Our society is turning into a datadriven society. According to a May 2013 article in ScienceDaily, “A full 90 percent of all the data in the world has been generated over the last two years.” We can learn a great deal from these massive amounts of data, and a "data science" approach can help us do so. Data science methods are used to derive knowledge from data in academic research, companies, governmental agencies, and any other organization that wants to make databased decisions. This course offers an introduction to the use of data science methods for social and behavioral science research. Upon completing this course, students will have acquired the skills necessary to apply statistical data science techniques to summarize and visualize complex data, discover patterns, and predict outcomes and trends for unseen data. Topics include prediction, classification, clustering, dimension reduction, shrinkage approaches, and more.
Specifics
During the course, students will complete two group assignments in which they will apply their data science skills to real behavioral and social science data. The assignments will be performed using the opensource statistical software platform R. The course will be completed with a written exam.
This course is compulsory for students of the major Psychological Methods and Data Analysis.
Recommended Prerequisites
Familiarity with basic statistics, in particular linear regression, is assumed.
Required Prerequisites
N/A
Recommended Reading
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: Springer.
