Why Big Data Cannot Be Ignored in MFE Type Program Curricula


Big Data skillsets (Python, R, Data Visualization, machine learning, etc…) are skillsets that are in demand on Wall Street and in almost all sectors of the economy. In finance, big data is used in retail banking, namely to analyze the behavior of customers in order to target products, anticipate default, detect fraud, etc. In investment banking there are also applications, and HFT is only one of them. With the computing power of the banks’ systems, the banks can now plan to develop a comprehensive Monte-Carlo system that captures the correlated risk drivers that affect the banks’ risk exposures. This infrastructure would be shared by all of the departments in the bank: risk management, which produces the risk measures (VaR, stress testing, etc.) and front offices, which price deals, calculate CVA, DVA, and other measures and manage collateral and liquidity, among other things. This infrastructure will not only generate a huge amount of data that needs to be structured and updated on a regular basis, but also a need to figure out how many different users can access this data independently of each other, a difficult technical problem to resolve. The future MFE applicant and student, as well as the future MBA student, must start preparing himself or herself better for the finance field, since jobs have started to require big data skillsets. In the next 10 years, the need for a data science skillset is going to increase exponentially. As usual, Haas is uniquely qualified and prepared to respond to the industry’s needs. We will announce very soon what we are doing since we anticipated these needs a few years ago.

About The Author

As Assistant Dean and Executive Director of the Berkeley MFE Program, my role-among other things-is to develop and maintain contacts with firms to find opportunities for the students and place them in outstanding internships and full-time positions.