Course Description

The field of entomology addresses diverse and unique types of data that requires analytical skills for both academic and non-academic job markets. This course will cover R programing language skills to manage, analyze, and visualize data. These skills can be used to address the analytical needs for professional entomologists.

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Athena Title

Analysis in Entomological Sci

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Prerequisite

Math proficiency is required, and prior statistics course experience is highly recommended.

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Semester Course Offered

Offered fall

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Grading System

A - F (Traditional)

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Student learning Outcomes

• Students will be able to recognize different functions, syntax, and file types used in R.
• Students will be able to interpret textbook and online example code to be repurposed for their own data.
• Students will be able to complete various simple statistical analyses using various types of data.
• Students will be able to determine the appropriate analysis based on the type of data and experimental design.
• Students will be able to design and manipulate data visualizations of publication quality.
• Students will be able to effectively defend and explain data analyses and interpretation of results.
• Students will be able to understand the importance and practice of reproducible science.

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Topical Outline

• Lecture: Review Syllabus, Types of Data Lab: Basics of R
• Lecture: Data Management, Transformations, and Visualization Lab: Tidyverse and ggplot2 Package
• Lecture: t-test, Chi-square, and Ordinary Least Squares Lab: Simple Linear Regression Excel exercise, lm function
• Lecture: ANOVA and ANCOVA, Package Report Due Lab: ANOVA Type I, II, or III, Multiple Comparisons, Anova vs. aov function
• Lecture: Metadata and Reproduceable Science Exam I Lab: Messy Data, RMarkdown and Quatro
• Lecture: Experimental Design (CRD, RCBD, Split Plot, Latin Square, etc.) Lab: Block and Random Effect Notation
• Lecture: Complex Data Visualizations, and MLE Lab: ggplot2 Package Specifications
• Lecture: Logistic Regression Lab: family syntax
• Lecture: Count Models and Overdispersion, Script De-Bug Due Lab: family syntax cont.
• Lecture: Mixed Effects Models Exam II Lab: glmmTMB vs. lme4 Package
• Data Workshop
• Student Presentations
• Final Exam

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Institutional Competencies Learning Outcomes

Analytical Thinking

The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.

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Syllabus

Select Faculty / Instructor to download syllabus:   Fair, Conor

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Public CV

Select a file to download:   Fair, Conor

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