The course gives a broad introduction to programming in Python and computational thinking for students with degrees outside of computer science. Its overall aim is to equip business and economics students with a basic but solid toolkit for using code to understand, structure, and solve practical problems in their own field.
The first part of the course introduces the Python programming language and core programming concepts such as variables, data types, control flow, functions, and elementary data structures (lists, sets, dictionaries). Through short, guided exercises, students learn how to combine these building blocks into small programs that automate routine tasks and implement simple decision logic.
The second part focuses on computational thinking and systematic problem solving. We discuss how to decompose complex problems into smaller steps, how to design and structure algorithms, and how to work with different levels of abstraction. Particular emphasis is placed on writing modular and reusable code, documenting solutions, and using debugging techniques to find and fix errors, thereby increasing the robustness and reliability of programs.
The third part of the course demonstrates how Python and computational thinking can be applied to business economics. Students are introduced to selected Python libraries for data analysis, visualization, web scraping, and simple simulation models. They learn how to obtain data (including from the web), prepare and analyze it, and use simulation to explore possible outcomes in settings such as inventory.
Throughout the course, integrated exercises and small projects help students connect the technical material to realistic business questions.
Exam info and full course description can be found in the course catalogue.
Requirements for taking the exam
In order to participate in the exam, there is an 11 days attendance requirement
Course specific:
To apply for the course, you must have passed a Bachelor's degree in Business Economics, Business Administration, or an equivalent degree. It is also recommended that the participants have basic knowledge of math and statistics.
General:
Exchange students: nomination from your home university
Freemovers: documentation for English Language proficiency
You can read more about admission here.
nikolai.stein@uni-wuerzburg.de
Nikolai Stein has since 2020 been an academic advisor at the chair for business informatics and information management at the Julius-Maximilians-University of Würzburg. Prior to this he finished his doctorate "Advanced Analytics in Operations Management and Information Systems: Method and Applications" from the same university.
