The course Introduction to Data Science (using R, ADILT) takes place at the University of Konstanz in 2021. However, as all course materials are freely available online, anyone interested in this topic is welcome to read and learn from these materials.
General information on the university’s Advanced Data and Information Literacy Track (ADILT) is available at https://www.uni-konstanz.de/en/teaching/adilt/.
- Introduction to data science 2 (ADILT, i2ds) (PSY-16710) at the University of Konstanz by Hansjörg Neth (email@example.com, SPDS, office D507).
- Spring/Summer 2021: Thursdays, 15:15–16:45, online.
- Course materials:
Completing this course enables students to understand, transform, analyze, and visualize data in a variety of ways. Whereas initial chapters provide an introduction to data types, data visualization, and exploratory data analysis (using base R and tidyverse packages), later chapters address more advanced issues of programming, predictive modeling, and creating simulations.
The course introduces the technologies provided by R (R Core Team, 2021a), RStudio, RMarkdown, including some key packages of the tidyverse (Wickham et al., 2019) (e.g., dplyr, ggplot2, tibble, and tidyr).
Audience and Preconditions
This course is targeted at students of all disciplines with a curiosity for data analysis. Prior familiarity with quantitative research methods and computer programming is a bonus, but not necessary.
More advanced students of psychology (FS 5–8) should consider the course Data Science for Psychologists (PSY-15150) that addresses additional topics and proceeds at a faster pace than this introductory course (see ZEuS for details).
In this course, we adopt an active learning and learning by doing approach. Good preparation (by working through the current topic before each session), regular attendance with active participation, and the conscientous completion of weekly exercises are essential for succeeding in this course.
Weekly readings and regular exercises are essential for learning the material and passing this course.
Grades are determined by solving weekly exercises (1/3) and succeeding in a final exam or data science project (2/3).