Why Proper Handling of Missing Data is Essential

Missing data is a common challenge in data analysis, and ignoring it can lead to biased results and unreliable conclusions. Properly addressing missing data is essential to ensure the accuracy and integrity of your analyses.

R provides powerful tools and techniques for handling missing data, ranging from simple methods to advanced imputation approaches. Mastering these techniques allows you to manage missing data effectively, preserving valuable insights and minimizing potential errors.

This workshop will teach you how to effectively assess and manage missing data using R, enabling you to make reliable, data-driven decisions in your projects.

 

 

What You Get

Master the art of missing data imputation in R with our interactive 8-week workshop! Each weekly session lasts 1.5 hours, combining theoretical insights, R programming guidance, and hands-on exercises, where we’ll collaborate on code and tackle real-world scenarios.

Designed as an exclusive learning experience, this workshop is limited to a maximum of 15 participants. The small group size ensures personalized attention, meaningful interactions, and a supportive environment where you can engage directly with the instructor and your peers. In addition, a comments feature will be available, allowing you to ask questions between sessions, share your progress, and get support if you encounter any challenges along the way.

The workshop is carefully structured to ensure steady progress, beginning with the fundamentals of missing data handling and advancing to more sophisticated imputation techniques. Each session promotes engagement and direct interaction, helping you build both practical expertise and confidence in managing various missing data challenges.

Recordings, R code, and all other materials from the live sessions will be made available to participants. This added flexibility allows you to revisit the material at your convenience or catch up on any missed sessions. After the workshop, you will retain lifetime access to all resources, ensuring you can refresh your knowledge whenever needed.

By the end of the workshop, you will have acquired comprehensive knowledge of missing data imputation in R, along with the practical skills to apply these techniques effectively in your projects. You will also receive a certificate verifying your participation, demonstrating your commitment to mastering this essential skill.

Here are more details on the workshop structure!

 

A Peek Inside the Workshop

Join our interactive 8-week workshop on missing data imputation in R, featuring 1.5-hour live sessions designed to help you build practical skills through hands-on learning and collaborative exercises with fellow participants.

We’ll guide you through the core concepts of handling missing data and demonstrate advanced imputation techniques in R. Using real-world examples, you’ll engage in collaborative problem-solving and gain the confidence to tackle even the most complex missing data challenges.

This workshop enhances your expertise in missing data imputation while also strengthening your general knowledge of R programming, statistical modeling, and data preparation — key skills for advancing in data analysis. Whether you’re new to R or refining advanced techniques, this workshop is designed for you.

Throughout the workshop, we’ll delve into several key R packages for handling missing data, with a primary focus on the widely trusted mice package. Mastering its state-of-the-art imputation methods will enable you to apply expert-level techniques and confidently communicate your results to colleagues and stakeholders.

Here’s the table of contents for the entire workshop! Each topic includes live lessons, exercises ranging from simple to advanced, and additional learning materials to support your progress.

 

 

 

Meet Your Instructor: Joachim Schork

 

However, I didn’t want to create endless tutorials that didn’t fulfill the need of its users. Instead, I created straightforward content designed to guide users to solutions for their problems as quickly as possible.

Now, six years later, Statistics Globe has gained:

 

 

 

 

This is such an incredible success, and I’m so thankful to everybody who participated in this journey! And please don’t get me wrong: I don’t want to brag about these numbers, but I think they can show you that my content works.

 

My Background in Missing Data Imputation with R

Missing data imputation in R is the topic I’ve worked on the most throughout my entire career! Already during my master’s program in survey statistics, I attended specialized lectures on missing data imputation and went on to write my master’s thesis on Handling Implausible Values due to Missing Data Imputation.

Later, I worked at a National Statistical Institute, where I contributed to the imputation of missing data for various social surveys, including the Labor Force Survey, EU-SILC, and the Tourism Survey. During this time, I also authored an extensive working paper titled Automatic Variable Selection for Imputation Models. More recently, I worked on a large consulting project where we developed an R package to provide a standardized missing data imputation process for various types of data sets of a client, including automated reporting to evaluate the quality of the imputations.

Drawing from my experience across various subtopics of missing data imputation and working with missing data in diverse types of data sets, I am confident in my expertise on this topic and excited to share my knowledge with you in this workshop. My goal is to guide you through the complexities of missing data imputation and help you apply these techniques effectively in your own projects.

 

Clicking this button will direct you to the checkout page, where you can enroll in the online workshop. I’d be honored to have you in the workshop and start learning together. 🙂