Rocket Surgery Made Easy: The Do-It-Yourself Guide to Finding and Fixing Usability Problems

By Nathan Rountree

It's been known for years that usability testing can dramatically improve products. But with a typical price tag of $5,000 to $10,000 for a usability consultant to conduct each round of tests, it rarely happens.

In this how-to companion to Don't Make Me Think: A Common Sense Approach to Web Usability, Steve Krug spells out a streamlined approach to usability testing that anyone can easily apply to their own Web site, application, or other product. (As he said in Don't Make Me Think, "It's not rocket surgery".)

Using practical advice, plenty of illustrations, and his trademark humor, Steve explains how to:
Test any design, from a sketch on a napkin to a fully-functioning Web site or application Keep your focus on finding the most important problems (because no one has the time or resources to fix them all) Fix the problems that you find, using his "The least you can do" approach By paring the process of testing and fixing products down to its essentials ("A morning a month, that's all we ask"), Rocket Surgery makes it realistic for teams to test early and often, catching problems while it's still easy to fix them. Rocket Surgery Made Easy adds demonstration videos to the proven mix of clear writing, before-and-after examples, witty illustrations, and practical advice that made Don't Make Me Think so popular.

• Format: Paperback
• Language: English
• ISBN: 0321657292
• ISBN13: 9780321657299
• Release Date: December 2009
• Publisher: New Riders Publishing
• Length: 168 Pages
• Weight: 0.79 lbs.
• Dimensions: 0.4" x 6.9" x 8.9"

Customer Reviews

Thorough, well-written, and crystal-clear explanations.

Highly recommend this book for a practical introduction to the theory and applications of Machine Learning. Great book if you are looking to ACTUALLY implement some machine learning systems, prefer to learn via diagrams, a "how-stuff-works"-style explanation, and skip much of the equations and heavy math that fills similar books. Obviously, this book is a perfect companion to the Weka machine toolbox, which is quickly...

Incredibly practical introduction

This book is perfect if you are trying to get your hands around what data mining and machine learning is. Most of the books I have read on this subject want to start with equations and get more complex from there, with little practicality. This book makes extensive use of examples and introduces the mathematical basis for algorithms where needed. The authors make the point that simpler algoritms often work best for solving...

Lucid

I'm surprisingly please with this book. I've been reading up on the topic and associated algorithms in other books for some time; I'm a software developer but don't have a statistics background, and so felt a lot of the texts were too focused on the math and the theory while being thin on content when it came to "rubber hitting the road", or even using clear, simple examples and straight-forward notation. This book is so...

Great Book in Every Way

The first edition of this book was good, but this is a huge improvement. The writing is really great, very clear, even when it heads into deeper waters. The explanation, for instance, of the various algorithms for accomplishing attribute discretization is very clear, even as the equations start to get very long and complicated. It's pretty incredible that this book is so readable, kudos to the authors for that. Most importantly,...

Good Book for Data Mining

This is the second edition of the author's Data Mining book. The first part of the book focuses on data mining algorithms, implementation issues, and how to evaluate the results of the data mining model. The second part focuses on the authors "Weka Machine Learning Workbench" which is available under a GNU General Public License. See their web site: http://www.cs.waikato.ac.nz/~ml/weka/index.html for the software. This...