Statistical Software Training Courses
About the Training
MPRC offers multiple-week, non-credit SAS, Stata, and R training courses. The classes are open to MPRC affiliated faculty and students, and course offerings will focus on SAS, Stata, and R topics relevant to population research.
Who Can Attend?
MPRC affiliated faculty, students, and staff are welcome to participate. Please contact Heide Jackson at heidej@umd.edu if you have any questions.
2020 Spring Offerings
MPRC will offer free non-credit short courses on Stata and R this semester. If you wish to attend, please use the pre-registration form.
Short Courses
Introduction to R
February 6, 2020 2:00-3:30pm
Location: Computer Lab Room 2 - 0227 Lefrak Hall
Data Visualization in R
February 13, 2020 2:00-3:30pm
Location: Computer Lab Room 2 - 0227 Lefrak Hall
Modeling in R
February 20, 2020 2:00-3:30pm
Location: Computer Lab Room 2 - 0227 Lefrak Hall
Longitudinal Data Analysis in Stata
February 11, 2020 2:00-3:30pm
Location: Computer Lab Room 2 - 0227 Lefrak Hall
Course Descriptions
Introduction to R
This course will introduce new users to R and R Studio. The course will introduce R and commonly used R packages, show how to load in data sets, perform basic data manipulation, run statistical select analysis, and highlight useful resources for getting R help.
Prerequisites: None.
Data Visualization in R
This course will show users how to visualize data in R. The course will show how to create useful graphs in R and will introduce helpful packages for visualizing data, particularly ggplot2.
Prerequisites: Some experience using R would be helpful.
Modeling in R
This course will introduce users to some packages for running advanced statistical models in R. The focus of this course will be on latent variable modeling with the lavaan package.
Prerequisites: Some experience using R would be helpful.
Longitudinal Data Analysis in Stata
This course will provide helpful tips for conducting longitudinal data analysis in stata. This course will discuss useful data cleaning strategies, data reshaping, and fixed effect and random effect models.
Prerequisites: Some experience using Stata would be helpful.