The Regression Cookbook
Finding relationships within populations or systems through data modelling and using these models to predict key variables is a cornerstone of the public and private sectors. In this context, regression analysis and supervised learning are two pivotal fields in data science, forming a critical synergy where statistics and machine learning intersect. The Regression Cookbook is an open educational resource project that bridges statistics and machine learning. It emphasizes mathematical and computational tools to model data, uncover meaningful relationships, and make key predictions about populations or systems. Uniquely, it presents regression techniques through dual programming perspectives, utilizing both R
and Python
.
This resource is being tailored to enhance the learning experience of graduate and undergraduate students interested in data science. Its “cookbook” format prioritizes practical, example-driven learning over purely theoretical approaches (while still providing theoretical sparks for students looking for this material), making it highly engaging. The Regression Cookbook seeks to reduce student costs while providing high-quality, freely accessible learning materials. The grant will support this resource’s development, integration, and ongoing maintenance, ensuring its sustainability as a valuable tool for students and educators in data science and statistics courses.
This educational resource is still an undergoing project with co-authors Andy Tai, Postdoctoral Teaching and Learning Fellow, and Ben Chen, Course Coordinator, in the Master of Data Science at UBC.