An intimate understanding of statistics and quantitative tools is necessary for ecologists, foresters, and conservation practitioners to tackle pressing ecological questions and management goals. In my teaching, I use a concept-based curriculum that engages students in statistics and quantitative ecology/forestry through a universal design for learning approach and open-source teaching tools. Below are some short details on courses I will soon be teaching or have taught in the past. See the Workshops tab at the top of the site for information on workshops I have taught.

**FOR 374 Forest Measurement, Modeling, and Inventory**: Mathematical and statistical foundations for the analysis of natural resource data. Statistical sampling designs, implementation, and analysis of forest inventory data. Linear and nonlinear regression techniques and their applications in allometric equations. Models for quantifying growth and yield of timber and non-timber products under different management and disturbance regimes. Application of Program R for efficient forest inventory workflows- Fall 2024, undergraduate level.
- NC State University.
- The first half of this course is based on our textbook “Introduction to Forestry Data Analysis with R”. A free working edition of the textbook is available online.

**FOR/STT 875 R Programming for Data Science**: R has emerged as a preferred programming language in a wide range of data intensive disciplines (e.g., O’Reilly Media’s 2014 Data Science Salary Survey found that R is the most popular programming language among data scientists). The goal of this course is to teach applied and theoretical aspects of R programming for data sciences. Topics will cover generic programming language concepts as they are implemented in high-level languages such as R. Course content focuses on design and implementation of R programs to meet routine and specialized data manipulation/management and analysis objectives. Attention will also be given to mastering concepts and tools necessary for implementing reproducible research.- Summer 2020 - Summer 2022.
- Michigan State University.
- Lead author on the open-source course textbook. Note that this textbook is no longer actively maintained, and thus certain sections (e.g., the spatial data analysis section) may be outdated.

**IBIO 831 Statistical Methods in Ecology and Evolution II**: This graduate level survey course is the second semester in a two-semester sequence, focused on the fundamental elements of data analysis in the fields of ecology and evolution. Students will learn how to interpret and model biological data with modern methods for estimation and inference using the R computing language. Topics include: frequentist and Bayesian inference, linear models, generalized linear models, random effects, generalized linear mixed models, multilevel models, zero-inflation models, model comparison and evaluation, and a holistic view of quantitative tools throughout the scientific process.- Spring 2022.
- Michigan State University.

A key hindrance to the adoption of modern quantitative and statistical tools is the lack of textbooks and associated teaching resources that present data analysis tools within the context of the specific applied area of interest (e.g., forestry, ecology). To that end, I have co-authored two textbooks focused on the use of R for different data analysis tasks in forestry and data science more generally. I strongly believe that such teaching materials should be open-source to mitigate learning barriers, and so I make an HTML version of all my textbooks freely available online. Below are the textbooks I am currently working on or have completed.

**Introduction to Forestry Data Analysis with R**by Andrew O. Finley and Jeffrey W. Doser (under contract with Chapman & Hall CRC). This book provides an introduction to programming for those who work with forestry and ecology data. It also serves to guide and illustrate implementation of fundamental forestry data analysis techniques using a contemporary and powerful programming language. A working online draft of the textbook is available here. We expect to have a complete published version in 2025.**Introduction to R Programming for Data Science**by Jeffrey W. Doser, Andrew O. Finley, and Vince Melfi. This book serves as an introduction to programming in R and the use of associated open source tools. We address practical issues in documenting workflow, data management, and scientific computing. This book was used to teach the online, asynchronous course FOR/STT 875 R Programming for Data Science at Michigan State University. The book is freely available here. Note that we no longer maintain this book, and so certain sections may be substantially out of date.