- File Size: 15993 KB
- Print Length: 617 pages
- Simultaneous Device Usage: Unlimited
- Publisher: O'Reilly Media; 1 edition (July 27, 2013)
- Publication Date: July 27, 2013
- Sold by: Amazon.com Services LLC
- Language: English
- ASIN: B00E6EQ3X4
- Text-to-Speech: Enabled
- Word Wise: Enabled
- Lending: Not Enabled
- Amazon Best Sellers Rank: #35,367 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking 1st Edition, Kindle Edition
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From the Publisher
|Data Science for Business||Data Science from Scratch||Doing Data Science||R for Data Science||Data Science at the Command Line||Python Data Science Handbook|
|What You Need to Know about Data Mining and Data-Analytic Thinking||First Principles with Python||Straight Talk from the Frontline||Visualize, Model, Transform, Tidy, and Import Data||Facing the Future with Time-Tested Tools||Tools and Techniques for Developers|
About the Author
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.
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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.
Terrible quality on amazon's part and this book is frequently used as a college text book, so knowing the equations are essential.
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!
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.
Top international reviews
I work primarily as a software developer, and like to consider that I have good general knowledge and experience of what data science ('data analytics', 'big data' etc.) is through College/Uni education and also the modern press and blog posts etc. However, I often struggled at times to fully understand, and perhaps more importantly knit together and apply, the core fundamentals of the topic. This book has provided exactly the explanations and 'glue' that I required, in that it delivers a very well structured (and paced) introduction and overview of data science, and also how to think in a 'data-analytics' manner.
If you preview the book with the 'look inside' feature then what you see in the table of contents is exactly what you get. Every chapter delivers upon its title (and promised 'fundamental concepts'), and frequently builds superbly upon topics introduced in early chapters. You'll move seamlessly from understanding how to frame data science questions, to learning about correlation and segmentation, to model fitting and overfitting, and on to similarity and clustering. With a brief pause to discuss exactly 'what is a good model' you'll then be thrust back into learning about visualising model performance, evidence and probabilities and then how to explore mining text.
The concluding chapters draw upon and summarise how to practically choose and apply the techniques you've learnt, and provide great discussion on how to solve business problems through 'analytical engineering'. There is also some bonus discussion on other tools and techniques that build upon earlier concepts which you might find useful, data science and business strategy, and some general thinking points around topics such as the need to human intervention in data analysis and privacy and ethics.
The book is superbly written and reads very easily, which for the potentially dry topic of data science is worthy of praise alone. The majority of chapters took me each approximately an hour to read, and then another couple of hours to re-read and ponder upon (and sometimes looking at other provided references) to fully understand some of the more complex topics and how everything related together. Each chapter also provided plenty of pointers and experimentation ideas if I wanted to go away and practically explore the topic further (say, with the Mahout framework, or R, or scikit-learn/Pandas etc.). The book could probably be read by dipping in and out of chapters, but I think you'll get a whole lot more from a cover-to-cover reading.
In summary, this is a superb book for those looking for a solid and comprehensive introduction to data science and data analytics for business, and I'm sure will that even the more experienced practitioners of the art will find something useful here. The book introduces topics in a perfect order, superbly builds your knowledge chapter after chapter, and constantly relates and reinforces the various techniques and tools your learning as it progresses. I wish more text/learning books were written this well!
The contents are good, well organised and they cover most of what you need to know.
The approach is not theoretical but practical and to the point.
The examples are also good as it is the level of detail.
And you have enough references to go deeper if you need.
Great job, I would love to have a second book to go deeper.
Has amazing coverage of topics, is very clear and thorough, and most importantly offers very inspiring and helpful examples of how various data science techniques can be applied that is not easy to find in any other book. The book also has great discussions on the field of data science in business and how to structure/manage data science teams.
Credo sia adatto a tutte le diverse tipologie di soggetti: lo sviluppatore, il manager, il dirigente, l'operativo, il ricercatore, l'analista... C'è materiale per tutti e il linguaggio è tarato in base alle diverse tipologie di interlocutore.
ATTENZIONE: è in inglese
A prima vista questo può sembrare scontato per un libro con un titolo del genere (e in effetti ce ne sono tanti in giro di questo tipo) ma la superiorità di questo testo sta nella linearità e chiarezza di esposizione e nella pragmaticità nell'affrontare i diversi casi aziendali.