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on March 10, 2015
This isn't really a book about the business applications of data science. Instead it has some businessy sounding bits and the start and end which feel like an afterthought. The middle seems like it was taken from a data mining textbook (or perhaps previously was one). Particularly strangely, it presents some math for machine learning but in a dumbed down way using notation the author invented (the strangest was a replacement for sigma as sum notation).
Rather than reading this you're probably better off reading a book about how business might be impacted by machine learning and related things (The Second Machine Age or Average is Over). Alternatively, if you want to know more about data science / data mining (now fairly deprecated term this book uses) or machine learning you'd be better off picking up Hastie's or Mitchell's book or taking Andrew Ng's course on Coursera.
Rather than reading this you're probably better off reading a book about how business might be impacted by machine learning and related things (The Second Machine Age or Average is Over). Alternatively, if you want to know more about data science / data mining (now fairly deprecated term this book uses) or machine learning you'd be better off picking up Hastie's or Mitchell's book or taking Andrew Ng's course on Coursera.
on February 4, 2016
I recently made a career transition from academia as a Professor of IT Management to industry as Senior Data Scientist at one of the Big Four consulting firms. The research skills that I use for academic research are the same ones that I use to help my firm discover data-driven insights that are actionable and transformative for restructuring strategies and operations to drive business value. I thought this would be an easy argument to make during the interview process, but in the end I had to develop a less academic-sounding argument for the interviews. Provost and Fawcetts’ Data Science for Business was ideally suited for this purpose.
Provost and Fawcett is THE text if you want to learn advanced statistical methods in business-related problem-solving contexts separate from any specific programming language like R. It’s also the right choice if you want to understand data science from a strategic perspective and its process characteristics. Provost and Fawcett is extremely useful for anyone who is trying to get up to speed and demonstrate knowledge in business analytics or data science in relatively short manner. This text is extremely well written—the authors use non-technical language for the most part—and it’s interesting!
Provost and Fawcett is THE text if you want to learn advanced statistical methods in business-related problem-solving contexts separate from any specific programming language like R. It’s also the right choice if you want to understand data science from a strategic perspective and its process characteristics. Provost and Fawcett is extremely useful for anyone who is trying to get up to speed and demonstrate knowledge in business analytics or data science in relatively short manner. This text is extremely well written—the authors use non-technical language for the most part—and it’s interesting!
on May 18, 2017
although billed, at least in part, as aimed at "business people who will be working with data scientists, managing data science-oriented projects, or investing in data science ventures" (p xiii), the book never points out that all analytic techniques make assumptions and that the data scientist needs to be questioned about that (when they don't mention it upfront) and questioned about what happens when assumptions are violated; in addition, many, maybe most, techniques have biases and these are never mentioned either; there is also no discussion of bootstrap (the authors use cross-validation instead thus, generally, wasting information) or of external validation and no warnings about what to beware of when using surrogates; at a lower level, the book is generally readable and generally well-informed but needs to be supplemented with something that covers how to, at least, question the technical people about assumptions and biases
on December 17, 2013
This book is ideal for anyone looking to understand data science, and especially those who might interact with data scientists at work. Roughly half the book deals with the essential data mining algorithms. The focus is on understanding what the algorithms do, not the details of how they do it, so implementation details are omitted. The math is certainly discussed, but kept to a minimum, and coupled with comprehensible, plain English explanations of each algorithm. Each chapter includes a case study illustrating how the algorithm can be used for a real-world problem.
The other half of the book (interspersed between the algorithms) deals with issues relating to design, implementation, evaluation, and deployment of models. Without understanding these crucial ideas, the algorithmic knowledge is useless. For example, the right and wrong techniques for evaluating model performance are discussed at length. A businessperson without adequate background could easily be misled by certain evaluation metrics, and the reader is taught to evaluate model performance with a critical eye. There is also a chapter on evaluating and critiquing data mining proposals, which nicely ties together the algorithmic, business, and practical concepts discussed earlier in the book. Some case studies are revisited in several chapters at increasing levels of sophistication, making the book feel like a cohesive whole rather than a mere compilation of chapters. If you’re coming from a technical background, you will learn a great deal about the business and practical/implementation aspects of analytics. If you’re coming from a business background, you will gain an understanding of what your data can do for you, and how to use it to your benefit. The book is an intense but very pleasant read, even funny at times. Highly recommended!
The other half of the book (interspersed between the algorithms) deals with issues relating to design, implementation, evaluation, and deployment of models. Without understanding these crucial ideas, the algorithmic knowledge is useless. For example, the right and wrong techniques for evaluating model performance are discussed at length. A businessperson without adequate background could easily be misled by certain evaluation metrics, and the reader is taught to evaluate model performance with a critical eye. There is also a chapter on evaluating and critiquing data mining proposals, which nicely ties together the algorithmic, business, and practical concepts discussed earlier in the book. Some case studies are revisited in several chapters at increasing levels of sophistication, making the book feel like a cohesive whole rather than a mere compilation of chapters. If you’re coming from a technical background, you will learn a great deal about the business and practical/implementation aspects of analytics. If you’re coming from a business background, you will gain an understanding of what your data can do for you, and how to use it to your benefit. The book is an intense but very pleasant read, even funny at times. Highly recommended!
on March 7, 2015
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!
