Teaching Assistant
HS550: Environmental Nursery Production Fall 2025 Number of Students: 09
The course focuses on the impacts of the nursery industry on the environment and environmentally sound nursery practices. Exploration of the major challenges facing the nursery industry that drive decision making during production. Evaluation of past and current research addressing these challenges and sampling procedures and interpretation will be learned. Graduate status and an undergraduate nursery production or management course or working knowledge of nursery production required.
Introduction to Programming with R
This beginner level online course offers an introduction to programming with R, a widely used language for statistical computing and data visualization in data science and related fields. You’ll learn to work with RStudio, a popular integrated development environment (IDE), and explore how to represent real-world data using vectors, matrices, arrays, lists, and data frames. You'll filter data using conditional logic to analyze subsets effectively and apply functions and loops to manipulate and summarize datasets. The course also covers writing custom functions to organize code and handle errors. You'll tidy data using the tidyverse and create compelling visualizations using R’s grammar of graphics. By the end of the course, you'll be able to package, test, and share your R code.
The course is adapted from CS50r: Introduction to Programming with R.
If you are interested in participating in our next batch, please email your expression of interest or find the registration link below.
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Data Analysis & Visualization Using R
Number of Batches: 5 Number of Students: 90
This intermediate level online course is designed for undergraduate and graduate students with a background in agricultural science who are looking to strengthen their data analysis skills. It combines theoretical instruction with hands-on training in the fundamentals of statistical analysis essential for agricultural research. Using the R programming language, you'll learn to manage and analyze real-world agricultural data, with a focus on examples from plant breeding and related fields. The course covers key statistical concepts such as descriptive statistics, ANOVA, genetic parameter and path analysis, correlogram, and graphical visualization. Through guided exercises, you’ll gain experience applying these methods using R, interpret, and draw meaningful conclusions to support research. By the end of the course, you'll be equipped to apply basic statistical techniques confidently in your own agricultural research projects.
If you are interested in participating in our next batch, please email your expression of interest or find the registration link below.