Introduction to programming for Marketing Analytics (R or Python)
(Fall semester, Seminar)
Objective:
The module is designed to equip students with the fundamental programming skills needed to collect, clean, and organize data. Students can choose if they want to learn programming in R or Python.
We use a non-technical approach to deliver the content. We start every unit with "real-world" questions and explain key programming concepts "on the way" while discussing the solutions to these questions. We do not require any experience with R/Python as we start from the very beginning. However, we do require the willingness to actively participate and contribute to the class.
This module is part of a pilot project for a series of hybrid courses addressing fundamental concepts in data science. We would like to invite students who participate to provide us detailed feedback. This will help us understand how the concept is received and what are important areas for improvement.
Learning outcome:
Understanding fundamental programming concepts that are essential for most real-world marketing analytics applications. These include loading external data (from csv files); manipulating data (merging, aggregating, and selecting observations), simplifying complex and repetitive tasks (writing loops and conditional statements) and automating code (writing functions).
Syllabus: forthcoming
Instructors:
Dr. Radu Tanase
Fei Wang
Contact:
Fei Wang
Type:
Seminar
Target audience:
Master level students with no previous experience with coding in R/Python. Assigned to “Wahlpflichtbereich BWL 4”.
Frequency:
TBD
AP(ECTS)-points:
3
Language:
English
Course material:
All learning resources will be provided as part of the course.
Grading:
The following components comprise the final grade:
1. Analytics project I (40%): on-site, computer-based, BYOD format, bring a charger.
2. Analytics project II (40%): on-site, computer-based, BYOD format, bring a charger.
3. Analytics project III (20%): online
All assessments (either on-site or online) are computer-based. Please make sure you have all necessary equipment.
Absence Policy for Programming Project I and II:
In the event that a student is unable to participate in either of the on-site programming projects (Programming Project I or Programming Project II) due to a valid medical reason, the student must promptly submit a doctor's certificate to the instructor (within one week of the missed assessment).
With a valid doctor's certificate, students are thus permitted to miss a maximum of one (out of the two) on-site programming projects. An on-site programming project missed under these circumstances will not be factored into the final grade calculation. Consequently, only the on-site programming project taken will contribute to the final grade. If any further programming project is missed, this will be graded as "1".
Failure to provide the necessary documentation within the stipulated one-week period will result in the missed assessment being graded with a "1" and counting towards the final course grade.
This policy does not apply to Programming Project III.
Dates and Location:
Wednesdays
- 16.09.2026, 14:00-16:00h: Kick-Off (mandatory, on-site, PLM 103/4)
- 17.09.2026 - 20.10.2026: Learn the fundamentals of programming for marketing analytics I (online, asynchronous)
- 21.10.2026, 14:00-18:30h: Programming project I (mandatory, on-site, PLM 103/4)
- 22.10.2026 - 01.12.2026: Learn the fundamentals of programming for marketing analytics II (online, asynchronous)
- 02.12.2026, 14:00-18:30h: Programming project II (mandatory, on-site, PLM 103/4)
- 03.12.2026 - 18.12.2026: Programming project III (online)
Registration:
The number of participants is limited to 65 students. To apply, please use the official module booking tool and submit a short statement of intent (maximum half a page) and your transcript of records. Places will be allocated on a first come first served basis.
Students who have taken either Python - A non-technical introduction with applications to Marketing (MO0076) or Introduction to R for Marketing Analytics (MO0207) are not permitted to book the current module.
Note:
This information supports the official information in the electronic university calendar (VVZ – Vorlesungsverzeichnis). In case of doubt, the official information at the VVZ is valid.
Data Camp
This class is supported by DataCamp, the most intuitive learning platform for data science and analytics. Learn any time, anywhere and become an expert in R, Python, SQL, and more. DataCamp’s learn-by-doing methodology combines short expert videos and hands-on-the-keyboard exercises to help learners retain knowledge. DataCamp offers 325+ courses by expert instructors on topics such as importing data, data visualization, and machine learning.
