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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 Comprehensive Data Cleaning Strategy

Common Data Cleaning Tools

  • Drake
  • OpenRefine
  • Pandas (for Python)
  • Dplyr (for R)

Ensuring High Data Integrity

  • Completeness
  • Correctness
  • Accuracy
  • Relevance
  • Consistency

Automating the Data Cleaning Process

Monitoring Your Data Cleaning System

Summary and Conclusion

Requirements

  • Familiarity with data analytics concepts.

Target Audience

  • Data Scientists
  • Data Analysts
  • Business Analysts
 7 Hours

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