Data Science in Quantitative Finance and Risk Management
Course Description
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, you will learn about the theoretical and applied foundations of regression and classification designs for predicting market patterns. Next, you will gain exposure to proprietary metrics (such as Expected Shortfall) used to evaluate drawdowns of both single and multi-asset portfolios. Lastly, you 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. You will form research teams and play a stock market game using the skills you 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.
Course Criteria
Python coding experience is required. Prior knowledge in statistics and calculus will be helpful, but is not required.
Academic Interest
Social Sciences (e.g., history, sociology, business)
Application Materials
A complete application includes a transcript, two short essays, a letter of recommendation, writing sample, application fee, and a submitted parent confirmation. If you are seeking need-based financial aid, you must indicate that in your application before it is submitted. Please refer to the Application Instructions for complete details.
Instructor(s)
John Lee
Cost
$4,790
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