Data Smart: Using Data Science to Transform Information Into Insight Paperback – 22 Nov 2013
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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.
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Thankfully I didn't, because it is a difficult subject matter, but there aren't any problems with the writing or content.
I have had to over it quite a few times to "get it" and there are still things I don't fully understand, but at least I can replicate to get results. Some readers might be able to grasp it first time, but I think many won't unless they already know about things such as Clustering and Exponential Smoothing.
If you are looking for a basic intro to Data Analysis you should avoid this, but if you are looking to really get stuck in, then buy it. Be warned what you are letting yourself in for: just browsing it or reading once and putting on the bookshelf will be of no use whatsoever. I've spent at least 30 hours on it so far.
Overall the content and the ideas are brilliant, and the guy is really really funny - still doesn't make it any easier.
Those with a strong background in statistics and SAS/SPSS/R will find it interesting and find it useful for the purposes of helping other understand what they are doing (i.e. it isn't magic) - but the target audience would be for those trying to gain business insight (probably in SME's) who are technically able, but who only know and / or have access to one tool for solving the problem - i.e. Excel. There are a lot of people in this situation, and this book could dramatically widen their horizons when considering issues such as optimisation or customer insight.
Ultimately the author himself ends the book by stating that Excel isn't really the right tool for the job, and introduces R as an open source alternative. The demographic at which this book is aimed would most likely would not have considered this a possible path before they picked up this book, but I believe by the end of it most will give it proper consideration as a means to best apply the methods detailed within the book.
As an aside, the author has a quirky and amusing style, which went down well at least with me, such that I read the first 4 chapters in one sitting...
So far, I've learnt techniques for segmentation, how to analyze text data and an optimization scenario. I'm looking forward to applying these to work.
The Excel primer in the first chapter should be printed and distributed to the whole company - it's perfect, contains all I taught to my team.
The author has a great sense of humour. Also, he is a top class on what he does...otherwise he wouldn't lead the analytics team at mailchimp.
This book will help those transitioning from being good at Excel, to having the basic knowledge concerning data science. Excellent.
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