The Department of Mathematics jointly with Department of Statistics at Columbia University offers a track of its Master of Arts in Mathematics with specialization in the Mathematics of Finance.

The Department of Statistics offers free statistical consulting to the Columbia community. We help on the design, analysis, and interpretation of studies.

The Quantitative Methods in the Social Sciences program trains students to apply quantitative methods to social problems as they arise in business, government, and nonprofit organizations, and provides a strong foundation for those who go on to doctoral programs in the social sciences.

The MA in Statistics program prepares students for careers in finance, healthcare analytics, environmental science, and other data-intensive fields.

The Statistics major is an appropriate background for graduate work, including doctoral studies in statistics, social science, public health, genetics, health policy, epidemiology, marketing, opinion polling, economics, finance and banking, government, drug development, and insurance.

The Ph.D. program prepares students for research careers in probability and statistics in academia and industry.

Statistics is the art and science of study design and data analysis. Probability theory is the mathematical foundation for the study of statistical methods and for the modeling of random phenomena.


Congratulations to Department of Statistics Faculty member Professor Tian Zheng on being honored as an ASA Fellow "For creating novel statistical methodology in statistical genetics, bioinformatics and computational biology, and social network theory, especially as related to the measuring homophily and to surveying hard to reach populations, and for being a role model for doctoral students."
The Department of Statistics is offering the following Lecture Courses during Summer 2014.   Topics in Statistics (*limited space available) taught by Michael I. Jordan.  Topics in Probability taught by Mathieu Rosenbaum.  Topics in Statistics taught by Michael Stein.   Click "Read More" to see the Department Calendar for schedule and course details.
Implementing Reproducible Research Editor(s): Victoria Stodden, Friedrich Leisch, Roger D. Pen: In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Click "Read More" for further information.