Spring 2021 catalog is now live!
To determine if a course is remote, hybrid, or in-person use the catalog search tool to narrow results. Otherwise, when browsing courses, the section indicates teaching mode:
R = Remote
H = Hybrid
0 = In-person
Teaching modes (remote, hybrid, in-person) are subject to change at any point. Please pay close attention when registering. Depending on the timing of a teaching mode change, faculty also may be in contact with students.
This course is about preparing, visualizing, reporting and presenting different types of data. We will start with creating common plots (e.g., barcharts, histograms, density plots, boxplots, time series and lattice plots), but also discuss visualizing results of statistical models, such as linear or logistic regression models. We will use the ggplot library in R but then switch to the plotly library for interactive graphs with mouse-over and click events. Using R’s shiny and DT libraries, we will learn how to create and publish web-apps and dashboards that explore datasets and support online filtering. We will end the class with creating web apps that contain multiple graphs or maps which react to user inputs (such as selecting which variables to plot) or provide real time monitoring of streaming data. Throughout, we will use version control software (Github) to organize and keep track of our code.
This course will be taught in a semi-flipped style. While the instructor will introduce certain topics, students will often be responsible for reading material ahead of time and then work individually or in pairs to reproduce material or implement it on their own data.
Grading: no pass/fail option,
yes fifth course option
Grading will almost entirely be based on class participation, individual and team-work, project presentations and the student's portfolio.
Stat 201/202/302; Good knowledge of R
Preference may be given to stats majors who need the course in order to graduate, but then random selection.
This course teaches how to organize and present data graphically, but also how to critique existing data visualizations.