Data Cleaning in Python Essential Training

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

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