, and domain knowledge must be brought to bear… As you get better at data-analytic thinking you will develop intuition as to how and where to apply creativity and domain knowledge."
This paragraph makes me think of all those undergrad and graduate students studying Statistics at Universities all over the world, my daughter included, who are being bombarded by one math or statistics class after another (Calculus III, Math Stat I and II, Linear Algebra, etc.). Yet, far too often, they enter the real world lacking "data analytic thinking" or a sense of "basic principals" They do, however, have a sense of being overwhelmed and under prepared. The epic battle between "frequentists" and "Bayesians", takes a back seat to what should be the real controversy in statistics departments around the world, the balance between "application" and "theory". The book's "primary goals" should be the walking orders of every statistics program at any college or university anywhere.
From the outset (page 2), the authors state, "Data mining is a craft. It involves the application of a substantial amount of science and technology, but the proper application still involves art as well." Absolutely true! It's great to read this stuff! This is followed by a concise discussion of CRISP-DM, a well-defined data mining process, whose concepts are elementary, essential, and integral to the responsible, proper, and successful practice of data mining.
From this point on, the authors proceed to accomplish their primary goals. They present such topics as predictive modeling, correlation, classification, clustering, regression, logistic regression, linear discriminants, and much more. Their presentations are user friendly, their real world examples are interesting, and their guidance and insights are extremely valuable.
My criticisms are limited to their website. The Data Science for Business site leaves me wanting more real world examples to enjoy, access to more resources and tools of the trade, more references to peruse, and a more rigorous approach to some of the solutions. Perhaps Data Science for Business the sequel is on the horizon?
Whether you're a seasoned statistician (or, data scientist), a young aspiring novice, or an adventurous business person looking to expand his/her horizons, Data Science for Business by Foster Provost and Tom Fawcett is well worth the price of admission and the reading time you'll invest.
Foster Provost and Tom Fawcett state, "[i]deally, we envision a book that any data scientist would give to his collaborators…" I'll do them one better, I'm giving it to my daughter!
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Customers say
Customers find this data science book provides a great framework for analytics and machine learning, offering practical considerations and examples of project evaluation. Moreover, the information is well-explained with just enough technical detail, making it easy to read and understand, with one customer noting it's written for a college-educated non-mathematician. Additionally, the book is well-organized and structured, with one review highlighting how it carefully separates concepts from math theory.
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79 customers mention information value, 74 positive, 5 negative
...Data Science for Business is also an excellent resource to avoid data mining pitfalls....Read more
Informative but dry. Overall a good resource but can get dense. Should illustrate how real world tools can be used.Read more
This book addresses the topic of Big Data in a practical, understandable manner....Read more
Extremely useful and thorough, from the perspective of a business person whose business involves overseeing and delivering analytic products....Read more
42 customers mention information richness, 33 positive, 9 negative
This book is informationally rich and is well written in fairly easy to understand language.Read more
This is an excellent textbook on data science. The text itself explains concepts and theories well and provides definitions, examples, and formulas...Read more
...the book to be read by beginners, but its wide coverage and detailed case studies makes it a reference for experts as well....Read more
...While not highly technical, the authors covers each topic with enough rigor to appreciate the tools being presented and the insights being...Read more
31 customers mention readability, 27 positive, 4 negative
...Pros: Very direct and easy to read (for a data science book) Ties data science into real world business scenarios...Read more
...Removing this bias, the information provided was clear, concise, and helpful for anyone working with big data or in data analytics.Read more
...fantastic job breaking down the concepts into really clear, easy to understand language....Read more
Purchased as a text book for class, easily readable from the perspective of an average joe (non-data scientist).Read more
26 customers mention insight, 26 positive, 0 negative
Good comprehensive work! Inspiring.Read more
Best book on this topic I've read. Superior in all respects. Greatly reduces the learning curve, and demystifies the concepts.Read more
...Gives you the details, insights, underlying principles, and a glimpse but not the full set of math that would be necessary to actually be a...Read more
Great book. Thought provoking. A must read for all managersRead more
24 customers mention data science, 22 positive, 2 negative
...Finally a book about data science in which the use of the word 'science' is justified: questions, exploration, models, heavy testing and tuning....Read more
Good overview of Data Science for a manager or other non-data scientist....Read more
...Problems. Definitely, worth reading for business strategists, statisticians, and data scientistsRead more
...Reproducible research; Experimental design; R programming (or python, or perhaps SAS or Octave, but some mathy language for sure); Exploratory data...Read more
17 customers mention written quality, 17 positive, 0 negative
An excellent, well-written resource even for data science practitioners like myself....Read more
Well written, a good overview for the semi-technical business reader....Read more
Thank you for a brilliantly written text that allows the reader to navigate the field of data science and for lifting up the mystery cover for many...Read more
...this was the first very good overview of the process and it was written in clear, easy to understand language....Read more
12 customers mention ease of use, 10 positive, 2 negative
...Pros: Very direct and easy to read (for a data science book) Ties data science into real world business scenarios...Read more
...people, and the business objectives to the analysts in a clear and compelling way. This is a must-read for both sides of the house!Read more
...and explained in such a way that was concise and relatively easy to follow.Read more
...The math is minimal. There are no computer programs or algorithms.Read more
12 customers mention organization, 10 positive, 2 negative
Very well organized, easy to follow and full of real life examples....Read more
Lots of helpful information presented in a nice framework. I cited this in my graduate school thesis and used several quotations.Read more
structured - well-written - very learnful - stays away from the hype - perfect introduction for any business manager who wants to go further than...Read more
...So what's so good? The book is really well thought out and the grammatical style is top notch....Read more
Don't buy the kindle version
Let me first say the content of this book is great - my apologies to the authors for docking a star. That said, I should have docked more based on how the kindle version displays. Look at the size of the equation examples on the bottom half of the image with the pink highlight. Even when I enlarge the text (see the other image without a page number) the equations are horribly tiny. The image with a page number at the bottom shows you how it is displayed in a PDF version (which I had to get AFTER I purchased this from amazon). (Apologizes for not being able to give image numbers, but the pics uploaded in a different order than I submitted them). Terrible quality on amazon's part and this book is frequently used as a college text book, so knowing the equations are essential.
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- Jim-CReviewed in Germany on November 24, 2019
Great intro and quick refresher course
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- Payam MokhtarianReviewed in Australia on March 6, 2018
Five Stars
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AdrianoReviewed in Italy on October 27, 2017
Perfetto per iniziare, ma anche per chi ha già esperienza
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