Over the last years, companies have invested heavily in their data science departments. Experiments and machine learning algorithms are running around the clock to gain insights from large amounts of data. In this course students will be introduced to useful tools and practical insights, which help with producing actionable outputs from datasets.

We will discuss a full data-science workflow, which will include (if time permits)

• an introduction to the R programming language (for data wranling & analysis) and useful packages,
• relelavant data science topics like exploratoy and predictive (incl. time series) analysis, "causal thinking", 
• Logit and Machine Learning, using LLM as research tools,
• programmatic document creation using knitr/Quarto and version control/collaboration using git (time permitting).

Depending on need/feedback, we can discuss/skip specific topics. We will mainly focus on observational data (as opposed to surveys and experiments).

After successfully completing this course students will

• know different types of data
• can distinguish and assess different types of data analysis (causal vs. predictive, etc.)
• be able to get an overview of large data sets quickly
• produce statistical analyses and visualisations based on that data
• write scientific and technical documents using Quarto
• use git for version control and collaboration (time permitting).
• know the basic pitfalls of data-science projects

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). 

The course is taught using a combination of lectures, class discussions, case analyses, computer exercises, and student presentations. Theories will be applied to a real-world business or research cases presented by the students. The goal is to provide an open learning environment that encourages trial and error, discussions, and the development of practical skills for data-driven business/research. The focus of the course will be on gaining confidence in producing valuable outputs from a new dataset in a structured and effective way.

The course is taught using a combination of lectures, class discussions, computer exercises, and student presentations. Theories will be applied to a real-world business or research cases presented by the students. The goal is to provide an open learning environment that encourages trial and error, discussions, and the development of practical skills for data-driven business/research. The focus of the course will be on gaining confidence in producing valuable outputs from a new dataset in a structured and effective way.

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)

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