Data Smart: Using Data Science to Transform Information into Insight 1st Edition

by John W. Foreman (Author)

336 ratings

Frequently bought together

+
+

Total price:

To see our price, add these items to your cart.

Some of these items ship sooner than the others.

Show details Hide details

Choose items to buy together.

by John W. Foreman Paperback
$20.34
Get it as soon as Friday, Jul 1 FREE Shipping on orders over $25 shipped by Amazon
In Stock.
Ships from and sold by Amazon.com.
by Foster Provost Paperback
$32.49
Get it as soon as Friday, Jul 1 FREE Shipping on orders over $25 shipped by Amazon
In Stock.
Ships from and sold by Amazon.com.
by Eric Siegel Paperback
$19.32
FREE Shipping
In stock.
Usually ships within 3 to 4 days.
Ships from and sold by Book Depository US.

Foster Provost
913
Paperback
67 offers from $13.07
Eric Siegel
381
Paperback
62 offers from $4.03
Alex J. Gutman
147
Paperback
49 offers from $20.25
Cole Nussbaumer Knaflic
3,051
Paperback
in Business Mathematics
74 offers from $17.43
Charles Wheelan
2,146
Paperback
in Statistics
154 offers from $2.53
Roger Peng
42
Paperback
23 offers from $9.99

Editorial Reviews

From the Inside Flap

"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

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

Start reading Data Smart on your Kindle in under a minute.

Don't have a Kindle? Get your Kindle here, or download a FREE Kindle Reading App.

Product details

Publisher ‏ : ‎ Wiley; 1st edition (November 12, 2013)
Language ‏ : ‎ English
Paperback ‏ : ‎ 432 pages
ISBN-10 ‏ : ‎ 111866146X
ISBN-13 ‏ : ‎ 978-1118661468
Item Weight ‏ : ‎ 1.6 pounds
Dimensions ‏ : ‎ 7.3 x 0.8 x 9.1 inches

Best Sellers Rank: #63,179 in Books (See Top 100 in Books)
- #16 in Business Operations Research (Books)
- #29 in Artificial Intelligence (Books)
- #39 in Data Mining (Books)

Customer Reviews:
336 ratings

About the author

Follow authors to get new release updates, plus improved recommendations.

John W. Foreman

Brief content visible, double tap to read full content.

Full content visible, double tap to read brief content.

John is the Chief Data Scientist for MailChimp.com. He's also a recovering management consultant who's done a lot of analytics work for large businesses (Coke, Royal Caribbean, Intercontinental Hotels) and the government (DoD, IRS, DHS).

These days John does all sorts of awesome data science for MailChimp, and he blogs for fun about analytics through narrative fiction at AnalyticsMadeSkeezy.com. Spoiler alert: the characters who do meth are frequently confused or in peril. John does not do meth.

Customer reviews

5 star		69%
4 star		17%
3 star		7%
2 star		4%
1 star		2%

How customer reviews and ratings work

Top reviews from the United States

There was a problem filtering reviews right now. Please try again later.

Lou Gutnicki

Great Book, Lousy References

Reviewed in the United States on June 1, 2016

Verified Purchase

I would go as far as to say that the book is brilliant.
First, a drop about me from the standpoint of this book. I have been an IT professional for many years specializing in programming, database, and MS Office add-ons. Part of my job entails self enrichment, that is, expand my working knowledge in areas potentially important for my job. I chose Foreman's book to help with this task for a number of reasons: a) Data Science is a hot area and my company does have a Data Science group, b) I have lots of data experience under my belt - I felt that it would be nice for once to get some useful information from the data, and c) I have a really good Excel background - so I figured that Foreman's approach would be perfect for me - little did I know that I would seriously add to my Excel bag of tricks.
The author makes the assumptions that: a) the reader is somewhat technical, b) he knows nothing about Data Science, and c) he is relatively comfortable working in Excel.
Reading the book is a joy because Foreman has a cozy, chummy style. He definitely doesn't throw all the technical stuff at the reader rat-tat-tat machine gun style like many other authors. Instead, Foreman gently introduces his topics and then ramps up technical details carefully. This most definitely helps the learning process.
Speaking of learning, by the end of the you will have learned important concepts in "machine learning" and I believe that you will be ready for the next step. I sure was. I found the topics interesting and I wanted to learn more. This is where the book's only problem area comes into play - the next step. Foreman has 3 references - one good, but minor, one terrible, and the other is inappropriate. Let me explain.
Foreman recommends a free resource as a follow-on to his Forecasting Chapter. This is a good reference, but I believe that Forecasting is a minor topic in Data Science, unless, of course, Forecasting becomes your thing.
Foreman's main reference is: "Data Mining with R" by Luis Torgo. Foreman recommends this as the next step after his book.I tried to read this several times, but couldn't. It certainly wasn't my next step.
The other reference, "The Elements of Statistical Learning" by Trevor Hastie, et. al, is totally inappropriate for Data Science newbies. You can checkout the Amazon reviews for this book and you'll see that you need a pretty serious background in statistics to get anything out of that reference. In fact, the author Hastie says as much in his next book "An Introduction to Statistical Learning- with Applications in R". This is the appropriate next step, but I'll get to that in a moment.
Here are my recommendations:
A. Read Foreman's book and follow along with him in working through the Excel spreadsheets. This is a first step in getting comfortable with Machine Learning.
B. Take the Coursera courses: 1) Machine Learning Foundations: A Case Study Approach, and 2) Machine Learning: Regression. The courses are free unless you want completion certificates, in which case there is nominal cost.
C. Now you are ready for: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) This book is also available for free by the authors - check online.

