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Course Outline
Introduction
Overview of Data Cleaning
- Why is Data Cleaning Important?
Case Study: When Big Data Is Dirty
Developing A Thorough Data Cleaning Strategy
Common Data Cleaning Tools
- Drake
- OpenRefine
- Pandas (for Python)
- Dplyr (for R)
Achieving High Data Integrity
- Complete
- Correct
- Accurate
- Relevant
- Consistent
Automating the Data Cleaning Process
Monitoring Your Data Cleaning System
Summary and Conclusion
Requirements
- An understanding of data analytics concepts.
Audience
- Data Scientists
- Data Analysts
- Business Analysts
7 Hours
