Data Smart: Using Data Science to Transform Information into Insight
| Author | : | |
| Rating | : | 4.84 (564 Votes) |
| Asin | : | 111866146X |
| Format Type | : | paperback |
| Number of Pages | : | 432 Pages |
| Publish Date | : | 2015-09-02 |
| Language | : | English |
DESCRIPTION:
Foreman is Chief Data Scientist for MailChimp, 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. . John W
Lou Gutnicki said Great Book, Lousy References. 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 . Insightful, practical, and colorful. Perspective from a biased reviewer. Evan Miller 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-le. Reminds you that technical books can be insightful and fun to read Jim Vallandingham 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
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. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.. 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
It's not about coding or database technologies. Really. But if you can't have John, then reading this book is the next best thing."—Patrick Lennon, Director of Analytics, The Coca-Cola CompanyMost people are approaching data science all wrong. 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 spreadsheetVisit the companion website at wiley/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 BayesClustering via k-means, spherical k-means, and graph modularityMathematical optimization, including non-linear programming and genetic algorithmsWorking with time seri