Highlight of the Week - How to do Data Science with Missing Data
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How to visualize missing data and identify the three different types of missing data
How missing data affect whether we should avoid, ignore, or account for the missing data
Advantages and disadvantages of each approach
How to visualize and implement approaches
Practical tips for working with missing data
Recommendations for integrating it with your workflow!
Module 1: An introduction to missing data
Module 2: Strategies for doing data science with missing data
- Avoid missing data
- Ignore missing data
- Account for missing data
- Unit missingness
- Item missingness
Module 3: Practical considerations and warnings
This course is for current and aspiring Data Scientists, Data Analysts, Machine Learing Engineers and AI Product Managers
Knowledge of following tools and concepts is useful:
Familiarity with Python and Jupyter notebooks
Some knowledge of Pandas library is useful, but not required