Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.
- Understand how data science fits in your organization―and how you can use it for competitive advantage
- Treat data as a business asset that requires careful investment if you’re to gain real value
- Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
- Learn general concepts for actually extracting knowledge from data
- Apply data science principles when interviewing data science job candidates
22 reviews for Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
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T Fischer –
Data Science for Business provides a robust framework for approaching data analytics. The framework can be applied in a variety of business contexts and is very practical.
tjain –
As name suggest Data Science for Business is for business people. It is an education book for business people not for technical crowd.
Book explains Data science and data mining concepts in very crisp manner. Those who does not have patience to read whole book should read chapter two. Now if you interested then go ahead. I guess you will.
Book is around 500 pages long which makes it difficult to read but still book has good content for business folks without using arcane jargon.
Gert De Caluwe –
structured – well-written – very learnful – stays away from the hype – perfect introduction for any business manager who wants to go further than the ‘myth’ and ‘high level slogans’
K. Gilman –
This book strikes the right balance between providing practical advice for the aspiring data scientist and giving a solid foundation in the theory that can be generalized beyond the contents of the book. A lot of business books claim to be appropriate for a wide audience from novice to expert but this one really hits that mark. I’ve searched thoroughly for a book that is a comprehensive accounting of the techniques and possibilities of data analytics and been disappointed until I found this one. I’ve read the entire thing and I find myself referring back to it frequently. Whether your planning to perform data mining work yourself or you just want to really understand what it’s all about, you won’t be disappointed. There are some additional topics that I had wished were included in more detail but I’ve discovered that this is because this is still a young discipline and those things (e.g., formal detailed methodologies) just don’t exist yet. Don’t let the low price fool you, this is the most comprehensive book of usable data mining available right now.
jpk –
Nice intro book on Data Science.
Audience business manager without too much jargon.
Topics are organized logically and book could be use as good supplementary text
in not too technical university course.
Santiago Ortiz –
I don’t know if it’s that I was in big need of a book like this one, or is simply that the book is excellent. Finally a book about data science in which the use of the word ‘science’ is justified: questions, exploration, models, heavy testing and tuning. This is not a book about technology -it barely mentions Hadoop!. Instead, it introduces the thinking and the fundamental ways to solve problems using data (and intelligence).
Trevor Burnham –
If you’re thinking of analyzing large datasets for common business applications (e.g. targeted marketing), this book will give you everything you need to know and then some. It not only gives an overview of the most popular techniques for prediction and categorization, but also highlights some of the subtler technical issues involved (overfitting, choosing an appropriate loss function) as well as higher-level concerns (privacy, comprehensibility). My only quibble is that some of the visualizations shown in the book are not adequately explained.
Geoffrey B –
Data Science for Business is one of several data science titles from O’Reilly. It is probably the most comprehensive in terms of theory. That said, it feels a bit too comprehensive, including everything from statistical methods to research proposals to how to talk to your data people. One thing it doesn’t get into though is the actual technology. It is assumed that your data experts will figure that out and you’ll ask technology questions at the meta level. It’s an approach that would make more sense if you spent more time understanding results at the meta level too. As a result I would suggest this otherwise comprehensive tome needs an accompanying guide to data science technology.
If you are an executive with low level experience doing data science, e.g. with Excel sheets for company specific data, this is a good guide to thinking seriously about higher level stuff. But if you are truly curious about what this data science stuff is all about, I would recommend starting with a different O’Reilly title, Doing Data Science, which provides more of a ground level approach to what you can do with data science and how.
Anonymous –
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!
RaffiMK –
I worked for a number of years at Fair Isaac, a data science company founded in the 1950’s. Although I’m not a data scientist myself, I spent quite a bit of time understanding how predictive analytics is used in different contexts. Foster and Provost have managed to complete a nearly impossible task: create the right “translator” to explain the math to the business 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!
Thomas –
Note – I was provided an ebook version in exchange for my review as part of the Library Thing Early Reviewers program.
In brief –
This is a great book for any in the data science field or wanting to just understand “Big Data” or a manager/professional just trying to “get current. “ I have a masters degree in software engineering with a data science background and three years experience in a prior job in Data warehousing. It was a long read, especially with the holidays, but well worth it, and more enjoyable than almost every technical book I have every read.
Strengths – Organization, having technical details in a side by side section for those who want it, covering details from definition, through use and application, as well as doing a good job explaining similarities and differences on key topics.
Weaknesses – there are a few small places I wanted more. Meaning if they could have somehow had more examples for the different models, situations, etc., especially as I got into more of the predictive models.
S. Livingston –
Lots of helpful information presented in a nice framework. I cited this in my graduate school thesis and used several quotations.
Kartik Kanakasabesan –
If you are one of those that is not going to wait till some sits down and teaches you daw science concepts then book is not for you. If you like get your hands dirty and get into meaningful conversations with your data scientists then this book is for you.
Stephen –
…speaking as someone who dove in without testing the water first.
You’ll be left with skills yet to develop, but enough confidence and competence to do so.
Mafie –
The book explains effective procedures and typicall pitfalls in plain english and with helpful descriptive real world examples.
For proceeding data mining in greater depth it would be not sufficient but to get started and understand data science better its a GREAT BOOK!
Zain Khandwala –
Provost and Fawcett’s book is one of the very few in the field that neither condescends nor patronizes the reader as it explores the motivations and machinery behind the most commonly used data analysis techniques in the analytics professional’s toolbox. While it stops short of providing detailed instruction on how to use these techniques, it provides the reader a solid foundation for taking this next hands-on step. And for those who are not working directly with data, but are otherwise stakeholders in the use of analytics to drive better organizational outcomes, this book will greatly enable you to understand and add value to the analytical process.
—
Zain Khandwala
Executive Director,
Institute for Advanced Analytics
Bellarmine University
Louisville, KY
Hari Venkataraman –
Provides a great framework to approach analytics/machine learning with real world examples. The book is written in easy to follow language and makes a difficult subject understandable even for beginners in this area. A must read if you’re exploring a career in analytics or just interested in learning more about the subject.
Lackshu Bala –
I like the fact that the book is very readable and the more technical pieces are discussed separately. I am considering getting the hard copy as it’ll be a good reference over the years. I have become a fan of the authors.
ggw –
Best first (maybe last) introduction text for Freshman.
I wish I had taken this courses 5 years ago.
Incredible Mouse –
The intuitive non-mathy approach makes this immediately accessible to a large audience. You will walk away with an understanding of the concepts, like you never thought possible. Very impressive.
Sandro Saitta –
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.
Chandan M Gokhale –
Great book. Exactly what I was looking for.