Zum Inhalt
Fakultät Wirtschaftswissenschaften

Data and AI in Economics

Course contents

This course is designed to introduce students to the intersection of data science, artificial intelligence (AI), and economics. It aims to equip students with the necessary skills to apply AI and data analysis techniques to economic problems. The course will cover topics such as programming for data analysis, machine learning techniques, AI applications in economics, and ethical considerations in AI and data science.


Upon completion of this course, students will develop the following capabilities: They will gain an understanding of the significance of data and artificial intelligence (AI) within the realm of economics, along with their various potential applications. Students will acquire proficiency in utilising programming and computational tools to analyse data within economic contexts effectively. They will become familiar with and adept at employing machine learning techniques specifically tailored for analysing economic data. Furthermore, students will critically examine the ethical considerations associated with the utilisation of AI and data science within the field of economics. Practical sessions throughout the course will be conducted using Python, which is considered the industry-standard programming language.




Registration and course materials

Since physical attendance is limited by the size of the computer lab, students must register in advance. To do so, please register for the "Data and AI in Economics" Moodle course (https://moodle.tu-dortmund.de/course/view.php?id=45005) and participate in the query regarding PC Pool places. The Moodle course will also provide you with the relevant material week by week.







Module: Finance I
Course: Data and AI in Economics
Stage of study: 2./3. semester
Lecturer: Prof. Dr. Peter N. Posch
Credits: 4 SWS / 7,5 Credits
Type: Lecture and Exercise
Language: English
Type of examintation: Exam or graded presentation based on written case study’s expose. 
When taught: Summer semester (as of 2023)