Course Description
Foundational data skills for natural resource professionals using R. Covers how collection and entry decisions shape what analyses are ultimately possible, and builds hands-on competency in data import, organization, cleaning, visualization, and reproducible reporting. No prior programming experience is assumed.
Athena Title
Data Fundamentals Nat Res Prof
Equivalent Courses
Not open to students with credit in FANR 5050, FANR 7050
Non-Traditional Format
This course will be taught 95% or more online.
Semester Course Offered
Offered fall and summer
Grading System
A - F (Traditional)
Student learning Outcomes
- By the end of the course, students will be able to evaluate how data collection, entry, and storage decisions affect the analytical options available for a dataset and recommend improvements to support intended analytical goals.
- By the end of the course, students will be able to import, organize, and manipulate natural resource datasets using R.
- By the end of the course, students will be able to recognize and correct common data quality problems, including errors, inconsistencies, and structural issues, in professional natural resource datasets.
- By the end of the course, students will be able to construct and interpret data visualizations using R, selecting appropriate plot types and design choices to communicate findings clearly for both technical and non-technical audiences.
- By the end of the course, students will be able to construct and share a reproducible data workflow using R and supporting tools for reporting and version control, producing documentation that allows others to understand and replicate the analysis.
- By the end of the course, students will be able to apply professional standards for data documentation, sharing, and responsible data management in natural resource contexts.
Topical Outline
- Data in natural resource management: sources, structures, and the case for reproducible workflows
- R fundamentals: environment, data structures, and programming basics
- Data import, wrangling, transformation, and tidy data principles
- Data cleaning and quality assessment
- How collection, entry, and storage decisions affect analytical options
- Exploratory data analysis and descriptive summaries
- Data visualization principles and practice for professional audiences
- Project organization, reproducible reporting, and version control
- Professional data documentation and sharing practices
Institutional Competencies Learning Outcomes
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
