R - A non-technical Introduction with applications to Marketing

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

Summary:
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.

Instructor:
Katherine Rother
Luca Lazzaro

Type:
Block course

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

Frequency:
Each spring semester

APS (ECTS):
3

Work load statement:

Content:
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.

Language:
English

Resources:
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.

Prerequisites:
Bring a laptop and charger.

Access:
Join our courses and decide if you want to participate. Then, officially register using the“Buchungstool” at the University of Zurich.

Grading:
Daily coding exercises

Final Exam

Online exercises after class

Dates and Location:
Block course, February 5-9, 2024, 9:00 - 17:00
Location: TBDElectronic University Calendar VVZ

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

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

Note:
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.