Data Science for Business
What you need to know about data mining and data-analytic thinking
Publisher: O'Reilly Media
Released: July 2013
Pages: 414

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
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oreillyData Science for Business
 
4.6

(based on 10 reviews)

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  • 5 Stars

     

    (8)

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100%

of respondents would recommend this to a friend.

Pros

  • Easy to understand (10)
  • Well-written (10)
  • Helpful examples (8)
  • Accurate (6)
  • Concise (4)

Cons

    Best Uses

    • Novice (9)
    • Intermediate (8)
    • Student (8)
    • Expert (3)
      • Reviewer Profile:
      • Developer (5), Educator (4), Maker (3)

    Reviewed by 10 customers

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    5.0

    Perfect survey for non-specialists.

    By Edmon

    from Knoxville, TN

    About Me Designer, Developer, Maker

    Verified Reviewer

    Pros

    • Accurate
    • Easy to understand
    • Helpful examples
    • Well-written

    Cons

    • None

    Best Uses

    • Intermediate

    Comments about oreilly Data Science for Business:

    I carry this book with me all the time, and I read it any time I have a spare moment. I am a CTO in a data analytics company and I find myself frequently in the need of explaining how and where data analytics can help businesses.
    I also find the need to inspire myself with some interesting applications that I did not have chance to try out yet. This book gives me both.
    The book has a rare computational, mathematical, and business depth that will satisfy both a computer scientist and a business person with a curious mind.

     
    5.0

    Insight into any world

    By ~ icp

    from New York, NY

    About Me Sales, Sales Executive

    Verified Reviewer

    Pros

    • Accurate
    • Concise
    • Easy to understand
    • Helpful examples
    • Well-written

    Cons

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      • Student

      Comments about oreilly Data Science for Business:

      I came across this book when preparing to interview for a sales position at a marketing technology company whose product relies on collecting, interpreting and making data actionable. It took less than a couple of hours to find myself immersed in real, recognizable stories, set as examples of data science's ability to reimagine the synthesis of a problem and present a hidden-from-plain-sight answer. Shortly afterward, I was introduced to the fundamental principles of data science, which expanded my analytical thinking, giving it multidimensionality and an understanding of how to expose a problem's solution, whether in business, the arts, social science, or life itself.

      In a world where an ever-increasing amount of actions and interactions are collected and stored in readily available data sets, those who master the analytical thinking to mine them will attain a significant competitive advantage.

       
      5.0

      Great Book

      By Andrew G

      from Colorado

      About Me Educator

      Verified Reviewer

      Pros

      • Easy to understand
      • Well-written

      Cons

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        • Intermediate
        • Novice
        • Student

        Comments about oreilly Data Science for Business:

        The future of computers is with data. But what do you need to know. This is a great overview of the field.

        It is perfect for helping a business person understand the terminology and processes involved in machine learning, statistical analysis, data mining, and the general data driven business plan.

        This book is also perfect for someone interested in computer science, but needing the complexities placed into some form of organization that will better help them decide what they want to focus on (or in what order).

        LEVEL: Beginner - Intermediate computer science OR ANY LEVEL Business Professional.

        In short, I believe that this is one of the core fields that everyone should have knowledge about, and this book does a great job of covering what data science is and how it is used.

        (2 of 2 customers found this review helpful)

         
        3.0

        Not a How-To Guide, More for Managers

        By Will J

        from Chicago, IL

        About Me Analyst, Professional

        Verified Reviewer

        Pros

        • Easy to understand
        • Well-written

        Cons

        • Not comprehensive enough
        • Too basic

        Best Uses

        • Novice
        • Student

        Comments about oreilly Data Science for Business:

        I had high expectations for this book. It's a part of the data science toolkit offered by O'Reilly and I own a majority of those books in that kit. I was disappointed at the 1.) scope of the book, 2.) depth of the book and 3.) applications towards business.

        Scope: I've read the Programming Collective Intelligence book, Machine Learning for Hackers and a few others so I'm aware of the breadth of topics the authors could have chosen. However, their supervised and unsupervised methods were really limited to: classification problems (Decision Trees, Clustering), Expected Value (very useful) and model evaluation. They only mention linear regression in passing. Maybe they considered it too basic, but I have to imagine predicting a value is more frequently done than classification problems.

