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Tìm sách theo yêu cầu
Book Description
Publication Date: November 4, 2013 | ISBN-10: 111866146X | ISBN-13: 978-1118661468 | Edition: 1
Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.
But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.
Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.
Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
Editorial Reviews
From the Back Cover
"Data Smart makes modern statistic methods and algorithms understandable and easy to implement. Slogging through textbooks and academic papers is no longer required!"
—Patrick Crosby, Founder of StatHat & first CTO at OkCupid
"When Mr. Foreman interviewed for a job at my company, he arrived dressed in a 'Kentucky Colonel' kind of suit and spoke about nonsensical things like barbecue, lasers, and orange juice pulp. Then, he explained how to de-mystify and solve just about any complex 'big data' problem in our company with simple spreadsheets. No server clusters, mainframes, or Hadoop-a-ma-jigs. Just Excel. I hired him on the spot. After reading this book, you too will learn how to use math and basic spreadsheet formulas to improve your business or, at the very least, how to trick senior executives into hiring you as their data scientist."
—Ben Chestnut, Founder & CEO of MailChimp
"You need a John Foreman on your analytics team. But if you can't have John, then reading this book is the next best thing."
—Patrick Lennon, Director of Analytics, The Coca-Cola Company
Most people are approaching data science all wrong. Here's how to do it right.
Not to disillusion you, but data scientists are not mystical practitioners of magical arts. Data science is something you can do. Really. This book shows you the significant data science techniques, how they work, how to use them, and how they benefit your business, large or small. It's not about coding or database technologies. It's about turning raw data into insight you can act upon, and doing it as quickly and painlessly as possible.
Roll up your sleeves and let's get going.
Relax — it's just a spreadsheet
Visit the companion website at www.wiley.com/go/datasmart to download spreadsheets for each chapter, and follow them as you learn about:
- Artificial intelligence using the general linear model, ensemble methods, and naive Bayes
- Clustering via k-means, spherical k-means, and graph modularity
- Mathematical optimization, including non-linear programming and genetic algorithms
- Working with time series data and forecasting with exponential smoothing
- Using Monte Carlo simulation to quantify and address risk
- Detecting outliers in single or multiple dimensions
- Exploring the data-science-focused R language
About the Author
John W. Foreman is Chief Data Scientist for MailChimp.com, where he leads a data science product development effort called the Email Genome Project. As an analytics consultant, John has created data science solutions for The Coca-Cola Company, Royal Caribbean International, Intercontinental Hotels Group, Dell, the Department of Defense, the IRS, and the FBI.
Product Details
- Paperback: 432 pages
- Publisher: Wiley; 1 edition (November 4, 2013)
- Language: English
- ISBN-10: 111866146X
- ISBN-13: 978-1118661468
- Product Dimensions: 9.3 x 7.4 x 0.8 inches
- Shipping Weight: 1.4 pounds (View shipping rates and policies)
- Average Customer Review: 4.8 out of 5 stars See all reviews (36 customer reviews)
- Amazon Best Sellers Rank: #4,225 in Books (See Top 100 in Books)
- #1 in Books > Textbooks > Computer Science > Artificial Intelligence
- #2 in Books > Computers & Technology > Computer Science > Modeling & Simulation
- #2 in Books > Computers & Technology > Computer Science > Information Theory
Most Helpful Customer Reviews
90 of 92 people found the following review helpful
5.0 out of 5 stars Insightful, practical, and colorful. Perspective from a biased reviewer. November 5, 2013
By Evan Miller
Format:Paperback|Verified Purchase
Disclaimer: I served as a paid technical editor for Data Smart. I am not affiliated with the publisher, but I did receive a small fee for double-checking the book's mathematical content before it went to press. I also went to elementary school with the author. So as you read the rest of the review, keep in mind that this reviewer's judgment could be clouded by my lifelong allegiance to Lookout Mountain Elementary School, as well as the Scarface-esque pile of one dollar bills currently sitting on my kitchen table.
