9781629607962-1629607967-Cody's Data Cleaning Techniques Using SAS, Third Edition

Cody's Data Cleaning Techniques Using SAS, Third Edition

ISBN-13: 9781629607962
ISBN-10: 1629607967
Edition: 3
Author: Ron Cody EdD
Publication date: 2017
Publisher: SAS Institute
Format: Paperback 234 pages
Category: Programming
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Book details

ISBN-13: 9781629607962
ISBN-10: 1629607967
Edition: 3
Author: Ron Cody EdD
Publication date: 2017
Publisher: SAS Institute
Format: Paperback 234 pages
Category: Programming

Summary

Cody's Data Cleaning Techniques Using SAS, Third Edition (ISBN-13: 9781629607962 and ISBN-10: 1629607967), written by authors Ron Cody EdD, was published by SAS Institute in 2017. With an overall rating of 3.8 stars, it's a notable title among other Programming books. You can easily purchase or rent Cody's Data Cleaning Techniques Using SAS, Third Edition (Paperback) from BooksRun, along with many other new and used Programming books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $14.74.

Description

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
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