"A must-read resource for anyone who is serious about embracing the opportunity of big data."
-- Craig Vaughan
Global Vice President at SAP
"This book goes beyond data analytics 101. It's the essential guide for those of us (all of us?) whose businesses are built on the ubiquity of data opportunities and the new mandate for data-driven decision-making."
CEO of Media6Degrees and Former Head of Google Search and Analytics
"Data is the foundation of new waves of productivity growth, innovation, and richer customer insight. Only recently viewed broadly as a source of competitive advantage, dealing well with data is rapidly becoming table stakes to stay in the game. The authors' deep applied experience makes this a must read--a window into your competitor's strategy."
-- Alan Murray
Serial Entrepreneur; Partner at Coriolis Ventures
"This timely book says out loud what has finally become apparent: in the modern world, Data is Business, and you can no longer think business without thinking data. Read this book and you will understand the Science behind thinking data."
-- Ron Bekkerman
Chief Data Officer at Carmel Ventures
"A great book for business managers who lead or interact with data scientists, who wish to better understand the principles and algorithms available without the technical details of single-disciplinary books."
-- Ronny Kohavi
Partner Architect at Microsoft Online Services Division
About the Author
Foster Provost is Professor and NEC Faculty Fellow at the NYU Stern School of Business where he teaches in the MBA, Business Analytics, and Data Science programs. His award-winning research is read and cited broadly. Prof. Provost has co-founded several successful companies focusing on data science for marketing.
Tom Fawcett holds a Ph.D. in machine learning and has worked in industry R&D for more than two decades for companies such as GTE Laboratories, NYNEX/Verizon Labs, and HP Labs. His published work has become standard reading in data science.
Most helpful customer reviews
228 of 233 people found the following review helpful.
The perfect balance
By m l
When trying to learn about a new field, one of the most common difficulties is to find books (and other materials) that have the right "depth". All too often one ends up with either a friendly but largely useless book that oversimplifies or a heavy academic tome that, though authoritative and comprehensive, is condemned to sit gathering dust in one's shelves. "Data Science for Business" gets it just right.
What I mean might become clearer if I point out what this book is *not*:
- It is *not* a computer science textbook with a focus on theoretical derivations and algorithms.
- It is *not* a "cookbook" that provides "step-by-step" guidance with little to no explanation of what one is doing.
- It is *not* your standard "management" title on the cool tech du jour available at airport stands and meant to be read in one sitting (buzzwords, hype and overly enthusiastic statements making up for the dearth of actual content).
Instead, it is close to being the perfect guide for the intelligent reader who -- regardless of whether s/he has a tech background -- has a sincere desire to learn how the tools and principles of data science can be used to extract meaningful information from huge datasets. Highly recommended.
132 of 135 people found the following review helpful.
Let Me Guess ...
By Big Data Paramedic
At it's core, Data science is the elimination of guess, intuition,hunch and decisions backed by Data .
Data Science is ranked the Sexiest Job Of 21st Century by Harvard Business Review. Today there is a tremendous demand for everything "Data Science", Companies need "Data scientists", IT resources are refocusing themselves to be the "Data scientists". Contrary to popular beliefs that Marketing benefits a lot from data science, companies are finding benefits across the spectrum of their operations . Example : A leading Trucking company used Data mining skill to predict which part of the truck is going to break next instead of replacing it at specific intervals, a Leading insurer predicted those who will complete their antibiotic course based on their home ownership history. If this type of stories and scope interests you, read the book "Big Data: A Revolution That Will Transform How We Live, Work, and Think".
I am an aspiring "Data Scientist" and so this review will have a slight tilt from a "Data Scientist" perspective over the business user.
WHAT THIS BOOK IS ?
