Teaching: Data-Driven Thinking in Financial Economics and Machine Learning
In our hands-on teaching projects, we prepare students to model and understand financial data from both a theoretical and practical perspective. All modules at the Bachelor and Master level combine financial intuition with computational and mathematical thinking—drawing on tools from statistics, computer science, and machine learning. This approach reflects the core of the KABFI philosophy: integrating economic reasoning with modern data science and machine learning. As one former master student noted after receiving a job offer from Morgan Stanley in New York, “Your classes truly prepared me for the interviews… The way you taught topics like factor models, stochastic discount factor, and machine learning gave me the confidence to speak about them.”
ADDITIONAL BACHELOR TEAM-RESEARCH MODULE STARTING WINTER 2025
Research Project in Financial Data Science and Machine Learning (Bachelor, Vertiefungsstudium, 9 ECTS)
(Working Title – Final Name TBD)
Starting in Winter 2025, we will offer a new 9 ECTS research module for advanced Bachelor students interested in supervised, team-based research at the intersection of machine learning, financial data science, and economic reasoning. Over the course of a full semester, small groups of 3–6 students will work collaboratively on real-world data problems that are relevant to both industry and academia. Projects often draw on financial market data but the methods and experience are fully transferable to other domains of data science and machine learning.
Participation requires prior completion of the Financial Data Science Bachelor module. Students will be supported by KABFI Ph.D. researchers acting as agile project supervisors and contributors, and some projects will be co-supervised by faculty from partner universities across Europe. Collaboration with computer science departments at other German universities is currently being planned and may allow for joint project teams—combining our computer science-oriented engineering students with external students from informatics backgrounds.
This module is ideal preparation for students interested in Bachelor thesis supervision, fast-track Ph.D. entry via KABFI, or careers in data-intensive fields within finance and beyond.
NEW BACHELOR Lecture-based MODULE STARTING SUMMER 2025
Financial Data Science (Bachelor, Vertiefungsstudium, 9 ECTS)
This course introduces students to modern, data-driven financial market analysis—combining financial thinking with Python-based applications and machine learning tools. It is designed for students in computer science, industrial engineering, and related fields (e.g., W-Info, W-Math).
Grading is based on group-based Python tasks (typically 3 students per team), with a focus on real-world financial data challenges.
The course provides the foundation for all further work in financial data science—whether in the new BSc Research Project module (see above), a Bachelor thesis, Master research, industry careers, or the KABFI Ph.D. program.
Students consistently report the course’s relevance for interviews and job preparation. One student, after joining Morgan Stanley in New York, wrote:
“Your classes truly prepared me for the interviews… The way you taught topics like factor models, stochastic discount factor, and machine learning gave me the confidence to speak about them.”
Another graduate, now at Barclays in London, emphasized how much it mattered to connect technical methods with economic thinking:
“The interviews were like your lecture notes… The ability to link math and intuition made the difference.”
For full course details, visit: risk.fbv.kit.edu/c-ram/2297.php