Quant Guide 2020: Carnegie Mellon University

Pittsburgh and New York City, US

Posner Hall, Carnegie Mellon University
Photo: Carnegie Mellon University

Successful applicants to Carnegie Mellon University’s Master of Science in Computational Finance are presented with a unique choice: the programme is taught on two campuses, and candidates pick one of them. The campus in Pittsburgh, Pennsylvania is the larger of the two and the university’s main academic hub. The New York campus is a lot smaller, but has understandable appeal for fans of quantitative finance. Classes take place in both locations; if a particular lecture is happening at the Pittsburgh campus for a given week, it is livestreamed to New York and vice versa.

Adjunct professor of business Richard Bryant is the executive director of the programme, which sits at eighth place once again in this year’s Risk.net quant guide. Part of the reason for its high ranking is the healthy average salary of degree-holders. The figure has risen by several thousand dollars annually since 2016, to a high last year of $105,056, six months after graduation. The faculty is also broad: teaching staff are drawn from the department of statistics and data science, the Heinz college of information systems and public policy, the department of mathematical sciences and the Tepper School of Business.

The programme’s three terms – autumn, spring and autumn – are divided into six mini-semesters of seven weeks each. The mini-semesters, each designed with a specific quantitative finance career path in mind, function in a similar way to what some institutions call concentrations or tracks. The six topics are trading, financial modelling, quantitative portfolio management, risk management and data science.

“As petabytes of financial data have become readily available, data science and statistics are playing a more important role in quant finance,” says Bryant. The degree has an embedded data science programme and a new addition to the curriculum is a corporate-sponsored project course in machine learning.

View this institution’s entry in the 2019 guide

View other universities and a guide to the metrics tables

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to copy this content. Please contact info@risk.net to find out more.

Most read articles loading...

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here