GETTING STARTED

1. Review the course information including description, objectives, grading, course book and other media, resources for R, and policies.
2. Watch this introductory video on the class (0:11:37) to learn about this course and class activities.
3. Watch this short video on Why use R? (0:03:56) to learn more about the choice to use the R language.
4. Sign up for a Posit cloud student account using this link. This will be your working environment for using R in this class. There is a ~$5 monthly subscription fee.
5. Log onto the Brightspace page. From the class Brightspace page, you can find a link to the class syllabus and schedule and the Yellowdig discussion forum under the 'Contents & Activities' tab.
6. Login into Yellowdig, watch my introduction video, and then make a post introducing yourself (either written or video). Here is a video (0:01:56) on using Yellowdig in this class and another short video (3 mins) from the creators of Yellowdig, and here is a description of how points are earned in the Yellowdig discussions.
7. Go to the class schedule through Brightspace or using the following direct link: Class Schedule.
8. Start Week 1 by watching the introductory video, completing the readings, watching the lecture videos, then complete the assigned tasks for that week, moving from left to right, including the online discussion questions, exercises, and ending with a quiz that is accessed through BrightSpace. ( Here is a description of the quizzes and you can find instructions for accessing quizzes here).
9. After completing week 15, take the final exam that will also be accessed through BrightSpace.

Course Information

Course Description

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This course provides an introduction to data modeling using the R statistical computing language and likelihood, information theoretic, Bayesian and machine learning 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 an online asynchronous class, meaning that it is an online class that has no scheduled meeting time but allows students to work on class material on their own schedule. The class component includes a variety of activities, including videos, quizzes, discussions of class material and assignments.

1. Readings. This is a reading intensive class: We will read 1-2 chapters per week from the assigned text. The chapter readings will provide the basis for online discussions in Yellowdig. There will be weekly quizzes in Blackboard over the assigned readings.

2. Supporting videos. Each week students will watch lecture videos, where the author (Richard McElreath) of our text presents each chapter. These will reinforce our chapter readings and provide an additional perspective on the assigned readings. We will also watch a series of videos on R that will introduce key concepts in the R language.

3. Discussions.There be a weekly online discussion in Yellowdig in response to assigned discussion questions and also in response to any student questions, comments, etc. that arise organically from the class material. Yellowdig is also the primary venue for seeking help from and providing help to others.
4. Exercises. Students will receive a set of problems that incorporate the statistical and/or R programming components of the assigned chapters for each week. Students can work on these in small groups in or outside of class. I encourage students to work together and share information and approaches as a means of learning the material, but each student should produce their own solutions (i.e., should write/type their own R code).

5. Quizzes. There will be a shortly weekly quiz over the assigned material from that week.

Course Goals

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Students will

1. gain proficiency in the R statistical programming language
2. become familiar with Bayesian approaches to statistical inference.

Achieving these goals will require:

• Completion of assigned readings and exercises
• Participation in assigned activities

Grading

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Student grades will be based on the following components:

Grades less than 70 are considered failing for grad courses.

Books and Media

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Students will also be assigned readings from the primary text (1 below) required for this class and that is available in the university bookstore or online. I have also included some reference books for will R but these are not required.

1. Primary text (required) for 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. Reference texts (not required) for R:
   1. The Book of R by Tilman M. Davies. Base R perspective. A good place to start.
   2. R for Data Science by Hadley Wickham and Garrett Grolemund. R from a Tidyverse perspective.
   3. Advanced R by Hadley Wickham. Great book for understanding the language.

I will also occasionally assign other readings or media as appropriate.

Policies

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• Late Assignments
• Academic Honesty and Plagiarism
• Student Learning Accomodations
• Academic Integrity
• Religious Holidays

R language

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We will use the free, open source programming language R to build data models. . We will be using the R language in an online cloud environment called Posit Cloud. This will allow me to set up a common environment for all students, assign and evaluate exercises, and examine student code. There is a monthly subscription cost of ~$5 per student during the semester. Follow this link to join Posit cloud for this class.

Below are additional resources for R.