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 Demissie Alemayehu, 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. PhD-level courses approved as electives for the MA in Statistics Program:
- STAT GR6201
- STAT GR6301
- STAT GR6701
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 via our online form (that will be sent via email to current students) 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.
GR6301 PROBABILITY THEORY I
Real analysis, undergrad probability, linear algebra, mathematical maturity (comfortable with proof-based courses)
GuR6201 THEORETICAL STATISTICS I
Real analysis, undergrad probability, and statistics, linear algebra, mathematical maturity (being able to carry out real-analysis/probability theory) proofs.
GR6701 Foundations of Graphical Models
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.
: Any further questions relating to the process should be directed to firstname.lastname@example.org