Customer Reviews

Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions

5 star:		(6)
4 star:		(3)
3 star:		(0)
2 star:		(1)
1 star:		(0)

 

 

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.

Like the rest of the excellent Head First Series this volume provides excellent planned pedagogy (teaching) in the field it addresses.
But, this excellence has been put to a harder test in this volume that asks: "How do I find, analyze and present the data which will answer the questions being asked by business leaders, scientists and policy makers?". It begins with an excellent introductory section (that easily could be turned into a book of its own) about how business and policy questions and undefined verbal problems can be analyzed and directed toward the data which will provide an accurate answer to the underlying question. It proceeds to design of data-based experiments, hypothesis testing and the appropriate statistical techniques which will provide accurate and easily understandable analysis. While there is a certain amount of data-based experimental design which is presented in introduction, the statistical methods are presented together with the tools that will allow anyone with a basic quantitative intuition to analyze complex data with Excel spreadsheets and the "R" statistical language. Data visualizations of quantitative information, data cleaning and representation of relational databases are presented in a light that will lead to accurate analysis and clear conclusions, as opposed to beautiful information visualizations for their own sake.

This volume will be a valuable educational tool and reference for future generation of business and policy analysts, as well as anyone who must find and communicate the answers to difficult questions based upon numerical and data-based analysis. This book illustrates that the Head First editorial format can be excellently applied to real life decision making and analysis issues as well as it has previously been employed in teaching high school and university subject matter.

While some of the Head First series have been quite helpful, this one has way too much fluff, making it tedious to find the important content. By way of example, the entire page 97 is devoted to "Profits fell through the floor" with a picture of a sad person, a picture of a pile of rubber ducks, a sample letter expressing a complaint, and the conclusion that "this is pretty bad news." Page 97 lies in the middle of the "optimization" chapter, but you don't get to the punch line on what to do about the "pretty bad news" until page 108. The pace of the book is simply too slow--which I attribute to an overuse of the Head First style, a style supposedly "designed for the way your brain works." My brain would have been happier if the editors had picked up the pace.

Skip this book if you really want to know how data analysis is done. Read it if you only want to get a quick look at what data analysis is about. This book covers no details. For example, in chapter 3, optimization, the author did a good job in introducing readers the idea (not the concept) of optimization and how to do it using Excel. But what is the algorithm behind the Excel solver? Or, how Excel solve the problem? Nothing is provided in the book.

This book provides an excellent, approachable introduction to data analysis. Although most experienced professionals or advanced students will find this text trivial, it serves as a god starting point for those who are completely new to data analysis. The text provides numerous interactive examples using Excel and R, but the examples do not cover these tools in any great depth. If you're looking to learn more about statistics, data analysis and data mining, this book is a good starting point.

Rather then another praise for this great Head First book, some really good topics in this book:

Chapter 2: TEST YOUR THEORIES
How to use the method of comparison and make them explicit.

Chapter 5: HYPOTHESIS TESTING
Falsification vs. satisficing. Falsification as the heart of hypothesis testing.

Chapter 6: BAYESIAN STATISTICS
Ah, the problem with the first base: I've never seen a better explanation of the Bayes' rule.

Chapter 7: SUBJECTIVE PROBABILITIES
The subject of incecting some rigor in our hunches. A very difficult topic well explained.

I wish we had during the MBA spent less time on statistics and focused more on real life business issues. Time well spent on data analysis, big numbers, good decisions etc.

This book does a thorough job covering the concept of data analysis, touching on both the soft side (requirements gathering, mental models) and the technical side (Excel, R). Like other "Head First" titles, it does it in an entertaining manner that makes reading the book a joy. The material is presented more like an enlightening conversation with an intelligent teacher than a brain dump of facts and theories.

It's an introductory book, well written, fun to read. I think lots of graduate programs could benefit from some of the examples of the book. For me, I wished it were a bit more advanced, but hey, it's purpose is to be an introductory material, and I think it does it well.

Michael Milton's HEAD FIRST DATA ANALYSIS: A BRAIN-FRIENDLY GUIDE offers a fine learner's guide to statistics and data analysis tying such analysis and discovery of customer bases to models for optimizing business and increasing sales. Learn how to organize data using Excel or Open Office and how to understand histograms, scatterplots and more with this outstanding survey using the latest cognitive research to present a new kind of learning experience key for computer and business collections alike.