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Data Science for Business: What you need to know about data mining and data-analytic thinking
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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.
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Paperback, 414 pages
Published
August 16th 2013
by O'Reilly Media
(first published January 1st 2013)
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Since I wrote it I think it's excellent
This is probably the most practical book to read if you are looking for an overview of data science, either so you can be in the know when terms like k-means and ROC curves are being bandied about or so you have some context when you start digging deeper into how some of these algorithms are implemented (esp when plowing through a book like The Elements of Statistical Learning: Data Mining, Inference, and Prediction).
I found it to be at just the right level because there is just enough math to ...more
I found it to be at just the right level because there is just enough math to ...more
When people say that data science is the way of the future, I break into a bit of a cold sweat because there’s an implication that I’m going to have to read another book filled solely with equations and proofs. It’s rare to find a book where you can get into the grit of a scientific framework without getting too bogged down by endless abstraction. However, Provost and Fawcett manage to soften the blow of overtly academic writing, while simultaneously fostering an intricate understanding and
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Data Science is quite a buzz word these days, though what it really entails is unclear. It's a porous field and you need to know a thing or two about it before you get sold hot air couched as substance. When I saw a friend mark this book as to read I decided to check it out and at least have a working definition of the current state of data science.
This is a good introductory book though to fully get it you need a working knowledge of basic statistics or be tenacious enough to be always ...more
This is a good introductory book though to fully get it you need a working knowledge of basic statistics or be tenacious enough to be always ...more
An an engineer I didn't like this book, it is too shallow. As a founder of a company doing data science I found some good business and management insights. I wish the authors had focused more on the business stuff. For business people I recommend the initial and final chapters.
Provost and Fawcett do a fantastic job of describing the main techniques used in data mining - classification, clustering and regression - along with high level explanations of the algorithms most commonly used for each. In addition, they present an expected value framework that is very useful for choosing the right balance between true positives, false positives, etc. in the predictions of a model.
Data Science for Business is by no means an easy read for even technical readers, unless you have ...more
Data Science for Business is by no means an easy read for even technical readers, unless you have ...more
Jun 05, 2018
Diana Nassar
added it
A thorough introduction to Data Science concepts and techniques. While this book, as the name implies, targets business professionals mainly, I personally think it is best to read it after [or while] getting your hands dirty with data science problems or taking an online course to establish a technical foundation first -- it depends on your background. I did this and I found it more relevant this way.
Good choice of real-world everyday business cases to keep things in context and illustrate the ...more
Good choice of real-world everyday business cases to keep things in context and illustrate the ...more
I set this book aside a little over a year ago, and this morning I've decided that I probably won't finish it. With my background in databases and business intelligence reporting, I thought I would really enjoy this book. I appreciated about the first half of the book. As I recall, the latter half of the book mined deeper into the slight variations of the same theories. This would undoubtedly be fascinating for the reader who wants to begin studying data science. I somehow doubt that many
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A great book for whom that ask about to implement data sciences on its work or projects, the book present from the simpliers ways to the most advanceds uses of matematical aplications; all of it linked with Gerential Skills and Decission making for business
The book is very well written, it explains the data mining tasks in good details, it also shows you how to approach your data mining problem from both its business and technical sides.
Great introductory book to analytics in management without being too technical but exploring the possibilities, applications and how to see when something is off in a project.
Excellent read for managers and newbie. Focuses more on the concept than on the technical framework. Should read by any manager or team leader planning to start a data science project.
Really good introduction to Data Science. Covers important principles that can be applied to many different applications in a way that gets into the technical details, but not too much. Many different profiles can take advantage of the lessons: engineers, managers, sales reps... and of course anyone interested on making data talk, both for specific needs (business-related or not) or just for the sake of discovering value in data. From project planning, data acquisition, exploration, evaluation
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Starts really well.
It is not another author trying to get a best seller under the "Big Data" hype. The book is NOT about algorithms (the information technology part they call it) but about the data science, the stages before and after "data preparation" and "modelling" in CRISP-DM.
It is not another author trying to get a best seller under the "Big Data" hype. The book is NOT about algorithms (the information technology part they call it) but about the data science, the stages before and after "data preparation" and "modelling" in CRISP-DM.
Great introduction and overview to the topic.
In a short summary, I am so glad I came across this book! It’s not only a great introduction to the complex world of data science, it has sparked a real interest in me to dig deeper into this subject.
The book will prove valuable both to readers with business or technical backgrounds, esp. if you want to understand how to harness data to add value to business problems, or how to engage with colleagues working on data mining, analytical models etc.
There are a few mathematical bits to the book – ...more
The book will prove valuable both to readers with business or technical backgrounds, esp. if you want to understand how to harness data to add value to business problems, or how to engage with colleagues working on data mining, analytical models etc.
There are a few mathematical bits to the book – ...more
Scanned through it really quickly.
The book does a good job explaining the most commonly used concepts in data science from a "business" perspective, as the title suggests. It delves sufficiently deep into the mathematics and goes through some good examples too.
