Introduces descriptive statistics and visual representations of quantitative data. Examines data sets using graphing and statistical software packages. Demonstrates how to present data in ways that are accurate, effective, and visually appealing.
Data visualization is a growing interdisciplinary field. This class will review the latest theories in data visualization and apply them to real data sets. Students will have the opportunity to explore multiple representations of quantitative data and investigate how those representations help create new knowledge.
Student learning goals
Understand when and how to calculate descriptive statistics for a data set including mean, median, mode, standard deviation, percentages, and correlation.
Be able to read, interpret, and critique a variety of visual representations of data including tables, charts, maps, etc.
Be able to determine which visual representation for a given data set would be an effective display of the the critical information.
Be able to present data in multiple formats including the use of various software packages to produce those formats.
General method of instruction
This class will be a mixture of lecture, labs, group work, and discussion.
No pre-requisites are needed for the class, just a general interest in the meanings and interpretations of quantitative data.
Class assignments and grading
The class will be graded on participation, problem sets, and a larger data analysis project.