This book is very well written ,but not for the faint heart. It is a text book and authors have taken lot of care so general audience can also benefit from it, and also not to dilute it's textbook value. To get the full benefit of the book, read about 50 pages ( Do not flip pages), never more than 10 -15 pages per session. The book is intense so you will need to take a break in between or will lose the thread. Once you are finished with fifteen pages, go to the first page and read , highlight the important areas and then go to the next page. So plan to read this book in a span of 2 -3 months. I know it is slow but if you want to understand the inner workings of "Data science", there is not much other option. Alternative is to flip across several superficial articles that is a staple diet of every blog and magazines.
WHAT THIS BOOK WILL DELIVER ?
When you are finished with the book, you should have a fairly good understanding of data science, For example, what type of analysis that needs to be done to identify
A. Will the Customer switch loyalty ? ( Yes / No )
B. What type of customers will cancel my subscription ? ( Ex : Middle Aged male from Manhattan will be 5% more likely to switch)
C. What are the methodologies to identify If I can up-sell a customer ( Ex : Someone who bought this book also bought )
D. What is a supervised Segmentation and When will you use it ? ( When the target is clear, if the person will default on his loan)
E. What is the significance of entropy in Data Science ?
F. Exposure to several formula's ( sleep triggers as I call it). Many of the tools have in-built formula's but you still need some idea what these formula's are.
G. Don't get defensive, be comfortable when your colleague sprinkles words like like Classification ,regression, Similarity Matching, Clustering, Modelling, Entropy etc.
WHAT ELSE YOU WILL NEED ?
Data Science does not exist in silo. It helps in decision making . So should be your learning, Here are my suggestions:
1. First and foremost, you need to spend consistent time. If you are running short of time, don't even bother to start
2. For those who are interested in understanding Data science, courseera dot org conducts a free 8 weeks course on "Introduction To Data Science" by an eminent Stanford Professor. It needs time and Commitment
3. You can get real life examples to work on in coursesolve dot org ( ex: Analyze the sleep cycle)
4. As a Data Scientist, you will need to understand "Big Data" . Browse an article and even experts use Data Science and Big Data interchangeably. Hadoop is the core of Big Data,but it is a world of it's own.
5. Read and start experimenting with Hadoop , PIG , HIVE, HBASE and the variations it offers. I did a basics training at edureka dot in , an Indian firm, not a great training but enough for you to understand and then go on your own. But if time and money permits, go to cloudera website and sign up for training. you will not go wrong
6. I signed up for Amazon elastic map reduce which has a higher level abstraction (for developers it is the difference between using sqlplus vs TOAD). It is not free but very cheap.
7. Try to be the "umbilical cord that looks for a stomach to plug ", look for a mentor, look for opportunity in your firm or elsewhere to grow your Data scientist skills.
For those looking for inspiration , google for Rayid Ghani, Chief Data Scientist at Obama 2012 Campaign.
5 of 5 people found the following review helpful.
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Excellent Introductory Summary Of Data Science
By William P Ross
Data Science for Business is an ideal book for introducing someone to Data Science. The authors have tried to break down their knowledge into simple explanations. I am skeptical of non-technical Data Science books, but this one works well.
In the beginning we are shown the motivations for Data Science and what fields they apply to. Some examples include movie recommendations, credit card charges, telecom churn rate, and automated analysis of stock market news. The book avoids going into the highly technical parts of creating the system but gives you links for where to go.
They do not really reveal the whole Data Science stack. For example Hadoop was mentioned as an implementation of MapReduce but they said going into Hadoop configuration would be too detailed for this type of book. I tended to agree, and even being a progammer myself, I thought they made the right choice to leave that out.
Where the book shines is in the explanations. I am very familiar with expected value calculations and there was a chapter on this. It was a much better high level discussion than I have seen elsewhere, and they mentioned possible pitfalls of the expected value framework.
I liked that the emphasis was on deciding what problem to solve in Data Science. The title of the book is appropriate as it is not just about analyzing data, but figuring out the business case. If you are new to Data Science or looking to get a high level overview this book is an great place to start.