(Effective Fall 2019)
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.”
: Any further questions relating to the process should be directed to email@example.com