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
Matt is currently Managing Partner and Principal Data Scientist at BetaVector. His full-time professional data work spans finance, education, consumer-packaged goods, and politics and he earned General Assembly's 2019 "Distinguished Faculty Member of the Year" award. Matt earned his Master's degree in statistics from Ohio State. Matt is passionate about responsibly putting the power of machine learning into the hands of as many people as possible and mentoring folx in data and tech careers. Matt also volunteers with Statistics Without Borders and currently serves on their Executive Committee as the Marketing & Communications Director.
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Course Abstract
If you've never heard of the good, fast, cheap, dilemma, it goes something like this:
You can have something good and fast, but it won't be cheap. You can have something
good and cheap, but it won't be fast. You can have something fast and cheap, but it
won't be good. In short, you can pick two of the three but you can't have all three. If
you've tackled a data science problem before, I can all but guarantee that you've run into missing data. How do we handle it? Well, we can avoid, ignore, or try to account for missing data. The problem is, none of these strategies are good, fast, *and* cheap.
We'll start by visualizing missing data and identify the three different types of missing data, which will allow us to see how they affect whether we should avoid, ignore, or account for the missing data. We will walk through the advantages and disadvantages of each approach as well as how to visualize and implement approaches. We'll wrap up with practical tips for working with missing data and recommendations for integrating it with your workflow!
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
Who should attend?
Any data professional who has encountered missing data, or anyone who is interested in learning more about missing data. There is no required background knowledge, though an understanding of linear regression, standard deviation, and Python programming can be helpful.