F&ES 603b / 2019-2020

Environmental Data Visualization & Communication (see description for application procedure)

Credits: 3

Spring 2020: M, 10:30-12:20, Kroon 319

Welcome to the Information Age. Data production is growing at 50 percent per year, or more than doubling every two years. We are not only producing more data from existing sources, we are also constantly creating entirely new streams of data, whether statistical, text, audio, video, sensor, or biometric. Yet our ability to access, manage, understand, and synthesize all this data is extremely limited. Visualization is a powerful means of enhancing our cognitive abilities to learn from data, especially when informed by insights into human behavior and social systems. While developing the quantitative skills necessary for analyzing Big Data is important, understanding how to effectively explore and communicate insights from data—whether big or small—is equally essential for policy makers, researchers, and practitioners alike. 

Application Procedure:
Applications due 5:00 pm EST on Friday, December 13th for FES 603b: Environmental Data Visualization & Communication (3 credits) Spring 2020, Mondays 10:30am–12:20pm
APPLICATION PROCEDURE: If you are interested in taking the course, please read the description below and email Dr. Jenn Marlon (jennifer.marlon@yale.edu) and Dr. Simon Queenborough (simon.queenborough@yale.edu) with the following information by 5:00pm, DECEMBER 13th:
1. Name
2. Degree program and expected graduation date
3. List of previous relevant coursework, including experience in R or other programming language/s, and statistics
4. What previous experience do you have, if any, with communication, graphics, or design?
5. What kind(s) of projects or datasets are you most interested in using to design visualizations? (If you already have a specific project or dataset in mind please describe it and state whether you have the data already in hand)
6. A brief explanation of why you want to take the course and how it would further your academic and career goals (200 words max)
SELECTION PROCEDURE:For logistical reasons, enrollment is limited to 16. Top priority will be given to students for whom the course will clearly advance their academic/career goals (i.e., students planning to communicate with and produce graphics in their careers). Some previous coursework in statistics, data science, or programming is also required.
Given the limited class time, it is critical that students have a basic understanding of the R programming language before the first day of class. Accepted students with little-to-no current R experience will be required to take additional material before the first class, e.g.:
- Intro2R, www.intro2r.info; or
- Learn R by Barton Poulson from LinkedIn Learning, https://www.linkedin.com/learning/learning-r-2/r-in-context?u=2110361
Readings for first class:
Wickam & Grolemond. 2017. R for Data Science. Introduction. https://r4ds.had.co.nz/introduction.html
Wickam & Grolemond. 2017. R for Data Science. Explore. https://r4ds.had.co.nz/explore-intro.html
Bruce & Bruce. 2017. Practical Statistics for Data Scientists. Exploratory data analysis.  https://www.oreilly.com/library/view/practical-statistics-for/9781491952955/ch01.html