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Data Cleaning in Python Essential Training

via LinkedIn Learning

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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?
Using Codespaces

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
Using Copilot to build schema
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 417 ratings

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