Course Syllabus
Time: Lecture – 2:00 - 3:15 PM; Tuesdays and Thursdays
Instructor: Dr. R. Quinn Thomas
Wickham, Hadley and Garrett Grolemund. 2017. R for Data Science. O’Reilly Press
- Available for free online: https://r4ds.had.co.nz
Other readings available on Canvas in the Files/Readings folder
Sophomore standing required. Foundational knowledge in quantitative and computational thinking expected.
While there are no specific prerequisites for the class, this class requires computer programing from the first class period. The course does provides multi-week training in the R programming language. However, students must commit to learning and applying computer programming. If you are concerned whether you are prepared for the course, please see me after the first day of class to discuss.
- Apply statistics and modeling to the analysis of environmental data collected at multiple spatial-temporal scales
- Demonstrate how to acquire and store environmental data from data repositories
- Create visualizations of environmental data, analyses, and uncertainty
- Evaluate ethical and methodological issues associated with data curation, quality control, and sharing.
- Explain the application of computational or quantitative thinking across multiple knowledge domains.
- Apply the foundational principles of computational or quantitative thinking to frame a question and devise a solution in a particular field of study.
- Construct a model based on computational methods to analyze complex or large-scale phenomenon.
- Draw valid quantitative inferences about situations characterized by inherent uncertainty.
- Identify ethical issues in a complex context.
- Articulate and defend positions on ethical issues in a way that is both reasoned and informed by the complexities of those situations.
- Module assignments (60%): Most of class time will be focused on analyzing different environmental dataset. Each module will help develop different environmental informatics skills. Each student will submit a written assignment associated with each module.
| Module | Title | Environmental Focus |
|---|---|---|
| 1 | Climate Change and Lakes | How does climate change alter lake temperature? |
| 2 | Data Analysis and Visualization in R | Animal populations |
| 3 | Lake Ice Phenology | How is global ice cover in lake changing? |
| 4 | Water Quality | Where are rivers in the U.S. exceeding legal nutrient levels? |
| 5 | Global Climate Change | How are global temperatures changing and what is causing it? |
| 6 | Terrestrial carbon flux | How much carbon dioxide does a forest remove from the atmosphere? |
| 7 | Terrestrial Carbon Stocks | How much carbon is stored in a forest ecosystems and where in the ecosystem is it stored? |
| Ethics and Environmental Informatics | What are important ethical issues associated with environmental data? |
- Exams (40%): The in-class exams assess the student’s capacity to apply previously learned techniques to a new environmental dataset. There will be two exams through the semester.
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The course uses the R programming language exclusively. R is a common language in data science and ecology. There will be instruction in R throughout the course but this is not a programming course. R is a free language. This allows you to use it in your future career without need to purchase a license.
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You are expected to bring your laptop computer to each class because you will be using in most classes. Your laptop must have R and RStudio installed and working.
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Group vs. individual work. Each assignment will specify whether it is group or individual work.
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The Undergraduate Honor Code pledge that each member of the university community agrees to abide by states:
- “As a Hokie, I will conduct myself with honor and integrity at all times. I will not lie, cheat, or steal, nor will I accept the actions of those who do.”
- Students enrolled in this course are responsible for abiding by the Honor Code. A student who has doubts about how the Honor Code applies to any assignment is responsible for obtaining specific guidance from the course instructor before submitting the assignment for evaluation. Ignorance of the rules does not exclude any member of the University community from the requirements and expectations of the Honor Code.
- For additional information about the Honor Code, please visit: https://www.honorsystem.vt.edu/
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Your attendance at lecture is expected, and you are expected to be at class on time. Each class may include an in-class activity that requires your attendance to receive credit.
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If you have a question, please do not hesitate to ask. In fact, other students probably have the same question! Come to lecture on time since we will start right away. In particular, the first five minutes of each lecture are quite crucial because they establish the direction for that session. Therefore, if you come in late, certain things may not make sense, and you will miss important announcements. Throughout the semester, please be courteous to all of your fellow students and to me so we can create a positive learning environment.
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All assignments and lecture pdfs will be distributed using Canvas.
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Any student with special needs or circumstances for exams or assignments should make arrangements to meet with the instructor during the first week of classes.
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You are strongly encouraged to complete the Student Perceptions of Teaching (SPOT) questionnaire. Constructive student feedback is important for enhancing the learning experience in this course. Changes to the class and instruction may result from suggestions that are shared with me. Comments about specific aspects of the course or instruction are most helpful. For example, past comments indicated that real-world examples were important for helping students to understand key concepts, and so more of these examples were added to the course materials.