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LinkedIn Learning

Data Cleaning in Python Essential Training

via LinkedIn Learning

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Overview

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Improve the overall analytic workflow of your organization by boosting your data cleaning skills in Python.

Syllabus

Introduction
  • Why is clean data important?
  • What you should know
  • Using GitHub Codespaces with this course
1. Bad Data
  • Types of errors
  • Missing values
  • Bad values
  • Duplicates
2. Causes of Errors
  • Human errors
  • Machine errors
  • Design errors
  • Challenge: UI design
  • Solution: UI design
3. Detecting Errors
  • Schemas
  • Validation
  • Finding missing data
  • Domain knowledge
  • Subgroups
  • Challenge: Find bad data
  • Solution: Find bad data
4. Preventing Errors
  • Serialization formats
  • Digital signature
  • Data pipelines and automation
  • Transactions
  • Data organization and tidy data
  • Process and data quality metrics
  • Challenge: ETL
  • Solution: ETL
5. Fixing Errors
  • Renaming fields
  • Fixing types
  • Joining and splitting data
  • Deleting bad data
  • Filling missing values
  • Reshaping data
  • Challenge: Workshop earnings
  • Solution: Workshop earnings
Conclusion
  • Next steps

Taught by

Miki Tebeka

Reviews

4.4 rating at LinkedIn Learning based on 260 ratings

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