Data Science for Business : What You Need to Know About Data Mining and Data-Analytic Thinking, Paperback

Data Science for Business : What You Need to Know About Data Mining and Data-Analytic Thinking Paperback

3 out of 5 (5 ratings)


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


  • Format: Paperback
  • Pages: 408 pages, illustrations (black and white), charts
  • Publisher: O'Reilly Media, Inc, USA
  • Publication Date:
  • Category: Knowledge management
  • ISBN: 9781449361327



Free Home Delivery

on all orders

Pick up orders

from local bookshops


Showing 1 - 5 of 5 reviews.

Review by

Data Science is a good overview on how to mine the prodigious amount of digital data available these days. It describes the techniques involved, goes beyond the basics with many real-world examples, and takes the reader into the formulas and algorithms used to draw conclusions. There are numerous sidebars that discuss some of the common errors that can occur in the process. The book's value will be based on the reader's expertise in this growing area - one of its strengths for this newcomer is the discussion of the types of problems data mining can solve and how the questions are formed so that the data can be analyzed for an answer. It also has a useful chapter on how text can be mined to yield results in the business world. Be prepared to go beyond Data 101 if you sit down with this book.

Review by

Data science is the new best thing, but like Aristotle’s elephant people study to define exactly what data science is and what the skills required are.When we see data science we tend to recognise what it is, that mixture of analysis, inference and logic that pulls information out of numbers, be it social network analysis, plotting interest in a topic over time, or predicting the impact of the weather on supermarket stock levels.This book serves as an introduction to the topic. It’s designed for use as a college textbook and perhaps aimed at business management courses. It starts at a very low level, assuming little or no knowledge of statistics or of any of the more advanced techniques such as cluster analysis or topic modelling.If all you ever do is read the first two chapters you’ll come away with enough high level knowledge to fluff your way through a job interview as long as you’re not expected to get your hands dirty.Chapter three and things get a bit more rigorous. The book noticably changes gear and takes you through some fairly advanced mathematics, discussing regression, cluster analysis and the overfitting of mathematical models, all of which are handled fairly wellIt’s difficult to know where this book sits. The first two chapters are most definitely ‘fluffy’, the remainder demand some knowledge of probability theory and statistics of the reader, plus an ability not to be scared by equations embedded in the text.It’s a good book, it’s a useful book. It probably asks too much to be ideal for the general reader or even the non numerate graduate, I’d position it more as an introduction to data analysis for beginning researchers and statisticians more than anything else, rather than as a backgrounder on data science.

Review by

This book demonstrates the statistical pattern known as the long tail: a few things I understand relatively well and a much larger proportion that are beyond my grasp. Despite years working with information systems this makes me starkly aware that I can't claim to be a data scientist!I think this work would come into its own near the top of a reading list for a course on the subject. Without that, it seems short on worked examples and exercises to develop skills while I can see that, on a course, those would be provided and the information contained here would act as an excellent (albeit still challenging primer). Outside of that context, it didn't fulfil my hope of developing my skills although I did find the chapter on text mining (perhaps closest to my own areas of expertise) particularly fascinating.

Review by

I set this book aside a little over a year ago, and this morning I've decided that I probably won't finish it. With my background in databases and business intelligence reporting, I thought I would really enjoy this book. I appreciated about the first half of the book. As I recall, the latter half of the book mined deeper into the slight variations of the same theories. This would undoubtedly be fascinating for the reader who wants to begin studying data science. I somehow doubt that many managers would get farther than half way.

Review by

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.

Also by Foster Provost