This book is still being written and revised to teach the course Introduction to Data Science (using R, ADILT) at the University of Konstanz in 2021. It currently serves as a scaffold for the curriculum that will be filled with content as we go along.
Contents and audience
This book will eventually contain materials needed to teach a variety of introductory courses on data science for undergraduate students of various disciplines. The materials and examples are designed to engage and motivate students from different fields to apply computational tools to solve challenging problems. Hopefully, students will welcome the summaries of essential commands and find solving the exercises both enjoyable and enlightning.
Depending on student needs and the goals and length of a course, some of the more specialized chapters (e.g., …) can be skipped and used as the basis of a more advanced curriculum.
By contrast, combining the chapters of Part I and Part IV with some appendices (e.g., …) provides a general introduction to data literacy and reproducible research that is using R, but not focusing on data science or the packages of the tidyverse.
As this text is still being revised and data science is a dynamic field, it is likely that the current version contains some typos and mistakes.
Please email me (as
uni.kn) to report any errors, possible improvements, or any other feedback or observations that you are willing to share.
Introduction to data science (i2ds) by Hansjörg Neth is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The HTML-version of this book uses Google Analytics to evaluate the popularity of its different chapters. The website does not collect any personal data of individual users.
This book is still being written. Its current version was generated using R version 3.6.3 (2020-02-29) and the following packages:
- base (3.6.3), datasets (3.6.3), dplyr (1.0.2), ds4psy (0.5.0.9004), forcats (0.5.0), ggplot2 (3.3.2), graphics (3.6.3), grDevices (3.6.3), grid (3.6.3), here (0.1), knitr (1.30), magrittr (1.5), methods (3.6.3), purrr (0.3.4), readr (1.3.1), rmarkdown (2.3), stats (3.6.3), stringr (1.4.0), tibble (3.0.3), tidyr (1.1.2), tidyverse (1.3.0), unikn (0.3.0.9003), utils (3.6.3).
Thanks to all package authors and the R community for making this book possible!