Hunter Glanz is an Associate Professor of Statistics and Data Science at California Polytechnic State University (Cal Poly, San Luis Obispo). He received a BS in Mathematics and a BS in Statistics from Cal Poly, San Luis Obispo followed by an MA and PhD in Statistics from Boston University. He maintains a passion for machine learning and statistical computing, and enjoys advancing education efforts in these areas. In particular, Cal Poly’s courses in R, SAS, and Python give him the opportunity to connect students with exciting data science topics amidst a firm grounding in communication of statistical ideas. Hunter serves on numerous committees and organizations dedicated to delivering cutting edge statistical and data science content to students and professionals alike. In particular, the ASA’s DataFest event at UCLA has been an extremely rewarding experience for the teams of Cal Poly students Hunter has had the pleasure of advising.
SAS + R Part 1: Connecting SAS and R in Your Data Science Workflow
Monday, April 26, 2021 – 10:00 AM – 2:00 PM PDT
As robust statistical software packages, SAS and R boast a great number of tools for addressing all of your data-related needs. While there exists large overlap in what they provide, today’s statistical and data science problems increasingly involve multiple software packages. After all, if you have access to all of these tools then why not explore how they can improve your workflow! In this class we will explore the complete workflow of cleaning a dataset, exploring it, visualizing it using a combination of SAS and R.
SAS + R Part 2: Using R Shiny to Make Your Data Wrangling and Visualization Interactive
Wednesday, April 28, 2021 – 10:00 AM – 2:00 PM PDT
While both SAS and R include a rich suite of tools for working with your data, there often exists a collection of tasks and activities that get repeated with every new dataset. Traditionally such repetition could be addressed by building macros or functions. R Shiny enhances this process by making your data work interactive! Not only can this save you some code and work, but it provides a way for consumers of your work to do all of your cool data science-y things without needing to know how to program. In this class we will build our very own basic shiny applications using R.