Environmental Data Literacy

"...the dynamic integration of data generated through observation and simulation is enabling the development of new scientific methods that adapt intelligently to evolving conditions to reveal new understanding. The enormous growth in the availability and utility of scientific data is increasing scholarly research productivity, accelerating the transformation of research outcomes into products and services, and enhancing the effectiveness of learning across the spectrum of human endeavor.

National Science Foundation. 2007

Data visualization is the key to understanding. We cannot, as a species, gain as much insight into biological phenomenon by looking at vast tables of values and data files as when we plot and visualize it. Increasingly, the amount of data and the rate at which we can accumulate it far exceeds our capacity to manually manipulate and examine it. This is why we need advanced tools and specific training to be able to wrangle these torrents of data into formats that serve our research, intellectual, societal, and personal needs. Learning to become agile in data analysis is the very heart of this course.

The following presentation (4:47) was given by Dr. Hans Rosling (1948-2017) speaking about life income and expectancy for BBC. His body of research and enthusiastic methods for visualization set the stage for a much larger appreciation for dynamical visualization processes, particularly through the past decade. These are the kinds of 'out-of-the-box' presentation and discussion allows us to gain a much more fundamental understanding from our data.

Being able to attain, work with, visualize, manipulate, and analyze data is an absolutely necessary set of skills for all environmental and biological scientists. This course is designed to help you gain foundational skills and strategies necessary for you to grow as a data scientist. This course will include:

  • Fundamentals of data acquisition
  • Approaches for visualizing data.
  • QA/QC and
  • Applying specific models to data
  • Communicating

This course is designed with the following general flow.

The iterative process of data workflows.

Once data are acquired, there is a cyclical process of visualization, transformation, and the application of statistical models. This cycle may proceed for extended periods of time, each time refining the way we look at, treat, and perform specific analyses. Eventually, this cycle leads to communication external to the data scientist, which is increasingly becoming more dynamic, as Dr. Rosling demonstrated above.

Within the general theoretical model, this course will explore, in order, univariate, bivariate, and spatial data types emphasizing the reticulate processes outline above. At the end of this course, it is expected that you will attain the skills as a data scientists, capable of working with environmental (and other) big data, forming the foundation for the rest of your graduate education.


During this semester, you will be primarily working on data. This requires us to jump directly into the kinds of tools that you will need to be proficient with, the mechanisms by which you can attain biologically relevant data, and an skills to apply those tools. We will be primarily working in R, an open-source set of software that forms the backbone of modern analytical approaches.


The syllabus covers the logistics of the course. You must read and in the space below agree to the conditions of the syllabus before we start this course.


Schedule of Topics

Schedule of Topics

Contact Information

When I am on campus, my door is always open. If you have any questions or want to chat about content in this course, feel free to pop in and see me.

Continue to the next section: Getting R