Semester course; 3 lecture hours. 3 credits. Enrollment is restricted to students with graduate standing or those with one course in statistics and permission of the instructor. Develop quantitative skills for the visualization, manipulation, analysis, and communication of environmental "big data." This course focuses on spatial environmental data analysis, interpretation, and communication, using real-time data from the Rice Rivers Center and the R statistical analysis environment.
As both a student and instructor in statistics classes, I found I spent a vast amount of time and effort describing the characteristics of statistics (derivations, expectations, etc.). This is perfectly fine; it is important on many levels to make sure that practitioners understand the basis and context of all the kinds of analyses they use. However, the drawback here, in my experience, is that once you've spent a semester or year getting all this knowledge under your belt, and you can easily demonstrate your understanding of the parameters in a model, you cannot actually work with real data. This class is designed to produce practitioners of data analysis.
To understand data analytics, one needs to recognize the entire workflow. Below is a brief graphical depiction of how analysis actually works—in the real world. In this class, we will work on all of these components using the open-source R language.
- Collect: Getting data from an external source into a format that you can use is often the most time-consuming step in the analysis. The content of this class will provide training in data import from local, online, and database sources.\
- Visualize: Visualizing data is key to understanding. In the image below, notice that the variables X and Y in all the displayed data sets have equivalent means, standard deviations, and correlation up to 2 decimal places! We will emphasize visualization, both static and dynamic, throughout this class.
- Transform: Pulling data into your analysis ecosystem is not sufficient. Often the data need to be reformatted and reconfigured before it is actually usable.
- Model: The application of models to subsets of data is often the step that takes the least amount of time and effort. However, the application of a model to data is not the endpoint. The model must be visualized and, many times, the underlying data or derivate data must be transformed and submitted to subsequent models.
- Communicate: The effort we put into research and analyses is meaningless without effective communication of your data and findings to a broad audience. Here we will focus on how to develop effective data communication strategies and formats.
The purpose of this course is to help you build your data skills and to develop a foundational understanding upon which subsequent courses will build. The overarching goal here is to develop a working knowledge of the R statistical computing language and enough proficiency to import raw data and then iterate through the visualization, manipulation, and analysis steps in the creation of output that is easily communicated to a scientific audience.
The content of this course is built upon the following general Ctudent Learning Objectives (CLO):
Students will demonstrate functional fluency in using R and its associated libraries (e.g., Tidyverse, Quarto) for data import, transformation, visualization, and analysis, establishing a generalizable skillset for quantitative inquiry.
- Bloom's Level: Apply / Analyze
- Reinforces: Seeing R as a tool for thinking and doing, not just syntax or statistical analysis
- Notes: This aligns with the practical literacy needed to "think with data" in a coding environment. It emphasizes generalized fluency over memorization or syntax drills.
CLO 2: Analyze and interpret commonly encountered environmental data and associated analyses using appropriate exploratory and statistical techniques
Students will apply foundational exploratory and statistical approaches (e.g., binomial models, contingency tables, regression, spatial summaries) to common ecological, environmental, and evolutionary datasets to support data-driven inference.
- Bloom's Level: Analyze / Evaluate
- Reinforces: Judgment in data workflows, including exploratory iteration and critique.
- Notes: This keeps the emphasis on doing the analysis and interpreting results, not on statistical derivation of model components. It fits the framing: "not a stats class" but "using common tools to make sense of real data." It also creates space for iteration and model refinement, aligning with the "model, visualize, refine" approach.
CLO 3: Communicate data-driven findings using publication-quality scientific writing and visualizations.
Students will produce clear, compelling, and reproducible documents that communicate quantitative findings, formatted according to scientific norms and using tools like Quarto and Markdown.
- Bloom's Level: Create
- Reinforces: Scientific communication and agile presentation of quantitative and qualitative information in industry-standard formats.
- Notes: This grounds communication in scientific practice, where students must compose and format their insights clearly and rigorously. It ties tightly into how you assess work ("as if submitting for publication") and emphasizes narrative data fluency, not just procedural results.
This course is designed as a sequence of individual, stand-alone modules. Each is self-contained and includes a lecture, slides, a larger narrative document, a video demonstration, and an assessment.
| Deliverable | Details |
|---|---|
| Welcome & Logistics | Setting up the logistics for the class, getting R, RStudio, and Quarto installed on each of your machines, and getting a tour of the IDE. |
| Markdown | Establish a functional working knowledge of markdown for reproducible research and begin working with Markdown as an output for data analysis. |
| Data Types & Containers | Understanding the fundamental data types and containers within R and how to import, work with easily, and export raw data. |
| Tidyverse | Data manipulation. Like a boss. |
| Graphics that DON’T suck | Hello publication quality graphics, using the grammar of graphics approach |
| AI in Data Analytics | Integrate Artificial Intelligence in the development of code and text. |
| Points Data | Spatial process and data configurations |
| Lines, Polygons, & Shapefiles | Vector data formats |
| Raster Data | Continuously distributed spatial data |
| Statistical Confidence | Base understanding of statistical inferences and the properties of sampled data |
| Categorical~f(Categorical) | Contingency table and categorical count data |
| Continuous~f(Categorical) | Analysis of Variance (or equality of means) |
| Continuous~f(Continuous) | Correlation & Regression approaches |
| Categorical~f(Continuous) | Logistic regression |
- Course Instructor: Professor Rodney Dyer
- Email: rjdyer@vcu.edu
- Webpage: rodneydyer.com.
- Office Hours: Online Wednesday 9-10 and 1:30-2:30 via zoom or by appointment.
- Meeting Times: T/R 12:30 - 13:45
- Meeting Location: MCALC 1105
- Final Exam Scheduled: First Tuesday of Finals Week
This course requires that you bring your own laptop or other computing device that is capable of running RStudio and the R statistical language. There is no required book and all content is provided via online resources.
The grade for this course is based upon the totality of the points gained for all assignments, as well as a single large data analysis project that will be due at the end of the semester. This final will account for 10% of your overall grade. Grades will be determined using the normal 10% scale:
- A (>= 90%),
- B (>= 80% & < 90%),
- C (>= 70% & < 80%),
- D (>= 60% & < 70%), and
- F (< 60%).
All percentages are concrete, and scores will be rounded to the nearest integer; no extra credit will be given.
All of the content in this class is given as take-home assignments and tests. You will have a full 7 days to complete and turn in the work. The intention here is to give you more than sufficient time to complete the work because we do not rush data analysis. On the due date, I will post the answers so you can check your work. After the answers are posted, there will be no points awarded for late work.
All content is provided as slides, handouts, and video content. Much of the work in this class will be conducted during the in-class session. As such, you must show up to class if you intend to get the content. Data analysis is a hands-on experience, and the more doing it you engage in it, the more efficient you will become.
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