The 4+1 BA/MA Accelerated Pathway Program is available only to current Columbia Undergraduates (Columbia College, the School of Engineering and Applied Science, the School of General Studies, and Barnard College). This option allows students to complete both the bachelor’s degree and the master’s degree (BA/MA) in a shorter period of time, an option that is financially advantageous.
Applicants interested in the 4+1 BA/MA Accelerated Pathway Program should apply at least two months prior to the semester in which they intend to begin taking courses that will count towards the MA Degree. Students must consult the Director of Undergraduate Studies. This consultation is a required part of the application process and must take place before the application is submitted.
For application instructions and details visit our Application Procedures. The GRE is not required for BA/MA applications.
- Applicants who wish to begin taking courses for graduate credit during the Spring 2026 semester must submit the 4+1 BA/MA application by November 12, 2025.
- Applicants who wish to begin taking courses for graduate credit during the Fall 2026 semester must submit the 4+1 BA/MA application by April 24, 2026.
Qualifying students apply to the program during their junior year or the first semester of their senior year. In the graduate portion of the Program, admitted students can be hired as Research Assistants or Graders in their first semester. We also offer a small number of BA/MA summer bridge research internships. The Program is ideal for students whose career goals will be furthered by graduate-level training and who are interested in pursuing a PhD in statistics or another field.
Upon enrollment in GSAS, BA/MA students may be granted up to four courses (12-15 credits) of transfer credit and up to 1 Residence Unit (RU) for graduate courses (4000-level and above). Students transferring fewer than four courses will be granted a partial RU based on the number of courses.
For questions, please contact the Director of Undergraduate Studies.
Alumni Spotlight
Eric Feder
Director, Data Science 2U
Graduation: 2013
Current Title: Director, Data Science, 2U
Career Highlights:
- Got to live out one of my dreams and work for a Major League Baseball team when I interned for the New York Yankees as statistical analyst for a year.
- While working at TheLadders, I had the privilege of mentoring a summer intern. I felt tremendous satisfaction from seeing him learn and grow, and loved passing some of my knowledge and experience on to him.
- After working with product managers and software engineers on a new website feature for many months, it felt great to finally roll it out to our users and to see how much it improved their experience.
“At Columbia, I learned both the theories and the practical skills that I needed to find a great job and start contributing immediately, and which I continue to use me to this day.”
Jacob Ma Weaver
Jacob Ma-Weaver
Graduated: 2009
Role: Portfolio Manager, Cable Car Capital LLC
Jacob completed the MA program along with a BA in Comparative Literature & Society from the College and was a Teaching Assistant in the department.
- After graduation, Jacob worked in financial analyst roles at McKinsey and two fundamentally-oriented investment firms, where he used econometric modeling and time series analysis techniques in several research projects.
- In 2013, he founded Cable Car Capital LLC, an investment adviser that manages a long/short equity hedge fund.
- While some investment strategies require applied statistical analysis, Jacob notes that his research is now more qualitative.
"Coursework on quantitative methods in finance helped narrow my investment-related career interests. While I decided against becoming a quant, the MA program gave me a great foundation for thinking about the world in probabilistic terms, which is core to my role as a portfolio manager."
Chris Mulligan
Chris Mulligan
Chris Mulligan, Dual Degree BA/MA in Statistics, 2015
Role: Quantitative Researcher, Two Sigma Investments LP
- Current duties: build statistical models to make financial market predictions using highly non-traditional data.
"The Statistics department provided a grounding in the key areas of statistics, giving me a set of tools I've been able to directly leverage professionally. More importantly, the department taught me how to learn and understand statistical problems, so that when I face novel problems in the future I can figure out how to solve them."