April 19, 2014

Master of Financial Engineering Program Opens Big Data Lab

Big Data Lab
MFE Executive Director Linda Kreitzman (center) with Big Data Lab Managers Lab Managers Aaron Nebres (l.) and Charles McCutchen (r.)

Recognizing the growing importance of Big Data, the Berkeley Master of Financial Engineering (MFE) Program has opened new lab space that will support MFE, MBA, and other Berkeley graduate students in strengthening data science skills.

The lab is one manifestation of plans by the MFE Program to increase learning and collaboration opportunities around what is known as “Big Data,” or large data sets beyond the ability of commonly used software tools to capture, organize, manage, and process within a useful amount of time.

“The future will require us to process zettabytes and yottabytes of data," says MFE Executive Director Linda Kreitzman. (1,000 zettabytes is equal to 250 trillion DVDs).

Kreitzman helped mark the opening of the renovated  lab, located in the Long Business Library in S270, at a ribbon-cutting ceremony Sept. 10. The high-tech laboratory gives students and faculty access to real-time financial data, professional research tools, and leading analytical software, all of which can be remotely accessed anytime and from anywhere in the world, keeping MFE students connected during internships and research projects. It is overseen by Lab Managers Aaron Nebres and Charles McCutchen who both bring industry expertise to the professors and students.

One new tool is OneTick, which allows the students access to the same large high-frequency data sets used by major financial institutions.  OneTick was made possible by a gift from OneMarketData through a university donation program.  Through OneTick NASDAQ TotalView-ITCH data (complete order book and trade data) as well as NYSE TaQ (trade and quote), data are currently available, and currency data and data from other asset classes will be available soon.

 "Access to this kind of data helps students to develop and optimize trading strategies, perform statistical research, and re-create market conditions for detailed study of specific events—all of which connects classroom lessons to actual securities and markets," says Kreitzman, who is assembling a Big Data steering committee composed of technology experts and financial executives and raising funds for lab equipment and new initiatives. 

The lab also offers new opportunities for students across Haas and Berkeley to collaborate on data science learning, she adds. In particular, the lab will immediately connect the Haas MFE advantage, namely access to financial market data and financial expertise, to the EECS students’ know-how of big data processing and machine learning.  It builds on  MFE's connection to EECS first created three year ago when EECS Professor Laurent El Ghaoui began teaching an Optimization Techniques course to MFE students.

“The vision for this lab is to make sure we give all students access to Big Data and the skills to use data science, which is changing the world of finance,” Kreitzman says.

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