Data Modeling for Environmental Science
PBIO 394 (3 credits)

Spring 2022; Tuesday 11:40-12:55 pm (Eastern US) (remote via Microsoft Teams)
Instructor: Brian Beckage (Brian.Beckage@uvm.edu)
Office Hours (remote by appointment)



Course Description


Course Goals


Grading


Books & Media


Schedule

(Available in January)

R



Course Description

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This course provides an introduction to data modeling using the R statistical computing language and likelihood, information theoretic, and Bayesian approaches to inference. The course includes a focus on the R language as a tool for data modeling and emphasizes examples and case studies from ecological and environmental sciences.


Class Structure. This class is a hybrid online class, meaning that it is an online class that has synchronous and asynchronous components. The online synchronous component will meet once a week for 75 minutes, where we will engage in a variety of activities, primarily discussions of class material and assignments.

  1. Readings. We will discuss the assigned readings for that class periods. This will also present an opportunity to ask questions or seek clarification about the readings. These discussions will provide a review prior to taking a short quiz on the readings that will be posted in Blackboard.

  2. Supporting videos. Each week, students will watch a pre-recorded lecture, by the author (Richard McElreath) of our main text, where he presents each chapter. (I chose this book in part because of these video lectures). This will reinforce our chapter readings and provide further explanation of assigned material. As you are watching these video lectures, please ask questions, make comments, and respond to your classmates' questions using Yellowdig. We will discuss the chapters both in yellowdig and in our class meetings. We will also watch a series of R videos that will introduce key concepts of R. These videos do not correspond to our readings in R but provide another perspective on programming in R.

  3. Exercises. Each week, students will receive a set of problems that incorporate the statistical and/or R programming components of the class. Students will form small groups during class to work on these problems and will complete them outside of class. Although sharing of information and ideas is encouraged as a means of learning the material, each student should produce their own solutions (i.e., should write their own R code).

  4. Chapter presentations. Beginning in the second week of class, the assigned readings on statistics (not R) from McElreath will be presented by students. These short presentations should summarize and outline the material presented in the readings, including the most important and key points, and should highlight components that were confusing so that we can discuss clarify this material.


Course Goals

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    Students will

  1. gain proficiency in the R statistical programming language
  2. learn to construct and use likelihood functions.
  3. learn to use information theoretic approaches to statistical inference.
  4. become familiar with Bayesian approaches to statistical inference.


Achieving these goals will require:


Grading

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Student grades will be based on the following components:

Grading

  1. Class presentation of assigned chapters: 10% of course grade.
  2. Participation in discussions through Yellowdig: 20% of course grade.
  3. Quizzes on assigned readings: 10% of course grade.
  4. Homework assignments as Complete/Incomplete: 40% of course grade.
  5. Final exam: 20% of course grade.

Books and Media

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Students will also be assigned readings from the following set of books (below). These books will be required for this class and are available in the university bookstore or as otherwise noted below.

  1. Primary text for the statistics portion of this class: McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd Edition). ISBN-13: 978-0367139919 Link to book.
  2. Primary text for the R programming portion of this class: R for data science by Hadley Wickham and Garrett Grolemund

We will also watch a variety of videos inside of class on both statistics and R.

R

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We will build data models in the free, open source programming language R. Below are some additional resources for installing and using R.


R project for statistical computing

RStudio IDE

R for data science (book)

Installation of RStan/Stan & Rethinking package




Information on installing R and R studio.

Install R: Windows

Install R: Mac

Install RStudio

Setting Working Dir: Windows

Setting Working Dir: Mac