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Course Outline

Introduction

Overview of Data Sanitization

  • The Importance of Data Sanitization

Case Study: Challenges Posed by Inaccurate Large-Scale Data

Formulating a Comprehensive Data Sanitization Strategy

Common Data Sanitization Applications

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

Ensuring High Data Integrity

  • Completeness
  • Correctness
  • Accuracy
  • Relevance
  • Consistency

Automating the Data Sanitization Workflow for government use cases

Monitoring Data Sanitization Systems

Summary and Conclusion

Requirements

  • Competency in fundamental principles of data analytics.

Audience

  • Data Scientists
  • Data Analysts
  • Business Analysts
 7 Hours

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