The only serious limit this book has is that it doesn't give you actual code examples or pseudo-code of the algorithms. So if your goal is to learn how to implement these algorithms, you'll need to go further than this book, but if your ...more
The book does a good job explaining the most commonly used concepts in data science from a "business" perspective, as the title suggests. It delves sufficiently deep into the mathematics and goes through some good examples too.
The only serious limit this book has is that it doesn't give you actual code examples or pseudo-code of the algorithms. So if your goal is to learn how to implement these algorithms, you'll need to go further than this book, but if your ...more
Data Science for Business is a great book to give an overall view of how data analysis can be used in day-to-day business problems. The authors do a really good job of describing a construct or process, and then using examples to really flesh those out into real-life situations. It is overall a good overview of data-analytic thinking and great for those who aren't too familiar with the subject but looking to dip their toes into the field.
At parts, it did feel like there was a lot of "here's a ...more
At parts, it did feel like there was a lot of "here's a ...more
This is a surprisingly good high level overview of data science. It showed up on the new books shelf at our library - I'm surprised it is from 2013 ("ancient" in computer book terms) but this has held its age very well. Definitely written as a communication tool between the "suits" and the "hackers" - perhaps just a little too much math for the suits and definitely not enough depth for the hackers, so a good balance. The books avoids "Sigma" and "Pi" notation (not quite sure, maybe the analytics
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The is a well-written and highly accessible guide for non-tech people with a marketing or business executive background, focusing on the underlying principles and data science techniques to solve real-world problems when using data mining and data analytics for improved business outcomes. The authors present just enough math and technical details on how to use those techniques as well as several business scenarios and examples demonstrating how the tech stuffs fall into place so that readers are
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Indispensable complement to more technical data science books and, overall, a very easy and enjoyable read. A rich selection of business cases (from Whisky list diversification at a liquor store, to stock price movement prediction based on news stories mining, via customer churn management). Thanks to very nice examples and illustrations, I, for example, now better understand (i) purity measure via entropy, and information gain for segmentation, (ii) the importance of expected value for
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I had only a basic understanding of statistics before tackling this book (least square regression mostly). However, I waa able to draw much from this book as it does a great job of showcasing many different techniques while also providing exemples of which type of situations they can be applied. The book really helps expand perspective and guides the reader if he/she wants to delve deaper into certain subjects. Highly recommended for anyone studying business or who is interested in data science
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As a graduated Data Science student, I can say that this book and my Master touched the same topics. I would give it 4,5 stars as it is a great book to understand Data Science. For people who are using this book as a starter into Data Science, I would recommend to also have a look at R or Python and look at a dataset (There are build-in Data sets). This way you can have a clearer image of what Data Science is like. A remark for this book is that you need some understanding of data sets in order
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Not what I was expecting, is not an introductory book at all, maybe the last chapters of you want to know some nitty gritty of hiring a data scientist.
Analytics is not something that you could learn reading, you need to do it and to do it well, and you can do it using excel not only R or python, so if you really want to learn these are the books that you need:
1. Marketing analytics by Wayne Winston
2. Data Smart by John Foreman
Dry reading at several chapters.
Analytics is not something that you could learn reading, you need to do it and to do it well, and you can do it using excel not only R or python, so if you really want to learn these are the books that you need:
1. Marketing analytics by Wayne Winston
2. Data Smart by John Foreman
Dry reading at several chapters.
I read the book and gained an understanding of the data science concepts and terminology then I search for code samples in python to try see how the classifers worked. It was helped to understand what the classifier did and why it could be applied to a business case before programming classifier to run in python. I thought the book was comprehensive and the author had experience solving business problems
A solid read that covers the broad spectrum of applicable data science in a business context. It goes into the various techniques with a fair amount of depth. Not recommended for those looking for a purely business focused read, as it's more for those seriously interested in how data science works, including some fairly complex math. If you are looking at data science and want an introduction that's not afraid to give you some meat in the process, check this out.
I HIGHLY RECOMMEND this book to those who are trying to understand the concepts of data science. He kept the difficult math formulas to the minimum and focused on the core ideas, which made it really easy to read and comprehend. Since I'm watching Andrew Ng's courses online, this book was the perfect complimentary resource. There are difficult chapters, but still overall he has a wonderful way of explaining.
Very technologically accessible verbiage and presentation of concepts for business-folk. But not to the point where data scientists or engineers would not find this useful or an enhancement in understanding.
The title of the book is misleading, as one might expect an overview of the challenges and possibilities of data science for business opportunities. However, what one finds is a quite deep explanation of the main aspects of data science: from supervised learning to clustering, from models to optimization via ROC curves. Not one page without interesting and critical information and, even for someone with some hands-on knowledge about DS -albeit without being an expert-, each read required through
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Great introductory book into Data Science. Teaches you various ML techniques used to solve common industry problems and will also help you frame your mindset correctly so you can be confident in your results.
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“The need for managers with data-analytic skills The consulting firm McKinsey and Company estimates that “there will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” (Manyika, 2011). Why 10 times as many managers and analysts than those with deep analytical skills? Surely data scientists aren’t so difficult to manage that they need 10 managers! The reason is that a business can get leverage from a data science team for making better decisions in multiple areas of the business. However, as McKinsey is pointing out, the managers in those areas need to understand the fundamentals of data science to effectively get that leverage.”
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