The RIEEE EnviroData Collaborative (EDC) is a new program designed to meet a growing need at Appalachian State University: building environmental data science and modeling capacity through hands-on skill building, shared learning, and collaborative exchange. As the pace of innovation in areas like machine learning, remote sensing, and cloud computing accelerates, researchers across disciplines are seeking opportunities to update and expand their technical skill sets and to continue to learn beyond graduate training.
Through periodic, skill-focused workshops led by both campus experts and invited facilitators, this program will create space for faculty and research staff to gain practical experience with new tools, exchange knowledge, and grow their capacity to conduct cutting-edge, data-intensive research. Beyond individual skills, the EDC will foster a culture of collaboration and interdisciplinary connection. Our goal is to cultivate a vibrant, exchange-driven community that enhances the visibility of environmental data science at Appalachian and increases the impact of faculty research. Register to attend one of our upcoming fall workshops:
Getting Hands-On with Data: Interactive visualizations in RInstructors: Michael Erb, Research Scientist in RIEEE; William Armstrong, Associate Professor in GES Date & Time: September 23, 2–4 p.m. Location: Anne Belk Hall, Room 222 Register here.
Interactive visualizations provide an engaging way of exploring large datasets and browsing data with peers and students. In this workshop, you’ll learn to process data in R and produce interactive figures using several R packages: tidyverse, plotly, and leaflet. We’ll focus on two main examples: an interactive 3D scatterplot of penguin data—which can be zoomed, rotated, and filtered—and an interactive map of North Carolina population. Other visualizations will be included throughout. Participants will learn to: Load data in R and process it with tidyverse Create interactive figures with plotly Create an interactive map with leaflet Browse these figures locally or share them online using App State webspace
From Data to Decisions: Supervised machine learning with RInstructors: Lasanthi Watagoda, Assistant Professor in MAT; Hasthika Rupasinghe, Associate Professor in MAT; William Armstrong, Associate Professor in GES; Michael Erb, Research Scientist in RIEEE Date & Time: November 12, 1–3 p.m. Location: Anne Belk Hall, Room 222 Register here.
Unlock the hidden patterns in your data! The ability to discover associations buried within large and complex datasets is becoming a must-have skill for modern researchers and educators. This hands-on workshop is designed to empower faculty with practical tools in supervised machine learning using R, providing both conceptual insights and applied techniques.
We will begin with an overview of supervised learning, focusing on regression and classification tasks, and demonstrate how to set up and evaluate models using training and testing data. Participants will also explore powerful variable selection methods like LASSO and Elastic Net, which simplify models and enhance interpretability. The second half of the workshop highlights decision trees as a foundational model and shows how they can be strengthened through ensemble approaches—including bagging, random forests, and boosting—to deliver stronger predictive performance. Participants will learn to: Differentiate between regression and classification in supervised learning Apply LASSO and Elastic Net for variable selection and model improvement Build and interpret decision tree models Explore ensemble techniques: bagging, random forest, and boosting Gain hands-on experience with training/testing workflows in R
Please contact Michael Erb (er...@appstate.edu) with any questions. | 