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!
Data Science - Technical implementation tied to corporate strategies and goals-risks and reward potential
on November 5, 2015
The institution strategy and goals need to be reflected in the procedures used to analyse the data base of the institution and the determination as to what data is relevant. The book discusses ways to obtain the data needed and the short term volatility in return to the company that can result. But the authors show that this can eventually lead to improved efficiency focus and profitability for the company. The book requires a background in a number of supportive academics for full understanding . The discipline has defined its own language much like most of the technolgical disciplines and is best appreciated by those familiar with the vocabulary. It is a book that warrants study not just as a quick read for introduction. For a person studying or practicing in this area I highly recommend this book for both its interest and as a reference book. Foster Provost and Tom Fawcett have made a valuable contribution to the understanding of Data Science.
on March 7, 2016
This is an excellent textbook on data science. The text itself explains concepts and theories well and provides definitions, examples, and formulas that help the reader understand and apply these concepts. The information presented is well-organized, and the visual aids include ample graphs and charts. Section breaks are obvious with well-designed titles. Chapters are easy enough to read but don't over-simplify important concepts. Inclusion of Glossary, Bibliography, and index, as well as a detailed table of contents, makes it easy to navigate. The only exception our instructor took with the text during my course was their insistence that only the best data scientists should be considered. Removing this bias, the information provided was clear, concise, and helpful for anyone working with big data or in data analytics.
on April 2, 2015
Excellent discussion of data science methods without excessive focus on mathematical elements. These are included at a level that can be understood for the skilled marketer who has background but does not wish to go deep into the math. The coverage is broad with both supervised and unsupervised methods in data mining. Topics cover tree models to logistic regression, to scoring. A discussion of holdout model tests, prediction & validation. Particular emphasis is placed on how to frame questions to apply to the business case so suitable conclusions can guide business decisions and strategy. You will get the sense that the authors are battle tested veterans of the data mining business and have applied their creativity to a broad range of business, data and technical challenges.
Only two caveats to this book. First, as purchaser of the kindle edition, I found the equations included in the text were sometimes very readable and sometimes the type was so small as not to be legible at all. Be warned. If you intend to follow the math that is included, perhaps the paper edition would be best. Second, this book does not dwell on the statistical packages that can be used to support data mining efforts. If you are interested in exploring these methods in practice, you will need to look further.
Only two caveats to this book. First, as purchaser of the kindle edition, I found the equations included in the text were sometimes very readable and sometimes the type was so small as not to be legible at all. Be warned. If you intend to follow the math that is included, perhaps the paper edition would be best. Second, this book does not dwell on the statistical packages that can be used to support data mining efforts. If you are interested in exploring these methods in practice, you will need to look further.
on July 18, 2014
Foster Provost and Tom Fawcett are known for their work on fraud detection, among others. I have recently read their last book, Data Science for Business – What you need to know about data mining and data-analytic thinking. No suspense: it’s one of the best data mining book I have ever read. Its style allows the book to be read by beginners, but its wide coverage and detailed case studies makes it a reference for experts as well.
As the title suggest, the book has a real focus on business with plenty of industry examples and challenges. The style is very pleasant since authors have made efforts to put the reader in specific situations to better understand a problem. To be noted the very interesting discussion of data mining leaks as well as data mining automation. The book is divided by concepts and provides a focus on them (instead of techniques). Although no exercice is present, the book could easily be used as a resource for a course.
Each chapter is clearly divided into basic and advanced topics. The evaluation phase of the data mining standard process is deeply discussed. The section about Bayes rule is very well written. Data Science for Business is also an excellent resource to avoid data mining pitfalls. Chapter 13 is a must-read in order to understand success factor for implementing data mining in a company. To conclude, targeted at both beginners and experts, Data Science for Business is the new reference for data mining professionals working in industry.
As the title suggest, the book has a real focus on business with plenty of industry examples and challenges. The style is very pleasant since authors have made efforts to put the reader in specific situations to better understand a problem. To be noted the very interesting discussion of data mining leaks as well as data mining automation. The book is divided by concepts and provides a focus on them (instead of techniques). Although no exercice is present, the book could easily be used as a resource for a course.
Each chapter is clearly divided into basic and advanced topics. The evaluation phase of the data mining standard process is deeply discussed. The section about Bayes rule is very well written. Data Science for Business is also an excellent resource to avoid data mining pitfalls. Chapter 13 is a must-read in order to understand success factor for implementing data mining in a company. To conclude, targeted at both beginners and experts, Data Science for Business is the new reference for data mining professionals working in industry.
