Fundamentals of Financial Data Science
Certificate Course - Financial Engineering - FE.2.2.I26
| Date | Mar 1 - 5, 2027 |
| Duration | 5 days |
| Location | On campus - Karlsruhe |
| Language | English |
| ECTS | 4 |
| Cost | 2,660 € |
Course prerequisites - Completion of Certificate Program “Digital Financial Markets” is highly recommended.
Fundamentals
Understand statistical learning and time-series models, including AR and ARMA processes, stationarity, OLS and maximum-likelihood estimation.
Technology
Use Python to estimate statistical models, reconstruct innovations and implement one-step and multi-step forecasts.
Applications
Apply statistical models to economic and financial data and interpret results under uncertainty and noisy observations.
What You´ll Explore
- Explore the Data Generating Process (DGP) behind linear time-series models.
- Understand autoregressive (AR) and ARMA models and their stationary properties.
- Learn how parameters are estimated using Ordinary Least Squares (OLS) and Maximum Likelihood Estimation (MLE).
- Develop one-step and multi-step forecasts for expected values and variances.
- Analyze economic and financial datasets while accounting for uncertainty and noisy observations.
- Implement model estimation, innovation reconstruction and forecasting in Python.
Your Key Takeaways
- Understand the foundations of statistical learning and financial time-series modeling.
- Specify and analyze AR and ARMA models and their stationary properties.
- Apply OLS and maximum-likelihood methods for parameter estimation.
- Develop and evaluate one-step and multi-step forecasts.
- Analyze financial data and interpret model results under uncertainty.
- Implement statistical models and forecasting methods in Python.
Taught by Recognized Experts in Fundamentals of Financial Data Science
Benefit from the knowledge of leading specialists with extensive experience in research and industry. Their deep expertise guarantees a course of outstanding academic and practical quality.
Prof. Dr. Maxim Ulrich
Prof. Dr. Maxim Ulrich is Professor of Risk Management and Financial Economics at KIT and has built an international academic career, including serving as Tenure-Track Assistant Professor at Columbia Business School from 2008. His work focuses on quantitative finance, asset pricing, and financial machine learning, making him a recognized expert in the field.
He brings extensive experience from academia and practice, including fintech ventures and collaborations with institutions such as the ECB and Eurex.
Who Should Attend
This course is particularly beneficial for professionals in the following fields:
-
Data scientists and quantitative analysts
Professionals analyzing financial and economic data using statistical models, time-series methods, and Python. -
Financial engineers and risk analysts
Engineers and analysts working with quantitative models, financial time series, risk modeling, or data-driven valuation methods. -
Quantitative developers and FinTech professionals
Developers implementing statistical models, parameter estimation, forecasting methods, and data-driven financial applications. -
Investment analysts and portfolio managers
Professionals using quantitative methods and financial data to analyze market dynamics, assess uncertainty, and support investment decisions. -
Professionals working with financial forecasting
Analysts applying time-series models and statistical methods to forecast financial and economic developments and interpret results under uncertainty. -
Researchers and academics in quantitative finance
Researchers, PhD candidates, and academics focusing on financial data analysis, econometrics, statistical modeling, or financial time series.