Cody's Data Cleaning Techniques Using SASĀ®, Third Edition Reviews

I had the great fortune of being asked to prepare a promotional review for the Third Edition of Cody's Data Cleaning Techniques Using SAS book. And boy I am glad. Like other books written by Ron Cody, this book is easy to read, flows well, and is packed with example after example containing valuable techniques that every SAS user should know when identifying and cleaning data.

This new edition doesn't simply rehash content from the first and second editions with minor changes and enhancements either. It boasts several new chapters that I know will be immensely popular with SAS users everywhere, such as the use of Perl regular expressions to detect data errors and check the format of character values (such as ZIP codes or email addresses), and using formats and functions to standardize company names and addresses. If you have anything to do with using SAS to identify and fix problems and errors in your data, then go right out and purchase Cody's Data Cleaning Techniques Using SAS, Third Edition. You and your data will be glad you did.

Kirk Paul Lafler
SAS Programmer, Applications Developer, Educator and Author
Software Intelligence Corporation

Your Results are only as good as the Quality of your Data.

Cody's Data Cleaning Techniques Using SAS, Third Edition focuses on not only knowing your data but also on addressing real data issues that impact results and conclusions. Ron's statistical programming techniques help identify the root cause of data issues and outliers.

The scope of Cody's book is comprehensive, from reading and processing character, numeric, and date values to addressing duplicate records and standardizing data. There is also a section for preventing errors with integrity constraints. SAS programmers can apply Cody's examples to easily identify and resolve data errors. SAS programmers can learn and take advantage of special functions, such as NOTDIGIT, NOTALPHA, NOTALNUM and VERIFY, as well as PROC FORMAT tips for identifying invalid values. I highly recommend Cody's book to better control data quality.

Sunil Gupta
Associate Director Statistical Programming

This book is full of well-explained tips and tricks for dealing with dirty data, and it is very well laid out. I found the section on using Perl regular expressions in SAS to find your problematic data especially valuable."

Derek Morgan

Data cleaning is necessary when preparing your data sets for analysis and reporting. Cody's Data Cleaning Techniques Using SAS, Third Edition makes this critical first step much, much easier. Ron's strong skills and experience as an educator and writer shine through with clear explanations and many easy-to-adapt programs that will help you achieve your data cleaning requirements.

If you have the first or second edition of this book, it's time to retire them. Replace your older editions with the third edition because Ron has thoroughly updated his book and added four new chapters.

Ron's book is suitable for SAS programmers at any level of programming experience. Beginning programmers will learn new techniques while more advanced programmers can refresh their programming toolkit. New chapters in this edition cover Perl regular expressions, standardizing data, and using regression techniques to find errors in numeric data.

The book's organization by type of data error and by general technique makes it easy to understand data cleaning concepts. Especially useful is the list of the names of all programs found in the book. With a quick scan of this list, you will find techniques that suit your data cleaning requirements.

Even though I've worked as a SAS programming consultant for more than 30 years, I will refer to this book over and over because its presentation organizes my thoughts on how to approach preparing new data sets for further analysis and reporting."

Michele Burlew
SAS Consultant and Author
Episystems, Inc.

Ron's new book, Cody's Data Cleaning Techniques Using SAS is great! In this third edition, he has added a good number of new chapters and even more detail to the existing ones. There are code segments throughout the book that explain in simple terms each line of code.

Ron gives a practical reason for using each technique and an example of how to accomplish the cleaning task. Two of the new chapters, the one on using Perl Regular Expressions to find errors, and the other one showing how to find errors in highly skewed data distributions using SAS regression processing make these complex tasks seem like I should have known how to do them a long time ago!

I believe this book accurately and completely covers how to clean your data. I would recommend that everyone who works with data in any fashion buy this book!"

William E. Benjamin Jr.