Live training with Matt Brems starts on September 21st at 12 PM (ET)
Training duration: 4 hrs (Hands-on)
Course Outline
1. Introduction to Missing Data
2. Strategies for doing Data Science with Missing Data
- Avoid Missing Data
- Ignore Missing Data
3. Account for Missing Data
- Unit missingness vs. item missingness.
- Weight class adjustments for unit missingness
- The three types of missing data: MCAR, MAR, NMAR
- Imputation techniques (deductive, single, multiple)
- Pattern submodel method
4. Putting it together in a workflow
5. Practical considerations and warnings
Instructor Bio:
Matt Brems
Global Lead Data Science Instructor | General Assembly
Matt Brems
10% discount ends in:
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Course Abstract
Learning Objectives
By the end of this course, you should be able to:
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Describe the impact of missing data using simulations
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Identify techniques for avoiding missing data and give specific examples of how to avoid missing data.
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Define unit and item missingness, and identify when they occur
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Implement weight class adjustments, and identify advantages and disadvantages of this technique
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Define and give examples of data that are missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR)
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Describe a workflow for doing data science with missing data
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Describe proper regression imputation and the pattern submodel method
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Select the best missing data technique given your situation and real-world constraints
What is included in your ticket?
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Access to live training and QA session with the Instructor
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Access to the on-demand recording
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Certificate of completion