Introduction

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This book provides an introduction to data science that is tailored to the needs of psychologists, but is also suitable for students of the humanities and other biological or social sciences. This audience typically has some knowledge of statistics, but rarely an idea how data is prepared and shaped to allow for statistical testing. By using various data types and working with many examples, we teach tools for transforming, summarizing, and visualizing data. By keeping our eyes open for the perils of misleading representations, the book fosters fundamental skills of data literacy and cultivates reproducible research practices that enable and precede any practical use of statistics.

The materials in this book are based on a course at the University of Konstanz in 2019. The course provides an introduction to data science in R (R Core Team, 2019) from a tidyverse (Wickham, 2017) perspective and originally relied on R for Data Science (Wickham & Grolemund, 2017) as its textbook.

References

R Core Team. (2019). R: A language and environment for statistical computing. Retrieved from https://www.R-project.org/

Wickham, H. (2017). tidyverse: Easily install and load the ’tidyverse’. Retrieved from https://CRAN.R-project.org/package=tidyverse

Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Retrieved from http://r4ds.had.co.nz