        Depth: With the narrow focus they did explain the concepts of k-Nearest Neighbors, Decision Trees, and Expected value VERY well but there's no code and the bibliography is not sorted by chapters so it's harder to identify where you should go next. Had the authors at least done pseudo-code I would have appreciated the effort. It would still have been appropriate for managers who need to know the basics of these advanced analytical techniques.

        Business: This peeved me the most. There were only two real "applications" / examples used that were related towards business - Churn of wireless phone customers and targeted advertising. I feel like the "for business" portion of the title set the expectation too high.

        However, if you need to get a general understanding of decision trees, clustering and "expected value" of targeted advertisements, this is a good book. I can definitely see a person with zero analytics experience but having to manage a team of advanced analysts would definitely benefit from this book - little math but great explanations (for the topics that are discussed). In addition, I could see this book as being supplemental to a high-level database marketing class (i.e. one that doesn't go into the math of the algorithms but just runs the procedures and interprets the results).

        Bottom Line: Not deep enough to start doing the analyses and not enough examples of real business applications to generate many new ideas for your own work.

        (1 of 1 customers found this review helpful)

         
        5.0

        Introduction to data science

        By Victor Sheng

        from Conway, Arkansas

        About Me Designer, Educator

        Verified Reviewer

        Pros

        • Accurate
        • Concise
        • Easy to understand
        • Helpful examples
        • Well-written

        Cons

          Best Uses

          • Expert
          • Intermediate
          • Novice
          • Student

          Comments about oreilly Data Science for Business:

          "Data Science for Business" is extremely well written for data scientists. It identifies a small set of fundamental principles underlying data science. By walking you through these fundamentals, you can easily understand many well-known data mining algorithms. A broad set of practical data analytic skills based on real world applications introduced can help you perform data analytics effectively. Overall, it is broad, deep, but not too technical. Readers do not need sophisticated mathematics and statistics background to understand the concepts and methodologies in data science.

          (1 of 1 customers found this review helpful)

           
          5.0

          Excellent for understanding background

          By ahmetRasit

          from Ankara, Turkey

          About Me Developer, Maker

          Pros

          • Easy to understand
          • Helpful examples
          • Well-written

          Cons

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            • Intermediate
            • Novice
            • Student

            Comments about oreilly Data Science for Business:

            It's a great textbook that clearly tells the underlaying concepts of data analysis approaches, without making you scared or feel incapable.

            Only the basic equations are given if they're really necessary throughout the book, which makes you comfortable reading the book. Also this helps you to read the book uninterrupted, which also keeps you motivated during the chapters. I wanted to learn the fundamental concepts many times from different sources (books, tutorials, MOOCs) but this book is the best among them. I actually use many of the approaches mentioned in the book but after finishing the book that I feel that I've really understood and digested the concepts.

            I've already started to suggest this book to my friends and colleagues, and strongly recommend for anyone interested in learning the background of data analysis approaches.

            (2 of 2 customers found this review helpful)

             
            5.0

            Great book for DS.

            By mkokkodi

            from New York City

            About Me Data Scientist, Researcher

            Verified Reviewer

            Pros

            • Accurate
            • Concise
            • Easy to understand
            • Helpful examples
            • Well-written

            Cons

              Best Uses

              • Expert
              • Intermediate
              • Novice
              • Student

              Comments about oreilly Data Science for Business:

              The book covers basic concepts of predicting modeling and how data science is applied in various business problems. It is a great book for anyone who wants to learn data science techniques and deal with data. One of the major strengths of this book, is that focuses on evaluation techniques, and streamlines the process of understanding good and bad models.

              I believe that this can be either your introductory book in Data mining / data science or a book that will pinpoint a set of things that you might be doing wrong, even if you are an experienced data scientist.

              Finally, I found it very coherent and easy to read.

