Through a partnership with the Library’s Research Data Services, DSI is offering new 1-credit short courses, Data Wrangling in R and Applied Causal Inference. These meet for 5 weeks and are designed to provide some focused, hands-on training on different topics and tools. DSI is also partering with the School of Architecture to open the Data Visualization course to a broader audience. Researchers from any disicpline are invited to join any of these classes; the descriptions for each are below.
DS 6559-001 Data Wrangling in R Instructor: Clay Ford Description: This course covers data cleaning and data manipulation in R. Topics include reading in/writing out data in various formats, R data structures, working with date/time data, character manipulation, using regular expressions in R, reshaping data, data transformations, data aggregation and basic data visualization.
DS 6559-002 Applied Causal Inference Instructor: Michele Claibourn Description: This course examines approaches to causal inference using the potential outcomes framework. Methods covered will include matching, difference-in-difference, regression discontinuity, and instrumental variables, . The course will be a mix of lectures, discussion, and application examples in R and will emphasize understanding the approaches conceptually and implementing them computationally.
SARC 5400-001 Data Visualization Instructor: Eric Field Description: This is a course about information and data visualization. We live in a world rich with information. This course teaches visual and spatial thinking coupled with data analysis tools and custom web-enabled programming to construct and envision information. To find and even invent approaches toward seeing into complex problems, we will study, and make, useful, compelling and beautiful tools to see.