Data Science in Quantitative Finance and Risk Management

*Taught Online* Have you started or are about to start your investment journey? Do you want to know more about terms like “recession” and “volatility,” and how they might affect your own bank account? Are you interested in mathematics and its application to human emotions? This course introduces the leading statistical models and methods which quantitative  researchers use to understand the ever-evolving markets and build insightful financial strategies, such as machine learning, risk profiling, and portfolio optimization . At first, students will learn about the theoretical and applied foundations of regression and classification designs for predicting market patterns. Next, students will gain exposure to proprietary metrics such as Expected Shortfall used to evaluate  drawdowns of both single and multi-asset portfolios. Lastly, they will experiment with portfolio allocation tactics by visualizing risk-to-reward graphs under various buying and selling conditions. These techniques can be applied to the U.S. and foreign asset classes, including equities, commodities, and cryptocurrencies. Students will form research teams and play a stock market game using the skills they learned throughout the course with the objective of experiencing  how professional Quants pitch asset strategies to their clients. All implementations will be done using Python.

See sample syllabus here.