The Biometrics Section will sponsor seven continuing education courses at the 2018 Joint Statistical Meetings in Vancouver. This month, we highlight the following two:

Introduction to Bayesian Nonparametric Methods for Causal Inference

Instructors: Jason Roy and Michael Daniels
Have you ever thought about trying more innovative approaches to causal inference, but you don’t know how to begin? Bayesian nonparametric methods (BNP) could be exactly what you are looking for! In this short course, these experts will review BNP methods and illustrate their use for causal inference in the setting of point treatments, dynamic (longitudinal) treatments, and mediation. The BNP approach to causal inference has several possible advantages over popular semiparametric methods, including efficiency gains, the ease of causal inference on any functionals of the distribution of potential outcomes, the use of prior information, and capturing uncertainty about causal assumption via informative prior distributions. You’ll learn even more from a wealth of examples, supported by detailed instructions on software implementation using R.

Data Science for Statisticians

Instructor: Rafael Irizarry
Do you possess the three key skills needed to succeed in today’s data science revolution? Come to this course to find out! This short course focuses on statistical thinking in today’s data science environment. Real-world data analysis examples will introduce data science concepts and skills not typically taught in traditional statistics courses. Exploratory data analysis, data visualization, machine learning, and tidyverse tools are just a few of the ‘essentials’ we will cover. And throughout the course, there will be an emphasis on three key skills needed for you to succeed: creating, connecting, and computing. This half-day course is an ideal quick guide for statisticians wanting to gain practical knowledge about data science.