Graduate Quantitative Methods Center

In the changing world of data science, R remains a cornerstone of data processing and analysis. R provides precise and varied capabilities, but also presents a great entry point to coding languages for the unfamiliar. This workshop will provide foundational knowledge for new and novice R users.

Looking for flexible alternatives to standard statistical methods? Join us for this introductory workshop on the basics of nonparametrics. We will explore traditional as well as modern techniques for analyzing data with a minimal set of assumptions. Examples in R will be provided.

Want to learn how to work with time series data? In this workshop, you will learn how to use R to visualize time series data as well as the fundamentals of using regression to create forecasts and model time series data.

Correlation does not imply causation, but what implies correlation? This workshop will explore the linear relationships between variables via correlation and regression. Come learn about correlation coefficients and how to examine linear relationships in normal and non-normal data.

Join us for an introduction to machine learning and how it can be utilized. We will be going through a workflow of how to develop and evaluate machine learning models and contrast a few different machine learning methods in R.

What do you do when your data isn’t normally distributed? This workshop will review the linear regression model and you'll learn how it can be extended to model other types of data like counts, binary outcomes, and categories. You’ll also learn how to present and interpret the results of these models.

Join us as we explore computational approaches to Bayesian inference. Participants will visit the theory and motivation behind Bayesian sampling techniques such as MCMC; applications in R will be provided.

Join us for an introduction to more advanced concepts in python and general software development. Learn about object oriented programming, unit testing, and making basic software.