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Data Science for Business: What you need to know about data mining and data-analytic thinking
 
 

Data Science for Business: What you need to know about data mining and data-analytic thinking [Kindle Edition]

Foster Provost , Tom Fawcett
5.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)

Kindle-Preis: EUR 15,13 Inkl. MwSt. und kostenloser drahtloser Lieferung über Amazon Whispernet

  • Länge: 413 Seiten
  • Sprache: Englisch
  • Aufgrund der Dateigröße dauert der Download dieses Buchs möglicherweise länger.
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Kindle Edition, 27. Juli 2013 EUR 15,13  
Taschenbuch EUR 26,96  


Produktbeschreibungen

Kurzbeschreibung

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

Über den Autor und weitere Mitwirkende

Foster Provost is a Professor and NEC Faculty Fellow at the NYU Stern School of Business, where he has taught data science to MBAs for 15 years. His research and teaching focus on data science, machine learning, business analytics, (social) network data, and crowd-sourcing for data analytics. Tom Fawcett has a Ph.D. in machine learning from UMass-Amherst and has worked in industrial research (GTE Laboratories, NYNEX/Verizon Labs, HP Labs, etc.). He has served as action editor of the Machine Learning journal, before which he was an editorial board member.

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0 von 4 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Das Probekapitel ist denkbar schlecht. Buch aber gut. 15. Oktober 2013
Format:Taschenbuch
Nicht mal ein Inhaltsverzeuchnis ist einzusehen - wo die tollen Bewertungen herkommen ist mir ein Rästen.
Muß hintenraus richtig gut werden, den der Start ist echt schwach.

Tja, dennoch muss ich fairerweise sagen, dass das Buch selbst klasse ist. Genau richtig um mal in das Thema reinzukommen.
Genug mathematischer Hintergrund um zu verstehen wie die Leute ticken.
wirklich gut - und es bleibt dabei, ds Probekapitel ist echt schlecht.
War diese Rezension für Sie hilfreich?
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Amazon.com: 4.8 von 5 Sternen  54 Rezensionen
44 von 45 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen The perfect balance 10. August 2013
Von m l - Veröffentlicht auf Amazon.com
Format:Kindle Edition
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.
21 von 22 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen An Outstanding, Readable and Efficient Data Analytics Practitioner's Guide 9. August 2013
Von Joseph Berwind - Veröffentlicht auf Amazon.com
Format:Kindle Edition
Excellent! Data Science for Business is an extremely well written practitioner's guide. The concepts and methodologies found inside ARE NOT EASY TO FIND in one comprehensive, comprehendible, and no-nonsense book. I received the book initially as part of my Master of Science in Business Analytics curriculum, and still, I purchased the eBook to have with me and use for my work. In fact, the principles and techniques covered in the book reshaped my understanding of big data business analytics and exactly how to do it and do it right.
24 von 26 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Data Science for MBA's 28. Oktober 2013
Von William R. Franklin - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Vine Kundenrezension eines kostenfreien Produkts (Was ist das?)
"Data Science", "Data Mining" and "Predictive Analytics" are some of the terms of recent vintage that define the application of modern mathematics, enabled by twenty-first century computing power, to identify patterns in the staggering quantities of data that can be so easily acquired and accessed. This new technology has such important ramifications in areas ranging from market behaviour to cyber security that even laymen need to have an understanding of its far reaching implications. One recent effort to make these ideas intelligible to the non-expert, the rather puerile "Predictive Analytics" by Eric Siegel, failed miserably. This book comes much closer to the mark.

With a readable, almost conversational, style Provost and Fawcett describe some of the fundamental notions in data science, casually discussing such standard topics as supervised and unsupervised learning, clustering, regression, linear discriminants, model building and even a pseudo introduction to Support Vector Machines. There is also a chapter on the danger of over fitting, a common malady afflicting those new to machine learning. Of course, the authors' stated desire to avoid any genuine maths renders some of the descriptions opaque and even misleading. Nevertheless, the alert layman will come away with a decent familiarity with some of the concepts and methods employed in this rapidly evolving technology.
13 von 13 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Most practical data mining book! 12. August 2013
Von Rong Zheng - Veröffentlicht auf Amazon.com
Format:Kindle Edition
This book tells you how to **think** from the angle of data when you make decisions. I have read so many data mining books. They often claim themselves practical simply because they provide examples in addition to the technical details. Well, this statement can be seriously misleading since no one is going to solve the same problem as the one in the book. Without a good explanation on the intuition underlying the technique, it is hard to make true links with examples and eventually even harder, if not possible, to extend what you read to what you need to solve in real applications.

