| Titel | Zeit | Ort | Dozent |
|---|---|---|---|
| R for Data Science | 16.09.2019 09:00 - 17:00 (Mo) | Raum 003: TUM Graduate School, Boltzmannstr. 17, 85748 Garching | Dr. Stephan Haug |
| R for Data Science | 17.09.2019 09:00 - 17:00 (Di) | Raum 003: TUM Graduate School, Boltzmannstr. 17, 85748 Garching | Dr. Stephan Haug |
In this learning-by-doing course, doctoral candidates will receive an introduction to data science with R. They will learn how to get data into R, get it into the most useful structure, transform it, visualize it and model it. In the modeling part we will focus on the linear model.
The course shows how to use the grammar of graphics, literate programming, and reproducible research to save time. Therefore we will use the tidyverse (https://www.tidyverse.org/), knitr (https://yihui.name/knitr/) and rmarkdown (https://rmarkdown.rstudio.com/) package.
A sound knowledge of basic statistics is required for getting the most out of the course. In addition, you should be familiar with the basics in R.
Literature: [1] Grolemund, G. and Wickham H. (2017). R for Data Science. O'Reilly. r4ds.had.co.nz/index.html
Important: Please bring your laptop with you and install R and RStudio (https://www.rstudio.com/). If you haven’t had an introduction to R, please attend the swirl course (see https://github.com/swirldev/swirl_courses#swirl-courses) R Programming.
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