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on 16 January 2014
In a nutshell: If you are looking for a simple (but not simplistic) introduction to nearly all of the underlying data science fundamentals then look no further, because this is the book for you!

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!
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Some good parts but waffles so much reading is like chewing old boots, endless points highlighted as 'important' which are started and then described as discussed elsewhere or out of scope for the book, too much padding and blabbering between the useful bits.
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on 19 March 2018
A good book for a business person to learn about data science. But IMO a poor choice for learning how to be a data scientist, there's very little practical teaching in the volume.
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on 1 February 2016
Only 5 chapters in and I have already learnt so many new concepts ,that have revolutionised how I work with data. A clear and well written book, will be in my library for a long time to come.
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on 3 June 2016
The amount of detail packed into this book is incredible.
I found the content to be relatively clear with some pages requiring a re-read.
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on 18 July 2017
A very good read, but you need to be a student of Data Analytics to get the full value out of the book. I'm not but I found it to be useful.
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TOP 500 REVIEWERon 26 September 2014
As Foster Provost and Tom Fawcett explain in the Preface, they examine concepts that fall within one of three types:

"1. Concepts about how data science fits into the organization and the competitive landscape, including ways to attract, structure, and nurture data science teams; ways for think about how data science leads to competitive advantage; and tactical concepts for doing well with data science projects.

2. General ways of thinking data, analytically. These help in identifying appropriate data and consider appropriate methods. The concepts include the [begin italics] data mining process [end italics] as well as the collection of different [begin italics] high-level data mining tasks. [end italics]

3. General concepts for actually extracting knowledge from data, which undergird the vast array of data science tasks and their algorithms."

There you have the nature and extent of the WHAT on which the information, insights, and counsel focus. Provost and Fawcett devote most of their attention to explaining HOW to apply these concepts to achieve high-impact data mining driven by data-analytic thinking. I share their belief "that explaining data science around such fundamental concepts not only aids the reader, it also facilitates communication between and among business stakeholders and data scientists. It provides a shared vocabulary and enables both parties [data scientists and non-data scientists such as I] to understand each other better. The shared concepts lead to deeper discussions that may uncover critical issues otherwise missed."
These are among the dozens of business subjects and issues of special interest and value to me, also listed to indicate the scope of Provost and Fawcett's coverage.

o From Big Data 1.0 to Big Data 2.0 (Pages 9-13)
o From Business Problems to Data Mining Tasks(19-23)
o The Data Mining Process. (26-34)
o Other Analytics Techniques and Technologies (Pages 35-41 and 187-208)
o Selecting Informative Attributes (49-56)
o Supervised Segmentation with Tree-Structured Models (62-67)
o Class Probability Estimation and Logistic "Regression" (97-100)
o Overfitting (113-119)
Note: This is a tendency to tailor models to the training data.
o Correlation of Similarity and Distance (142-144)
o Some Important Technical Details Relating to Similarities and Neighbors (157-161)
o Stepping Back: Solving a Business Problem Versus Data Exploration (183-185)
o A Key Analytical Framework: Expected Value (194-204)
o A Model of Evidence Lift" (244-246)
o Decision Analytic Thinking II: Toward Analytic Engineering (279-289)
o Co-occurrences and Associations: Finding Items That Go Together 292-298)
o Bias, Variance, and Ensemble Methods 308-311)
o Sustaining Competitive Advantage with Data Science (318-323)

As I worked my way through the book a second time, in preparation to compose this review, I was again reminded of comments by Eric Schmidt, executive chairman of Google: "From the dawn of civilization until 2003, mankind generated five exabytes of data. Now we produce five exabytes every two days...and the pace is accelerating." Correspondingly, the challenges that this process of data accumulation creates will become even greater. Provost and Fawcett wrote this book for those who must manage this process but also to assist the efforts of instructors who are now preparing them to do so.
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on 7 June 2015
Brilliant book. Strongly recommended.
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on 27 January 2015
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on 23 March 2017
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