              (6 of 7 customers found this review helpful)

               
              5.0

              a book for real world data science

              By Hans

              from New York, NY

              About Me Developer, Educator

              Verified Reviewer

              Pros

              • Accurate
              • Easy to understand
              • Helpful examples
              • Well-written

              Cons

                Best Uses

                • Intermediate
                • Novice
                • Student

                Comments about oreilly Data Science for Business:

                I've taken a data mining course using the draft version of this book as the textbook. As a working data scientist after school, I would say I benefited a great deal from reading the book and taking the course. I recommend this book to everyone who likes to learn more about data science, being a data scientist, data engineer, customer service manager or a sales manager.

                There are plenty of technical books on statistical machine learning, data mining and patten recognition. I've read many of them. What's lacking in the market is a book that talks about how those sophisticated mathematical algorithms can be applied in business practice, what problems would occur while applying those methods, and how to deal with problems in the wild. This book fills that gap between academia research and the business world. Without a strong mathematical background, you can still enjoy reading the book. With a computer science background, I benefit the most from the book on how it relates to the real world problem solving. It is always great to have a book describing to you how real world data mining looks like, how to pick different algorithms, and how to measure your results.

                This is not a book for machine learning experts (though both authors are renowned machine learning experts), but if you are not one of them, you will find this book very useful.

                (3 of 3 customers found this review helpful)

                 
                5.0

                A perfect primer for data science

                By Josh

                from Brooklyn, NY

                About Me Developer, Educator, Maker

                Pros

                • Accurate
                • Concise
                • Easy to understand
                • Helpful examples
                • Well-written

                Cons

                  Best Uses

                  • Intermediate
                  • Novice
                  • Student

                  Comments about oreilly Data Science for Business:

                  Intelligent use of data has become a force powering business to new levels of competitiveness. To thrive in this data-driven ecosystem, engineers, analysts and managers alike must understand the options, design choices, and tradeoffs before them. With motivating examples, clear exposition and a breadth of details covering not only the "hows" by the "whys", Data Science for Business is the perfect primer for those wishing to become involved in the development and application of data driven systems.

                  (8 of 12 customers found this review helpful)

                   
                  3.0

                  Encyclopedic guide for managers

                  By Matthew Ruttley

                  from New York, NY

                  About Me Data Scientist, Developer

                  Verified Reviewer

                  Pros

                  • Easy to understand
                  • Helpful examples
                  • Well-written

                  Cons

                  • Not comprehensive enough
                  • Too basic

                  Best Uses

                  • Novice

                  Comments about oreilly Data Science for Business:

                  (Originally posted here: http://ikigomu.com/?p=153 )

                  I'm currently participating in the O'Reilly Blogger Review Program – where bloggers are given ebooks of recent publications.

                  Data Science for Business fits an interesting gap in the market – managers who want to be able to understand what Data Science is, how to recruit Data Scientists or how to manage a data-oriented team. It says it is also for aspiring Data Scientists, but I would probably recommend Andrew Ng's Machine Learning course and Codecademy's intro Python course instead if you're serious about getting your teeth into the field.

                  Somewhere between an introduction and an encyclopedia, it gives fairly comprehensive overviews of each sub-field, including distinctions that I hadn't previously thought of so clearly. The authors are mostly unafraid to explain the maths behind the subjects. It dips into some probability and linear algebra – admittedly with simplified notation. There's no real mention of implementation (i.e. programming the examples) as one would usually expect with O'Reilly; but most competent readers will now at least know what they're "looking for" perhaps in terms of packages to install or if they want to try and implement a system from scratch. It is certainly designed for the intelligent, professional and far from popular science.

                  Whilst it is very thorough and interesting it could touch a nerve among Data Scientists, since should a manager of a Data Scientist really have to read a book such as this – surely in such a position of authority they should know of these techniques already? (an extreme example would be one footnote which even contains a description of what Facebook is, and what it is used for). Often, such unbalanced hierarchies are the cause of much unnecessary stress and complication in the workplace. However, this is often the case so perhaps this will be useful in that context.

                  I think, overall, I was hoping for a slightly different book – with more in-depth case studies of how to implement existing Data Science knowledge into Business scenarios. Nevertheless, it's an interesting, intelligent guide in an encyclopedic sense and fairly unique in its clarity of explanation and accessibility – I highly doubt I could write a better guide in that respect. Existing Data Scientists will find many clear analogies to explain their craft to those less technical than themselves and I reckon that by itself justifies taking a look :-)

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