MA Statistics students who have demonstrated exceptional academic potential and  who plan to apply for a PhD program may submit applications to take selected PhD-level courses offered by the department. Applications will be reviewed by the Director of the MA program, Professor Gabriel Young, and faculty instructors of the PhD-level courses. Space will be limited, with a cap decided by the faculty instructors of the PhD-level courses. 
1. (A) PhD-level courses approved as electives for the MA in Statistics Program
  • STAT GR6201 – Theoretical Statistics I
  • STAT GR6301 – Probability Theory I
  • STAT GR6701
  • STAT GR6104 – Computational Statistics (MA students to email instructor directly to be approved to take the course)
  • STAT GR6202 – Statistical Inference Theory II
  • STAT GR8201 (Sec 001) – Theoretical Statistics

(B) PhD-Level course not approved as an elective for graduation in the MA Program. 

  • STAT GR6105 – Statistical Consulting (Taken for pass/fail only)
2.  Application Review Criteria: To be eligible, students must meet all course prerequisites, have strong preparation in mathematics and statistics, and be strongly motivated to pursue a PhD program in Statistics or related fields. The review process will be based on an initial thorough screening of academic records and a second round of interviews by the faculty instructors.
3.  Required Application Materials: Applicants must submit the following materials online:
  • Name and UNI
  • Name of Undergraduate School and Major
  • All undergraduate transcripts
  • Columbia (and other graduate school) transcripts, if applicable
  • A resume
  • Name of Columbia mentor or research advisor, if applicable
  • PhD class desired (only one per semester)
  • A short statement (at most 500 characters) describing why you would like to take a PhD course
4.  Application Deadline: Completed APPLICATION must be submitted during the following application periods:
Note:  Each student may apply only for one eligible PhD course per semester. 

5.  Prerequisites: Below are the prerequisites for three of the PhD courses. For other requirements, please see the corresponding course information in the Registrar listings.  

Real analysis, undergrad probability, linear algebra, mathematical maturity (comfortable with proof-based courses)
Real analysis, undergrad probability, and statistics, linear algebra, mathematical maturity (being able to carry out real-analysis/probability theory) proofs.
You know basic probability and statistics, calculus, and some optimization. You are comfortable writing software to analyze data, and are familiar with a good programming language for statistics and machine learning, such as R or Python.
Statistics MA students who are interested in GR6701 should attend the first day of class and sign up for the waitlist maintained by the instructor. Admitted students will be notified subsequently.
Note: Any further questions relating to the process should be directed to Professor Gabriel Young (