Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining by Glenn J. Myatt

Product Description

A practical, step-by-step approach to making sense out of data

Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data.

Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including:
* Problem definitions
* Data preparation
* Data visualization
* Data mining
* Statistics
* Grouping methods
* Predictive modeling
* Deployment issues and applications

Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project.

From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.
Product Details
Amazon Sales Rank: #118634 in Books
Published on: 2006-11-28
Number of items: 1
Binding: Paperback
292 pages
Editorial Reviews

Review
"…a well-written book on data analysis and data mining that provides an excellent foundation…" (CHOICE, May 2007)

"This is a must-read book for learning practical statistics and data analysis" (Computing Reviews.com, May 22, 2007)


"…the book should be accessible to all its intended readers." (MAA Reviews, December 28, 2006)

From the Back Cover
A practical, step-by-step approach to making sense out of data

Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data.

Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including:
Problem definitions
Data preparation
Data visualization
Data mining
Statistics
Grouping methods
Predictive modeling
Deployment issues and applications

Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project.

From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.

About the Author
GLENN J. MYATT, PhD, is cofounder of Leadscope, Inc., a data mining company providing solutions to the pharmaceutical and chemical industry. He has also acted as a part-time lecturer in chemoinformatics at The Ohio State University and has held a series of industrial and academic research positions. Dr. Myatt is the author of numerous journal articles.
Customer Reviews

Good Overview of Data Mining and Analysis
This book is a relatively good survey of the main issues and concepts involved in data mining and analysis. It covers a wide breadth of material and does so in a fluid way, but I do have one gripe with it. The problem I have with this book is the level of detail on some subjects. I would say that this book is written for someone of an advanced undergraduate level to understand the concepts involved in data mining, even though someone with that degree would not have a serious position doing data mining. This perplexes me somewhat, because I somehow expected slightly more rigor in the way topics were covered in a data mining book. Not to the level of a math stat book, clearly, but still.

In summary, there's a lot here about how data mining is done, but not a lot about how to mine data. Perhaps I expected the wrong things, or perhaps I expected too much, but that's my initial take on the book. If you're looking for depth and detail you won't find it here, but if you want a thorough introduction to data mining, and I do mean only an introduction, this text definitely will serve you well.

Again, don't misunderstand, this text definitely has it's value. It gives you a lot of information about the basics of data mining under a variety of circumstances (variable types, for example ordinal vs. nominal vs. ratio and so forth) and how to approach the analysis in each case. It mainly centers on the univariate case and seems to only touch briefly on multivariate models (which, in practice, are usually the norm).

As stated, for an introduction to the process, concepts and main issue of data mining and predictive modeling, this text is absolutely fine. I just think it needs to be made more clear what the target audience is for this text. I'm not saying I was disappointed, because there is much to learn in this book even for the practicing statistician, but I would argue that I expected more, based on the description.

Even so, 4 stars for the good quality of the writing, the breadth of coverage and the layout of the book. This should be required reading for those entering (or thinking about entering) the field of quantitative analysis and modeling.