Whatever your intended career path in finance, this course will help you get the job you want. Big data and data science in general are areas of explosive growth in our economy. The global FinTech market is predicted to reach a capitalization of nearly $200 billion in a few years. Jobs in finance are increasingly requiring high-tech skills, and you will gain them in this course.
What will you learn?
- How to implement machine learning
- How to program in Python
- How to obtain financial data from the web (both manually and using bots)
- How to use statistical packages of your choice, including SAS, R, etc.
- How to build predictive models
- How to plot and understand trends
- How to evaluate risk and value-at-risk
- How to perform event study analysis (e.g., what is the likely price impact if a firm’s stock is downgraded?)
- How to conduct modern asset pricing, including understanding risk/return tradeoffs in portfolio management
It is essential to learn these skills for a wide range of career paths, including financial analyst, risk manager, portfolio manager, and many others. You will be equipped to work at institutions including start-up enterprises, big-five banks, accounting firms, consulting firms, investment banks, private equity firms, hedge funds, and many others.
Your deliverables will include brief weekly submissions that take only a few minutes to complete, two term tests, and a few group assignments. During live class, you will have enough time to complete most aspects of the group assignments. (The exception is the final write-up of the report which you’ll need to complete outside of class time.)
Professor Kamstra is an exceptional teacher, and accomplished researcher, and a widely quoted expert in the international press. He routinely achieves course evaluations above 6.5 (out of 7). The aspects of his teaching style mentioned most by his students include his ability to empathize with his students, his ability to explain technical material in a way that is easy to understand, and his passion for data science. Quantitative analysis can look hard, but if you follow Professor Kamstra’s clear step-by-step guidance through the course, not only will the material seem much easier than you expect, but you will excel.