In our hands-on teaching projects, we prepare students to model and understand financial data. All modules on the Bachelors and Masters level teach a combination of intuition, engineering tools and statistical modeling tricks.

Announcements for New Teaching Offerings Starting Summer 2025:

FINANCIAL DATA SCIENCE (for Bachelor students; Vertiefungsstudium)

FINANCIAL DATA SCIENCE IS BACK! After the flipped online class room experience of the Covid period (35 industrial engineering students and 35 computer science students of semesters 3-7), the course got restructured to a classical in-class lecture. Better than ever. More modern than ever; with a unique mix of MBA intuition and Engineering skills. During the restructuring, the focus was on creating an exciting and useful course for both, computer science and industrial engineering students (and their derivatives: W-inf, W-math, etc). You can earn yourself a free lunch if you show us another Bachelor course in any other university world-wide that covers such useful financial data science material :-)

This course introduces students to modern data-driven financial market analysis and equip them with a broad set of machine learning techniques for capital markets. The course begins with practical MBA-style case studies that the professor had developed during his MBA and Exec-MBA teaching at Columbia Business School in New York City. These Case Studies cover fundamental financial thinking: valuation, portfolio management and risk premia. The goal of this first part is to ensure all participants—regardless of prior knowledge—establish a strong foundational understanding of financial economics.  In parallel, students develop during lecture time essential Python programming and data handling skills (e.g., with Pandas, Statsmodels, and scikit-learn), which course participants apply to valuation, portfolio management (constrained optimization) and risk premia estimation (least squares methods).  Building on these fundamentals, the course takes a tangent to teach how to work with neural networks and other machine learning tools to forecast and trade in equity and option markets. Along the way, financial concepts are talked about such as analytical and numerical valuation and hedging of options, and modern portfolio construction techniques, including robust optimization and end-to-end reinforcement learning. Most of the financial concepts are paired in parallel with running Python code. Towards the end of the course, students get introduced to current high impact research papers in the field of financial data science and financial machine learning. This provides a good opportunity to understand aspects of the current research frontier, which is helpful for the Bachelor thesis, summer internships and jobs in the well paid financial industry.

Link to article on salaries in financial data science: https://www.linkedin.com/pulse/hedge-funds-paying-cool-15-million-sonali-basak?utm_source=share&utm_medium=guest_desktop&utm_campaign=co

Link to article on machine learning and investing: https://edition.cnn.com/2019/02/17/investing/artificial-intelligence-investors-machine-learning/index.html