Free PDF Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
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Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Free PDF Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
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Review
"A must-read resource for anyone who is serious about embracing the opportunity of big data."-- Craig VaughanGlobal 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."--Tom PhillipsCEO 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."--Â Alan MurraySerial 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 BekkermanChief 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 KohaviPartner Architect at Microsoft Online Services Division
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Book Description
What you need to know about data mining and data-analytic thinking
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Product details
Paperback: 414 pages
Publisher: O'Reilly Media; 1 edition (August 19, 2013)
Language: English
ISBN-10: 1449361323
ISBN-13: 978-1449361327
Product Dimensions:
7 x 0.9 x 9.2 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review:
4.5 out of 5 stars
192 customer reviews
Amazon Best Sellers Rank:
#10,162 in Books (See Top 100 in Books)
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.
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!
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.
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.
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