This was a beautiful book that really refueled my interest for Statistics (which I've been struggling to start learning...even though I know calculus and LOVE mathematics)...but it really caught my eye because it goes into detail about the R statistical programming language.
The first few chapters get you going on a specific mindset of how to interpret data, which is VERY important to keep throughout the entire reading of this book.
After that groundwork is established, you are taken on a really cool journey of some Excel features (don't freak out...those of you who don't know excel proficiently will be fine in the hands of this book) that you never would've believed were there! You can even use Google Docs to do the same things if you don't have a valid copy of Excel!
Finally, R comes into play with all its glory...I would've loved for a deeper dive with this technology, but there are several other books out there in which you can get down and dirty with R (http://www.amazon.com/The-Art-Programming-Statistical-Software/dp/1593273843/ and http://www.amazon.com/Cookbook-OReilly-Cookbooks-Paul-Teetor/dp/0596809158/ are my favorites and I own them both on my kindle).
I hope that eliminates all your FUD's (Fears, Uncertainties, and Doubts)...go and grab this book RIGHT NOW! You'll be blown away with what you'll be able to do after you read everything here!
P.S. It only takes about a week and a half to get through it going at a nice, slow, and comfortable pace...if you're HUNGRY like I was, you can knock it out in about 4 days.
I got the book promptly. It has that softbound textbook feel. but good binding not cheap or ready to fall apart. the intormation in it so far seems interesting and well organized. its in the "head first format" which means there is a lot of nice visual lay out and side notes and some graphics to make understanding the concepts by seeing them when possible. I like that format. It is still pretty clean and gets to the point. but I have only read and used so much of it at this point so I cannot go much farther into the content than that. -- in short I think it is a solid book to get if one wants to better understand how to interpret social science numbers, or other scientific numbers that they are shown in a way that they are wise to various ways that data can be spiked and spiced. how in depth I cannot comment on as I have not fully digested the book. But it is a book that is designed to be both read and used as a topical reference. And it has the "Head First" style keeping things clean but providing insightful commentary, context and graphical illustrations where it might really speed up or enhance understanding of a particular idea or complicated example. it also uses bolding in areas where you can pay attention to the new vocabulary you might want to learn in order to lay the ground work for even more technical education in data analysis. it even has a chapter where it goes over some of the more obscure plug ins for excel that are there for helping a person analyze data. I would basically treat this book as a nice survey of both the human technical sides of data analysis. it also covers things like data collection or effective data presentation, and as I said it refers to several readily available tools like excel for example and how they can be used by someone who wanted to know how to leverage their computer in order tame and extract meaning from data they have been given to interpret. -- I think that its a useful primer that is like a survey course in the subject sans the professor. But how good each section is I cannot comment on as I have only started with the book for a several weeks. but what I did read I found completely intelligible and because I am not a total novice at looking at Data, there were times I could use its nice formatting to skip past explanations I did not need because I already was familiar with them. If I fall in love with the book I may come back and say so and make my stars 5 instead of a 4 but at this point I would highly recommend this book for anyone who wanted a nice primer that went into to a very serviceable level of detail for a primer or survey type information source.
This book is for professionals that must analyze data in their daily work. First off, if you are unfamiliar with the approach of the "Head First" series of books by O'Reilly, the approach was and is revolutionary in the field of technical writing. The authors of this series know that page after page of terse text will not easily penetrate the brain of the working professional who needs help rather quickly. Traditional textbook models work best on students in a traditional classroom setting who can slowly absorb material over a period of several months with the help of bi-weekly classroom sessions with a professor. The working professional does not have this luxury of time or of personal tutoring.
Thus the authors both penetrate your brain and hold your interest by serving information up in unusual ways - odd pictures and illustrations, Q&A sessions, repeating the same material in different ways, and interesting case studies in which you are asked at every step to give your input. They'll even lead you down the the wrong path every now and then so that you remember the right one all the better.
As for the subject matter, this is not a book on statistics and how to solve problems in statistics. Instead, it is how you use various statistical models and tools and visualization to analyze often confusing corporate data and come up with recommendations based on that data. Some mathematical methods will be presented as they are necessary to solving the underlying problems - optimization, hypothesis testing, bayesian statistics, subjective probabilities, heuristics, and histograms - these are all mentioned and even have their own chapters. However, this book is also about tools - R and the analysis tools of Excel specifically. In the appendix, this book even shows you how to install R.
However, I don't believe that you could get away with knowing nothing of statistics and really get the most out of this book. If you do happen to have the luxury of a little time I suggest the following. Read the excellent Head First Statistics as a tutorial, and then use the problems in Schaum's Outline of Statistics (Schaum's Outline Series) to test your knowledge. Then you should be more than ready for this book.
The author has a chapter entitled "leftovers" that tells you what this book does not cover. I include that here so that you don't waste your time if this is what you are looking for:
1 Everything else in statistics
2 Excel skills - (book assumes previous experience)
3 Edward Tufte and his principles of visualization
4 PivotTables
5 Nonlinear and multiple regression
7 Null-alternative hypothesis testing
8 Randomness
9 Google Docs
I highly recommend this book for the right audience with the right experience level.
First, a disclaimer: as one of the technical reviewers for the book, I might be a little biased. Having said that, I'm willing to bet my copy of Head First Data Analysis that this won't be the last 5-star review you'll find here :-)
By my count this is the 20th book in the Head First series, so by now most Amazon customers know the story behind the Head First format, style, and pedagogy. These aren't your typical technical books, so if this is the first Head First book you're considering, you owe it to yourself to get a sneak preview first. I think you'll be in for a treat.
The Amazon Reader does have the first six pages of Chapter 1, which will give you some idea, but I'd recommend going to Head First Labs where you can download and read the entire 2nd chapter. You can also grab the full Table Of Contents in PDF format, which I believe is a little easier on the eyes than the TOC in the Amazon Reader.
The book is written for folks without hardcore data analysis experience who are looking for an introduction to analyzing data to make better decisions. You won't need a background in statistics, engineering, or computer science. While some data analysis books assume you're a math geek, Michael Milton does not.
And while many "Data Analysis" books pretty much revolve around Excel's data analysis functions (Analysis ToolPak, Solver, etc), this book is more about how you work with data, not about how you use a particular software tool. While you do use spreadsheets and a statistical computing software package called "R", the focus is on using the tools between your ears to become a better data analyst.
These days almost everyone needs to deal with and interpret data. Those that become successful know how to make sense of it all. This book will help you think about, process, and present your data so you can draw reliable conclusions to real-life questions.
Product Details :
Paperback: 486 pages
Publisher: O'Reilly Media; 1 Original edition (August 4, 2009)
Language: English
ISBN-10: 0596153937
ISBN-13: 978-0596153939
Product Dimensions: 8 x 1 x 9.2 inches
More Details about Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions 1st edition
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