Welcome to the Chair of Marketing

The exam review for the following courses will take place on Monday, October 28, 2024, from 9:00 AM to 9:55 AM sharp at FSR Ground Floor, Nauklerstr. 47:

• B320 Market Research
• DS320 Introduction to Data Science
• B421 eBusiness

Registration Details:

• Deadline: Registration is possible until Wednesday, October 23, 2024, at 23:59.
• How to Register: Please register for one of the available time slots following this link.

Our teaching program for the winter term is online, including seminars (e.g., B322a and B520) and DS500 Data Science Project. Please note that for some courses (primarily seminars) a registration is required, but for most of the lectures (e.g., B420, B420ex, B220) a registration is not necessary - just show up in class!

We are delighted to be able to offer a number of new courses taught by our new colleague Dr. Matthias Ritter. He will teach, e.g., a course on the Economics of Renewable Energy (B.Sc.) and Econometric Essentials for Applied Empirical Research (M.Sc.). The latter course is particularly useful for students who want to improve or refresh their econometrics skills from the B.Sc. Enrolling in this course will help students in mastering other M.Sc. courses that contain empirical elements.

Can Data Integration Improve Consumer Demand Planning in the Photovoltaic Industry?

• Forecasting customer demand is a critical business process in most industries. Forecasting solutions in business practice often focus on manual forecasting (“expert forecasts”) or timeseries forecasting. However, these approaches neglect the explanatory power of important external factors for customer demand and may thus lead to poor forecast accuracy in the volatile business environment of the PV industry.
• The goal of this work is to assess whether integrating internal and auxiliary data (e.g., historical data, seasonalities, expert forecasts, time series forecasts, customer demand signals, market data, price developments, Google trends, …) can be leveraged to build a feature-driven demand forecasting model and to improve the demand forecast accuracy at BayWa r.e. Solar Energy Systems.
• This thesis requires solid knowledge of and experience with R / Python as well as experience with Machine Learning.

Supervision: Prof. Dr. Dominik Papies with Dr. David Scheuermann (BayWa r.e. Solar Energy Systems)
On this page you will find all important information about the application process.