This is the website for the Data Science with
R (DataSciR) course offered in 2021. Click here if you are looking for the 2020 course website.
The course is limited to max. 30 students. Please apply for DataSciR by completing the following tasks until 03. April 2021:
On 05.04.2021, you will be notified if you can attend the course.
After admission, please complete this registration form until 20.05.2021.
Ten years ago, who would have thought, that
R, the “environment for statistical computing and graphics”, would become one of the most popular programming languages for data scientists?
The impressive growth of
R is not a coincidence. As free & open-source alternative to expensive & proprietary software like SPSS, Matlab and Excel,
R’s strengths have always been its capabilities for statistical data analysis as well as its functionalities to create powerful, aesthetically appealing graphics and charts.
R attracted a rather exclusively academic audience in the 90’s & 00’s, the
R community since has grown not only by sheer number but also in diversity, as people from different industries and backgrounds discover
R’ usefulness for a wide range of applications. As of February 2020, more than 15,000 (!) packages have been published to CRAN, ca. half of them since 2015.
Especially in the last decade, the functionality and versatility of
R has gained momentum. Among the most popular
R packages are:
In Data Science with R (DataSciR), you will learn fundamentals of
R and how to use the following packages for Data Science:
tidyverse” which includes packages like
tidyrfor data manipulation and
ggplot2for data visualization,
knitrfor reproducible & automated reporting,
shinyfor creating interactive web applications, and
tidymodelsfor inferential and predictive modeling.
You will demonstrate your proficiency in these packages on a semester-long graded data science project.
|1||05.-11.04||Welcome & Introduction||Welcome, First tour of R & RStudio||Welcome & Course Intro, First tour of R & RStudio||Ex 1||–||–|
|2||12.-18.04||Visualizing data with ggplot2||Introduction to ggplot2, Effective visualizations||Intro to ggplot2, Visualizing numerical and categorical data, Tips for effective vizualizations||Ex 2, Code-along||Q 1||R4DS chapters: Data Visualization, Exploratory Data Analysis, Graphics for communication. ggplot2 cheat sheet. Official ggplot2 book. curated list of ggplot2 resources and extensions. Keynote talk: Alberto Cairo - How Charts Lie: Getting Smarter About Visual Information|
|3||19.-25.04||The Tidyverse||The Tidyverse||Tidyverse intro & dplyr, Join functions, tidyr, readr&tibble||Ex 3, Code-along||Q 2||R4DS chapters: Data transformation, Tibbles, Data import, Tidy data, Relational data, Pipes. Cheat sheets: dplyr, Data Import (readr & tidyr).|
|4||26.04-02.05||R Markdown||R Markdown||R Markdown||Ex 4, no code-along this week||Q 3||R4DS chapters: R Markdown, R Markdown formats, R Markdown workflow. Quick Markdown tutorial. R Markdown cheat sheet. RStudio’s R Markdown reference guide. RStudio’s R Markdown Get Started Tutorial. List of all available Knitr chunk options. Book R Markdown: The Definitive Guide|
|5||03.-09.05||The R language: vectors, classes, functions, iteration||Vectors, classes, functions, iteration||Vectors, Classes, Functions, Iteration||Ex 5, Code-along||Q 4||R4DS chapters: Factors, Dates and times, Functions, Iteration|
|6||10.-16.05||Linear models||Linear models||Correlation, Linear regression||Ex 6, Code-along||Q 5||Tidy Modeling with R chapter: Fitting models with parsnip. R4DS chapters: Model basics, Model building, Many models|
|20.05||Deadline: project proposal submission & registration|
|7||17.-23.05||Data modeling with tidymodels||tidymodels||tidymodels||Ex 7, Code-along||Q 6||Tidymodels tutorial. Book Tidy Modeling with R.|
|8||24.-31.05||Creating web applications with shiny||shiny||TBA||Ex 8||Q 7||RStudio’s “Learn Shiny” tutorial. Mastering Shiny. Shiny cheat sheet. “Awesome” Shiny Extensions.|
|9||01.06-07.06||Misc. topics||Misc. topics||TBA||–||–||–|
|06.07||Deadline: project submission|
|09.07||Deadline: final presentation|
There are no mandatory prerequisites for DataSciR. However, you are expected to have a profound knowledge of fundamental data mining techniques, such as classification, regression and clustering. Hence, it is recommended that you have heard at least one of the following lectures (or comparable):
Also, you should have a basic programming and statistics knowledge. For example, you will learn the most important vector types and classes in
R, but you will not learn what a vector or a class is in general. Accordingly, you should know what the terms mean, standard deviation, probability, etc. mean.
Data Mining / Statistical Analysis:
caretpackage. Online documentation.
By the end of the first week, you should have installed the following software on your own laptop:
Also, please check whether you can successfully install packages. To do so, click on the Packages tab in the bottom-right pane in RStudio. Then, click on the Install button and specify an arbitrary package, e.g.
dplyr. Finally, click on Install. Alternatively, you can install a package from the console with
install.packages("dplyr"). If everything is set up correctly, no error messages should be displayed when you load the installed package with