Exploratory Data Analysis Missing Data
About
A summary of the main types of missing data, and different methods for handling them.
Learning Objectives
You will be able to:
- Summarize the main methods of dealing with missing data and their implications.
- Explain the difference between unit missingness and item missingness.
- Understand the main types of missing data.
- Explain the strategies for imputing missing data and their implications.
- Handle missing data using
pandas.
Content
| Topic | About |
|---|---|
| Full Lesson Deck | A walkthrough of the types of missing data and how to handle them |
| 02 Missing Data notebook | A demonstration of how to handle missing data using pandas |
Prerequisites
- Write and run Python code in a Jupyter notebook.
- Create basic Python functions.
- Python programming skills including using modules and libraries.
- Use
pandas.
References
📖 Reference Materials
Important note
Please ignore the _layouts folder and the config.yml file in the root of this repo. They are needed to make sure the repo renders well; please don’t change them or delete them and your repo will work fine :)