Course Overview

Course Outline

Assignments

Resources

Policies

Insurmountable Coding Problems

Welcome to your interactive Modules!



Each module listed is linked to another page containing instructions and helpful code. There are two to three modules to perform each week, at your own pace, to be completed (along with associated Homework and Peer Commentary, if applicable) by class on the following Monday. Homework assignments are real-world applications of procedures you’ll have been guided through in your modules.

Week 01 (2025-01-21)

(No Peer Commentary due)

Module 01 - First Steps with R

Module 02 - Basics of Version Control

Week 02 (2025-01-28)

Module 03 - Reproducible Research using RMarkdown

Module 04 - Working with Other Data Structures

Module 05 - Getting Data into R

Week 03 (2025-02-04)

Module 06 - Exploratory Data Analysis

Module 07 - Central Tendency and Variance

Week 04 (2025-02-11)

Module 08 - Probabilities and Distributions

Week 05 (2025-02-18)

Module 09 - Introduction to Statistical Inference

Module 10 - Classical Hypothesis Testing

Week 06 (2025-02-25)

Module 11 - Type I and Type II Error and Power

Module 12 - Introduction to Linear Regression

Week 07 (2025-03-04)

Module 13 - Elements of Regression Analysis

Week 08 (2025-03-11)

NO CLASS: SPRING RECESS

Week 09 (2025-03-18)

Module 14 - Basic Categorical Data Analysis and ANOVA

Week 10 (2025-03-25)

Module 15 - Multiple Regression and ANCOVA

Week 11 (2025-04-01)

Module 16 - Model Selection in General Linear Regression

Module 17 - Generalized Linear Modeling

Week 12 (2025-04-08)

Module 18 - Mixed Effects Modeling

Week 13 (2025-04-15)

Module 19: A Very Brief Introduction to Bayesian Methods

Week 14 (2025-04-22)

Module 20 - TBA

Module 21 - TBA

Module 22 - TBA

Week 15 (2025-04-29)

Module 23 - TBA

Module 24 - TBA

Module 25 - TBA

Previous Student Modules (and Other Course Modules)

Introduction to GIS and Spatial Analysis in R

(Prof. Schmitt; Fall 2019)

Spit Chain: Social Network Analysis in Nonhuman Primates

(Sam Vee, Nicole Merullo, Jess Martin, Bhavya Vadavalli, & Brooke Rothamer; Fall 2023)

Goodness-of-Fit Tests

(Erin Anderson, Lillian Holden, Amanda Wu, Emily Yang; Fall 2023)

Machine Learning

(Lia Bao, Julianna Dick, Emiley Garcia-Zych, Cat Metcalf, Angelique Lindberg; Fall 2023)

Bioacoustics

(Reese Hotten-Somers, Ritika Sibal, Sofia Weaver, Paige Becker, Allister Malik; Fall 2023)

Continuous-Time Movement Modeling

(Zoe Albert, Natalia Kelley, Frank Short & Victoria Zdanowicz; Fall 2021)

Phylogenetic Comparison between Molecular and Morphological Trees

(Abby Robinson, Isabel Novick, Marta Hammers, Nirmiti Naik; Fall 2021)

Weighted Gene Correlation Network Analysis

(Victoria French, CeCe Gerstenbacher, Warrenkevin Henderson, Elizabeth Varghese; Fall 2021)

Time Series Analysis

(Gabriel Vicencio, Max Dippel, Diego Alonso Larre and Miguel Rubio Garcia; Fall 2021)

Discriminant Analysis

(Laura Angley, Laura Brubaker-Whitman, Christian Gagnon, Melissa Zarate; Fall 2019)

Spatial Autocorrelation

(Lara Hakam, Greg Pelose, Brenna Stallings; Fall 2019)

Population Assignment for Genetic and Morphological Data

(Ish Chowdhury, Gianna Grob, C Vega; Fall 2019)

Using LIDAR Data in R

(Nicola Kriefall, Clint Lockwood, Ena Miculinic; Fall 2019)

Species Distribution Modeling in R

(Frank Azorsa Salazar, MaJo Salazar Nicholls, Feiya Wang; Fall 2019)

Factor Analysis (and MAYBE Principal Components Analysis)

(Graham Albert, Becca DeCamp, Faye Harwell, Zeynep Senveli; Fall 2017)

3D Visualization and Analysis

(Zach Coto, Maria Codlin, Julie Jung; Fall 2017)

Cluster Analysis

(Aarti Arora, Andrew Mark, Natalie Robinson, Audrey Tahjadi; Fall 2017)

Phylogenetic Tree Construction

(Brandon Guell, Isabella Muratore, Danielle Antos; Fall 2017)