# Course Coordinates

- PSY-15150, at the University of Konstanz by Hansjörg Neth (h.neth@uni.kn, SPDS, office D507).
- Summer 2019: Mondays, 15:15–16:45, D435.

- Links to current course syllabus | ZeUS | Ilias
- Essentials and WPAs at https://bookdown.org/hneth/ds4psy/.

# Course Description

## Overview

This course provides an introduction to R and conveys fundamental skills of data literacy and the basics of data science. It is suited for beginners and experienced students and contains 3 parts:

First, we introduce key concepts and commands of the R programming language for statistical computing. This includes working with the R Studio environment and writing reproducible research documents with R Markdown.

By working with different forms and types of data, the course provides a basic introduction to

*data literacy*. Although later sessions add elements of*computer programming*(e.g., writing functions and loops), our focus remains on making sense of data (e.g., by creating summary tables and visualizations).Regular exercises with real datasets explore the tools of the so-called tidyverse (including the R packages

`dplyr`

,`ggplot2`

, and`tidyr`

).

The course is based on chapters of the popular R for data science textbook (Wickham & Grolemund, 2017), but topics and datasets used in the course are geared to the interests and needs of psychologists (e.g., involving the data of patients or experimental participants).

Completing this course enables students to understand, transform, analyze, and visualize data in a variety of ways. While this course does not deal with *statistical testing* and only scratches the surface of *computer programming*, it teaches *reproducible research practices* and covers fundamental knowledge and skills of *data science*.

Enrolling in this course assumes no prior knowledge in programming or statistics, but motivation for weekly readings and for regularly solving exercises. Grades are determined by submitted solutions to exercises and a final project or exam.

## Background

Students of psychology and other social sciences are trained to analyze data. But the data they learn to work with (e.g., in courses on statistics and empirical research methods) is typically provided to them and structured in a (mostly rectangular and often *tidy*, Wickham, 2014)