What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems.
From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data isdata that gets in the way. This book explains effective ways to get around it.
Among the many topics covered, you’ll discover how to:
- Test drive your data to see if it’s ready for analysis
- Work spreadsheet data into a usable form
- Handle encoding problems that lurk in text data
- Develop a successful web-scraping effort
- Use NLP tools to reveal the real sentiment of online reviews
- Address cloud computing issues that can impact your analysis effort
- Avoid policies that create data analysis roadblocks
- Take a systematic approach to data quality analysis
Mapping the World of Data ProblemsAbout the Authors;Preface;Conventions Used in This Book;Using Code Examples;Safari? Books Online;How to Contact Us;Acknowledgments;Chapter 1: Setting the Pace: What Is Bad Data?;Chapter 2: Is It Just Me, or Does This Data Smell Funny?;2.1 Understand the Data Structure;2.2 Field Validation;2.3 Value Validation;2.4 Physical Interpretation of Simple Statistics;2.5 Visualization;2.6 Keyword PPC Example;2.7 Search Referral Example;2.8 Recommendation Analysis;2.9 Time Series Data;2.10 Conclusion;Chapter 3: Data Intended for Human Consumption, Not Machine Consumption;3.1 The Data;3.2 The Problem: Data Formatted for Human Consumption;3.3 The Solution: Writing Code;3.4 Postscript;3.5 Other Formats;3.6 Summary;Chapter 4: Bad Data Lurking in Plain Text;4.1 Which Plain Text Encoding?;4.2 Guessing Text Encoding;4.3 Normalizing Text;4.4 Problem: Applicatiol£¾
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