Data Science Workshops

The Kansas Data Science Consortium (KDSC) was tasked by the National Science Foundation to create data-science based workforce development opportunities. The KDSC is proud to offer a series of workshops to students and professionals that are career relevant and cutting edge.

Upcoming Data Bias and Fairness Speaker Sessions

Hosted via Zoom

This Spring, the Kansas Data Science Consortium will host three speakers via zoom to discuss relevant topics in data bias and fairness.

Zoom Link: https://kansas.zoom.us/j/8988235568

See session outline below:

Upcoming 2025 sessions
Date	Time	Topic	Speaker
March 11	12:30PM, CST	Safety, Ethics, and Biases in AI and NLP systems	Xuhui Zhou

"Criminality From Face" Illusion

Dr. Kevin Bowyer's presentation on the "Criminality from Face Illusion." This lecture analyzes how published results on this "criminality from face" problem can give the illusion that it can be done. This discusses that any positive results on criminality from face can only be an illusion, and that belief in this illusion is dangerous.

Bias in AI Algorithms for Mental Health

Dr. Chaspari's presentation on "Deconstructing and Mitigating Socio-Demographic Bias in AI Algorithms for Mental Health" for the Data Bias and Fairness Speaker Series. In this lecture, Dr. Chaspari spoke about AI algorithms and their relation to mental health outcomes. She relayed useful lessons regarding data science principles in research and machine learning.

For information on our R and Stata workshops, please visit our Coffee and Conversations page.

For information on our Federal Data Workshops, please visit our Community Data Labs page.

Contact

Questions about workshop content can be directed to williamduncan@ku.edu

Previous Workshops

Topics included "double robust" machine learning methods and causal inference models. Taught online and in-person, through hands on coding examples. Ideal for upper level graduate students, researchers, and professionals looking to harness a new technique.

Topics included a gentle introduction to programming in R and a refresher on probability theory, research design, and measurement. Ideal for incoming graduate students preparing for their first statistical methods course.

Designed to refresh mathematical skills and introduce key concepts. Recommended for incoming PhD and MA students, open to returning students who want additional review.