This book does an excellent job in this perspective. All the fundamental DM ideas (although there are so many different DM algorithms, they are all variations of only a few fundamental ideas) are explained by almost plain words illustrating human's thinking process. You will feel all the DM methods are familiar even though you have never learned them because they are presented just as a codification of rational thinking in everyday life. Once the intuition is uncovered this well, the examples in the book look so natural and you get a way to start doing your own DM tasks.

It can be your first DM book or an insightful book worth revisiting from time to time. I, as a DM educator, enjoy reading the book and learn a lot not only the insights but also how to transmit DM knowledge effectively.

I love this book!
22 von 25 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Data Mining? I do that too! 25. August 2013
Von Chris Allen - Veröffentlicht auf Amazon.com
Format:Taschenbuch
Foster Provost and Tom Fawcett have set out to write the go-to reference on Big Data. 'Data Science for Business', what you need to know about Data Mining and Data-Analytic Thinking, published by O'Reilly Media.

They have produced an authoritative book that is both a pathfinder and a lighthouse. It is a long, clearly-written book that shows what can be done using Big Data, where to go and what techniques to use to get it done, and what to watch out for.
Thank you for writing this book. The authors and their many references are already established and respected. The book brings the issues and their business applications together in one essential place. Already in just 1 month since release (25th July 2013) the eBook has gathered praise quotes from a dozen industry names. I am honoured to receive a complimentary review copy.
So to add to the recommendations, I pitch my review slightly differently: Who in business should buy this book? What does this book add to what we are already doing in business with Data and Data Mining?

On first reading, if you work in analysis, IT, Business Intelligence, Management Reporting, Marketing or SEO, I guarantee your reaction at some point will be 'I do that too'.

For me the 'Aha!' realisation came a few pages into chapter 2. The authors discuss database searches for the most profitable items in a business. All businesses do that every day! But not always in the way the academics think.
The book surprised me in covering a broader range of topics than I previously considered were Data Science. Here are some great success stories to illustrate what data science is. Buy the book to see how these things really work and how the leading companies are applying themselves to these challenges. These studies border on the commercially sensitive.

- How a supermarket can use their sales analysis to predict when people are expecting a baby, and so gain an advantage by making offers before their competitors.
- How advertisers use Facebook Likes to profile and segment their audience
- How Netflix make their movie recommendations
- How to compare web pages for plagiarism
- How to tell how far away a customer is from their mobile app
Chapter 10 talks about text analysis. In contrast to most of the book, I would say here that small and medium sized businesses are ahead of Google and the academics. While the search engines refine their algorithms to extract news and meaning from bare text, there is whole industry sector manipulating the source data to fool the algorithms and keep one step ahead: it is called Search Engine Optimisation.

If you are just starting out in using Big Data for your business decisions, you need to know the importance of Maths. In particular there are 2 challenges in the mathematics that underpin Data Science that I should warn you about even if you do not read the book:

* One is causation and correlation. When you find the beer-buying customers are also the nappy-buying customers, that is just the first step towards some very careful thinking before you draw any conclusions about which is cause and which is effect and how you might adjust your marketing or product mix to assist your customers accordingly
* The other is what is now called 'Overfitting'. Gaze hard enough and you will find trends in data just like you can find shapes in clouds or patterns on the back of your eyelids. If you search too hard through too much data, you invalidate correlation co-efficients and confidence calculations. Or to put it another way, every cloud looks like something.

A great book. For everyone who can still manage their high-school level maths, I recommend you buy this book. For everyone else, I recommend you be aware of the book and the issues within it and get it on the corporate bookshelf. For myself I look forward to checking back regularly for future editions as the science develops. Five stars.
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