R - A non-technical Introduction with applications to Marketing

(lecture, block course)

R Intro
"Not everything that can be counted counts, and not everything that counts can be counted."
Albert Einstein

People that use data analytics often spend more than 80% of their time with collecting, cleaning, and organizing data and only 20% with applying statistical models. This is not only true for real world analytics, but also for data analyses within bachelor/master theses.
This class will prepare you for those challenges by applying a non-technical approach. Meaning we start every unit with “real-world” questions and explain key programming concepts "on the way" while discussing the solutions to these questions.
This class is a lecture with integrated exercises. For every session, you are required to bring your laptop with the latest R version installed. We do not require any experience with R as we start from the very beginning (i.e. installing R and RStudio). However, we do require the willings to actively participate and contribute to the class. No statistical models (besides mean and standard deviation) will be discussed in this class.

Have a look at our trailer of the R Into class!

Katherine Rother
Debora Costa

Block course

Target Audience:
Bachelor students assigned to “Wahlpflichtbereich" BWL 4

each semester


Work load statement:

Part Workload Total Time
Course attendance 5 sessions à 7h 35h
Course preparation 25h 25h
Online exercises 30h 30h
Total   90h

Presenting data management and munging techniques that "scale well", i.e. that are applicable to datasets which are usually observed in real-world settings (1 to 100 GB) by working with R.


Crawley, M. J. (2015). The R Book. Chichester, Wiley.

The lecture will be complemented by online exercises on the e-learning platform provided by datacamp.com.

Datacamp logo

Bring a laptop.

Join our courses and make up your mind if you want to participate. Then officially register using the “Buchungstool” at the University of Zurich.

In-class tests, and final report.

Dates and Location:
Block-course, 7-11-February 2022, 9:00 - 17:00
Location: tbaElectronic University Calendar VVZ

We don’t require any prior knowledge on R. However, should time permit you can have a look at any introductory online course on R, e.g. at www.datacamp.com.

Don’t forget to officially register yourself using the registration tools at the University of Zurich.

This information in the syllabus supports the official information in the electronic university calendar (VVZ – Vorlesungsverzeichnis). In cases of doubt, the official information at the VVZ is valid.