Introduction to the management, analysis, interpretation, and communication of ecological data. Students will practice critically evaluating data-based claims, developing and implementing analytical workflows (i.e., data import, cleaning, statistical analysis, and visualization) to solve data-based ecological problems, and interpreting and communicating findings from data.
Athena Title
Ecological Data Science
Equivalent Courses
Not open to students with credit in FANR 2010
Semester Course Offered
Offered fall and spring
Grading System
A - F (Traditional)
Student Learning Outcomes
By completing this course, student should be able to critically and logically evaluate data-based claims in scientific literature, the popular press, and social media.
By completing this course, student should be able to apply appropriate analytical approaches and statistical tests to different types of ecological data.
By completing this course, student should be able to develop proficiency in the use of computational tools (e.g., R & RStudio) to effectively manage, organize, analyze, model, interpret, and visualize ecological data.
By completing this course, student should be able to communicate the outcomes of data-based analyses verbally, graphically, and in written form.
By completing this course, student should be able to integrate principles of open science and data ethics into analytical workflows and when communicating ecological information.
Topical Outline
This course is organized around seven core data science principles that are explored through an ecological lens: data management, data analysis, data visualization, coding, modeling, reproducibility, and ethics.
Within these areas, specific topics include:
Data Management
• Data organization, handling, and storage best practices
• Setting up and using RStudio projects
• Importing, viewing, and exporting raw and modified data
Data Analysis
• Data tidying and transformations
• Variability, distributions, and sample size
• Logic, probability, and hypothesis testing
• Designing experiments
• Making inferences and critically evaluating results
Data Visualization
• Graph types and visualization matches
• Graph mechanics and creation
• Interpreting graphs
• Ensuring accessibility of graphical information
Coding (e.g., using R and RStudio)
• Computational thinking
• Reading, writing, and refining code
Modeling
• Model selection, assumptions, and data needs
• Applying parametric and non-parametric statistical models
• Interpreting statistical outputs, relationships between variables, and uncertainty
Reproducibility
• Data provenance and metadata
• Reproducibility and version control
Ethics
• Responsible conduct and data integrity
• Data sovereignty and open science
• Uncertainty reporting, misinformation, and social consequences of data interpretation
General Education Core
CORE III: Quantitative Reasoning
Institutional Competencies
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
Critical Thinking
The ability to pursue and comprehensively evaluate information before accepting or establishing a conclusion, decision, or action.