169 people found this helpful

Helpful

Sandro Saitta

How to perform data science in Excel

Reviewed in the United States on May 21, 2017

Verified Purchase

If you want to get started with Data Science and don’t like learning a new language such as R or Python, then this book is a perfect fit for you. Entertaining, Data Smart: Using Data Science to Transform Information into Insight approaches data science from a unusual angle. John W. Foreman has written a book for those who wants to apply data mining without using advanced programming (R, Python, etc.). It doesn’t mean you don’t need to understand what data science is, and this book is very good at explaining it to non-practitioners.

Foreman’s book is written in a nice and funny stile, which makes it an easy read. Data mining algorithms are described with the minimum equations needed. Foreman has written a practical book and thus decided to use Excel as a tool for data science. The book starts with an introduction to Excel and its most famous functions. For data scientists using SAS, R, Python or Matlab, you may discover how powerful Excel is. But you will also see how clumsy it is to use Excel for data science. Whereas you would need a few lines in R, the book will take you through a dozen pages of step by step actions you need to perform to obtain the same in Excel. Not only is it more time consuming but also more prone to errors.

Don’t get me wrong: Data Smart is excellent at explaining how to perform data science in Excel. I just think Excel is not the right tool for it. The book is also a journey into MailChimp, the author’s company. This is nice and provides plenty of examples related to e-mail marketing. The book thus provides quick and high-level description of the problem, followed by Excel steps to solve it. In conclusion, Data Smart is a must read to get a fresh perspective on data science with a “Data Science using Excel” user manual. And for the experts? You can just skip the Excel parts and get insights into the field, with a focus on MailChimp use cases.

14 people found this helpful

Helpful

Adam H

Hilarious. Relevant. Accessible. Those are the only ...

Reviewed in the United States on June 12, 2017

Verified Purchase

Hilarious. Relevant. Accessible. Those are the only words necessary to describe this book. John breaks down really complex concepts in data science so those who *consume* the information understand what the heck is going on.

Too often I'm in a meeting where the data scientist is incapable of articulating what it is they did or found, and the stakeholders in the room nod in agreement despite their obvious incomprehension. This book helps both: the stakeholders understand what's going on with the major themes in data science, and the scientist can learn ways to pragmatically describe their art. Plus, both sides get to laugh. Win-win.

7 people found this helpful

Helpful

Bryan Jensen

A great overview of data science methods using Excel to help one learn

Reviewed in the United States on December 17, 2015

Verified Purchase

John Foreman has an entertaining and thorough writing style that makes this a great book for using Excel to think through several essential, basic data inference methods. While Excel is not the best tool for data science it is very widely used software; he does an adequate job making the inferential methods explained in Excel to be understood and then, at the end of the book, explains how to use the more powerful, free R application (a standard in data science field). (Though if one wants to learn and use R this book is too basic.)

What is nice about him using Excel (with data sets that are also available for download from the publishers site) is that all the extra steps Excel requires to carry out this methods is that it also helps the reader to better grasp the reasoning of these methods along the way. That he peppers the text with a humorous style also makes this a rare treasure for data science introductory books.

6 people found this helpful

Helpful

Top reviews from other countries

J. Norman

cool author, and written with humour. Unfortunately this does not make it any easier

Reviewed in the United Kingdom on October 4, 2014

Verified Purchase

I almost gave this a lower rating because it is a challenging read - I thought a couple of times of packing it in halfway through which never makes you feel good about a book.

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.

17 people found this helpful

JJ McChesney

Brilliant.

Reviewed in the United Kingdom on October 27, 2019

Verified Purchase

A lot has been written about data science, most of it just waffle or too technical. This book is brilliant. Pitched at the right level, hands-on and gives you a tool set you can actually use. Can't recommend this book enough.

j b taylor

Makes relatively complex concepts very simple

Reviewed in the United Kingdom on May 28, 2014

Verified Purchase

This book is an excellent practical application of commonly used advanced analytical techniques for data analysis in Excel. I was skeptical that this was possible before purchasing the book and partly bought out of curiosity of to what extent it could be pulled off, but I was pleasantly surprised.

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...

10 people found this helpful

D Edwards

A brilliant foundation with actionable explanations

Reviewed in the United Kingdom on December 4, 2014

Verified Purchase

I took delivery of this book 4 days ago. Since then I was posed a question by a client who wanted to look at 3 year forecast of marketing potential. I am comfortable with this however the data they had was not brilliant and there were a lot of unknowns and statistical anomalies which made this extremely difficult. This book really enabled me to get to grips with how best to tackle these sorts of questions in a clear and concise way and ensure you are keeping an eye out for outliers and noise. It has since spurred me on to order another couple of more in-depth books on statistics and this has since proved to be an excellent foundation book on which to build expert knowledge.

7 people found this helpful

John

challenging but incredibly useful. If you are looking for proper learning on ...

Reviewed in the United Kingdom on August 2, 2015

Verified Purchase

What a book! challenging but incredibly useful. If you are looking for proper learning on clustering and advance analytics this is a great book for the purpose. If you are looking for something quick read and move to the next book...don't buy it.
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.

One person found this helpful

See all reviews

Data Smart: Using Data Science to Transform Information into Insight 1st Edition

Frequently bought together

Customers who viewed this item also viewed