Data Science: Solving Missing Data Problems in R

About

Missing data often disrupt real-world machine learning and AI applications. Through a mix of lectures, hands-on labs, and practical case studies, you will learn to implement flexible, scalable, and statistically sound solutions using R, with emphasis on real-world applicability.
Led by experts in the field, including developers of the widely used mice package, the course explores contemporary approaches for generating imputations, synthesizing data, integrating imputation into AI workflows, and diagnosing the impact of missing data in statistical models and predictive modeling pipelines.

Most researchers need to deal with incomplete data. Missing data complicate the statistical analysis of data. Simply removing the missing data is not a good strategy and can bias the results. Multiple imputation is a general and statistically valid technique to analyze incomplete data. Multiple imputation has rapidly becoming the standard in social and behavioural science research.

This course will explain modern and flexible imputation and data synthesis techniques that are able to preserve salient data features. The course enhances participants’ knowledge of imputation principles and to provides flexible hands-on solutions to incomplete data problems. The course discusses principles of missing data theory, outlines a step-by-step approach toward creating high quality imputations, and provides guidelines on how to report the results. The course will use the authors’ MICE package in R to illustrate practical solutions to real data problems. The concepts and applications of the illustrated methodology would be equally applicable to other programming languages.

The course materials will follow the book “Flexible Imputation of Missing Data” by Stef van Buuren ( 2nd edition, Chapman & Hall, 2018) as well as a collection of papers and vignettes by the course team. The book can be read online for free

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