Anyway, books about "Data" seem to fit into one of the following categories:
* Extremely technical gradate-level mathematics books with lots of Greek letters and summation signs
* Pie-in-the-sky business bestsellers about how "Data" is going to revolutionize the world as we know it. (I call these "Moneyball" books)
* Technical books about the hottest new "Big Data" technology such as R and Hadoop
Data Smart is none of these. Unlike "Moneyball" books, Data Smart contains enough practical information to actually start performing analyses. Unlike most textbooks, it doesn't get bogged down in mathematical notation. And unlike books about R or the distributed data blah-blah du jour, all the examples use good old Microsoft Excel. It's geared toward competent analysts who are comfortable with Excel and aren't afraid of thinking about problems in a mathematical way. It's goal isn't to "revolutionize" your business with million-dollar software, but rather to make incremental improvements to processes with accessible analytic techniques.
I don't work at a big company, so I can't attest to the number of dollars your company will save by applying the book's methods. Read more ›
Anyway, books about "Data" seem to fit into one of the following categories:
* Extremely technical gradate-level mathematics books with lots of Greek letters and summation signs
* Pie-in-the-sky business bestsellers about how "Data" is going to revolutionize the world as we know it. (I call these "Moneyball" books)
* Technical books about the hottest new "Big Data" technology such as R and Hadoop
Data Smart is none of these. Unlike "Moneyball" books, Data Smart contains enough practical information to actually start performing analyses. Unlike most textbooks, it doesn't get bogged down in mathematical notation. And unlike books about R or the distributed data blah-blah du jour, all the examples use good old Microsoft Excel. It's geared toward competent analysts who are comfortable with Excel and aren't afraid of thinking about problems in a mathematical way. It's goal isn't to "revolutionize" your business with million-dollar software, but rather to make incremental improvements to processes with accessible analytic techniques.
I don't work at a big company, so I can't attest to the number of dollars your company will save by applying the book's methods. Read more ›
21 of 21 people found the following review helpful
5.0 out of 5 stars Reminds you that technical books can be insightful and fun to read December 20, 2013
Format:Paperback
When I began to read the introduction for this book, after receiving it as a gift - I was a bit disheartened. I am not one of personas listed in the 'Who Are You" section - a CEO or VP of an online startup, a beginner BI analyst. Instead, I am a software developer specializing in data visualization and data analysis.
Furthermore, Excel is far from my preferred research tool of choice. I like code instead of screenshots. Python, Ruby, and R are where I turn when I want to look at data.
*Even* with this mismatch of intended audience, I found myself engrossed in this book, reading it cover to cover in a few days.
Data Smart is a wonderful resource. The use of Excel as a primary means for exploring data science concepts is surprisingly effective. It strips away all the code magic. You can't rely on SciKit-learn, or Weka, or even proper functions when all you have are cells and sheets.
Instead, it provides a way for John Foreman to break down these complex concepts into the fundamental components that make them tick. You start to see the patterns between seemingly disparate technologies that are actually built off the same few bits of logic. Things start to click.
The writing and real-world situations are really what make it fun and worth reading through and enjoying the ride. John's style hits the sweet spot between clarity and comical. Each chapter is well scoped. You understand the rational behind why someone might want to use the particular tool being described to solve the problem at hand. The whimsy and flare added by the author moves the plot along at a good pace. The problems are simple enough to wrap your head around - but not toys. The datasets generated for this book must have taken a while to curate. Read more ›
Furthermore, Excel is far from my preferred research tool of choice. I like code instead of screenshots. Python, Ruby, and R are where I turn when I want to look at data.
*Even* with this mismatch of intended audience, I found myself engrossed in this book, reading it cover to cover in a few days.
Data Smart is a wonderful resource. The use of Excel as a primary means for exploring data science concepts is surprisingly effective. It strips away all the code magic. You can't rely on SciKit-learn, or Weka, or even proper functions when all you have are cells and sheets.
Instead, it provides a way for John Foreman to break down these complex concepts into the fundamental components that make them tick. You start to see the patterns between seemingly disparate technologies that are actually built off the same few bits of logic. Things start to click.
The writing and real-world situations are really what make it fun and worth reading through and enjoying the ride. John's style hits the sweet spot between clarity and comical. Each chapter is well scoped. You understand the rational behind why someone might want to use the particular tool being described to solve the problem at hand. The whimsy and flare added by the author moves the plot along at a good pace. The problems are simple enough to wrap your head around - but not toys. The datasets generated for this book must have taken a while to curate. Read more ›
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