QuantCamp is designed to prepare first-year graduate students for the graduate-level quantitative methods they will be using in their research. The program is an introduction for those with no experience in programming and statistics, but can also be a refresher course for those who have previously been exposed to the content. Participants are introduced to foundational concepts routinely used and referenced by most quantitative methods in science, learning basic mathematical and statistical concepts combined with a practical introduction to data analysis using R.

The broader purpose of QuantCamp is to foster graduate student success in the first academic year by facilitating the transition from undergraduate work to the rigorous quantitative methods training that will be given in their home departments. The program also aims to acclimate incoming cohorts to the new academic environment at UCR and provide a space for incoming graduate students to meet and socialize with their fellow graduate students and future colleagues.

Students meet online on nine consecutive weekday mornings for 3-hour sessions via Zoom (27 hours of instruction total). 

Students must have at least 80% attendance, satisfactorily complete a final R-script and plots project, and submit an end-of-course survey to earn the credential. 

Learning Objectives

1. Apply math and statistics concepts necessary for practical data analysis

   1. vector and matrix operations

   2. summation and product notation

   3. logarithms - lg(x) and ln(x)

   4. functions

   5. limits

   6. probability theories

2. Locate online and install R, R Studio, and Tidyverse packages

3. Use R and relevant packages to perform basic data manipulation commands and functions

4. Use R and relevant packages to perform basic simulation commands and functions

Assignments Completed

1. Perform basic data manipulation functions using the following 4 sets of commands:

   1. filter(), mutate(), select(), arrange(), summarise(), ifelse()

   2. tibble

   3. base plots (scatter plot, density plot, histogram, box plot)

   4. ggplot

2. Perform basic simulation functions using the following command and processes:

   1. set.seed()

   2. random sampling

   3. problem solving

3. Achieve 80% attendance (22 hours) or higher

4. Complete the program survey at the end of the course