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
 

Number of participants


Starts

Ends


Dates are subject to availability and take place between 9:30 am and 4:30 pm.
Open Training Courses require 5+ participants.

Testimonials (2)

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