Data Science (Specific Topics in Marketing) (SoSe 2022)
Contact person: Stefanie Dewender, M.Sc.
This course takes place in both terms of the summer semester.
Please register at the examination office for the regular examination period.
Credit points: 6 ECTS (BWL PO 2019)
During the course, please communicate and stay updated via the course page on Learnweb. Announcements, lecture slides and any additional material will be published there.
The course is limited to a maximum of 50 participants. If more than 50 people want to attend the course, the course leaders will make a selection. Interested students have to send a current CV, a short letter of motivation and a transcript of records to Stefanie Dewender (email@example.com) by March 30 at the latest. In case students have not performed any examinations during their master studies so far, they are invited to send their bachelor transcript. Students for whom the course is mandatory will be preferred. We will let you know by April 8 if you have been admitted to the course.
Data Science has become a vital and highly relevant topic in the business world. Marketers in today’s data rich environment do not only need to bring strategic expertise, but also require the ability to derive information and insights from multiple streams of data within and outside of their own company.
With the richness of data, complexity has increased. Identifying relevant data sources, combining data streams, mastering data storage and data administration has become similarly important as analyzing and interpreting data with analytical and econometric tools. With the advent of data, new technologies and analytical procedures conquered the field of marketing. Big data applications together with artificial intelligence dominate todays’ business intelligence departments. While these new methods provide great opportunities, they similarly bring substantial challenges to marketers.
To speak the same language as business intelligence, marketers need to acquire profound knowledge about the tools and methods available in today’s analytical world which help them to better address common marketing problems such as e.g. customer management, return on invest measurement, budget allocations, product preferences or willingness to pay.
Investing into these skills clearly pays off. Recent research points out that business school graduates with analytical expertise and the ability to develop own analytical models in environments such as R or Python benefit from higher salaries early on in their career. A study from Northeastern University in Boston quantifies this benefit at a 25% higher salary mark.
For this reason, this module aims to ensure that you bring the right skills to the job market.
The course combines a practical focus with the analytical and methodological skills. We will provide you with access to the DataCamp learning platform, where you'll be able to build and train your coding skills. Within the first phase of the course, you will have the opportunity to learn the basics of R and R Studio with the help of an exclusively curated online education program. Once you have acquired the necessary base skills, you will move forward to your own business intelligence project.
Together with our practice partner Dr. Wolff Group we provide students with several up to date business cases from which you will have to choose one. Together in teams, you will have eight weeks to complete the task. You will benefit from our own mentoring program as well as from the close collaboration with Dr. Wolff’s own business intelligence team that will similarly provide you with detailed feedback.
At the end of the semester, you will then have the opportunity to present your findings directly to the board of directors during a workshop held at Dr. Wolff’s own business academy in Bielefeld including a tour of the company and networking opportunities to learn more about the importance and value of data driven marketing.
- Prof. Dr. Raoul Volker Kübler (responsible)
- Lina Marie Oechsner (accompanying)
- Stefanie Dewender (accompanying)