Both local start-ups such as "Gurkerl" and multinational companies such as "Amazon" have invested heavily in their data science departments. Experiments and machine learning algorithms are running around the clock to gain insights from huge amounts of data. 

In this course students will learn about tools and gain practical insights into producing actionable outputs from datasets. We will discuss a full data-science workflow including and introduction to the R programming language, scientific document creation using the new Quarto format that can be used to produce reports, websites, and presentations, and version control using git for collaboration.  

Please note that some of the literature links to the German version of the books available in the WU library. All of them are available in english for free (legally) online. Either version can be used. 

You need to attend at least 80% of all classes to pass the course. Any classes missed must be compensated with a written essay about the materials covered. It is the student's obligation to gather the material necessary. This applies both to in-person as well as online classes (should the latter be necessary). Attendance is mandatory in the first lecture as well as the final lecture (for presentations). 

Grading is based on the following components:

• Final presentation (45%)
• Project plan for presentation (30%)
• Class participation (25%)

The following grading scheme is used:

< 60%                                fail (5)

60% bis 69,99%               sufficient (4)

70% bis 79,99%               satisfactory (3)

80% bis 89,99%               good (2)

>= 90%                             excellent (1)