Cody's Data Cleaning Techniques Using SAS, Third Edition - Softcover

"The scope of Cody's book is comprehensive, from reading and processing character, numeric, and date values to addressing duplicate records and standardizing data. I highly recommend Cody's book to better control data quality." -- Sunil Gupta, Associate Director Statistical Programming "Cytel"

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

"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."

Find errors and clean up data easily using SAS !

Thoroughly updated, Cody's Data Cleaning Techniques Using SAS, Third Edition, addresses tasks that nearly every data analyst needs to do - that is, make sure that data errors are located and corrected. Written in Ron Cody's signature informal, tutorial style, this book develops and demonstrates data cleaning programs and macros that you can use as written or modify which will make your job of data cleaning easier, faster, and more efficient.

Building on both the author’s experience gained from teaching a data cleaning course for over 10 years, and advances in SAS, this third edition includes four new chapters, covering topics such as the use of Perl regular expressions for checking the format of character values (such as zip codes or email addresses) and how to standardize company names and addresses.

With this book, you will learn how to:

• find and correct errors in character and numeric values
• develop programming techniques related to dates and missing values
• deal with highly skewed data
• develop techniques for correcting your data errors
• use integrity constraints and audit trails to prevent errors from being added to a clean data set