M.A. in Statistics Hybrid Online/On-campus Program

The Department of Statistics offers its M.A. program in a partially online format, offering students greater flexibility in completing the M.A. Degree.  The first semester (only) of the hybrid program is offered online.  The remaining courses are completed on campus, during which time the hybrid students are completely integrated into the resident program.

  • The degree requirements and the actual diploma/M.A. Degree rewarded are identical for both the hybrid online/on-campus program and the regular on-campus program.

The full-time Hybrid Program must be completed within three semesters:

    • Fall (Year One – Four Online Courses) – RU
    • Spring (Year One – On Campus) – RU
    • Students may opt to take summer courses on campus.
    • Fall (Year Two – On Campus) – ER

Part-time domestic students:

    • Are required to take a minimum of two courses per semester and
    • Complete a minimum of two RUs (Residence Units)
    • Finish the program within four years of the first semester of registration. 

Before choosing the part-time option, review the fall course schedule. The online courses tend to run during early morning hours.  In-person virtual attendance depends upon the course instructor.  

All online students must be present (online) for quizzes and exams.

  • International students in F-1 status must attend full time and register for all four online courses in the first semester.**
    • GR5203 D04 Probability (half-semester)
    • GR5204 D04 Inference (half-semester)
    • GR5205 D05 Linear Regression Models
    • GR5206 D01 Statistical Computing & Introduction to Data Science

Hybrid students, by program design, are remote during the fall semester.  On-campus privileges, such as a Columbia ID Card and use of the libraries, will not be available until the student arrives on-campus in the second semester. 


The MA program requires completion of two RUs (Residence Units) and a minimum of 30 points of graded courses.  A typical course is worth three points. 

Students must complete:

  • Three required core courses,
  • One capstone course, and
  • Six elective (3-pt) courses:  A minimum of three (3-pt) courses must be from the Statistics Department.*

Students must receive a letter grade in any course that will count for graduation.  Courses taken for Pass/Fail or R credit may be taken, but will not count for graduation. 

It is the responsibility of the student to reach out to the assigned Faculty Adviser for approval of the courses to count for graduation.  It is recommended that each student send the Course Checklist to the Faculty Adviser each semester prior to course registration and update the adviser with changes later on. 

During the semester, students struggling academically should contact their Faculty Adviser immediately (See Good Academic Standing).

*Mentored Research (GR 5398) and Statistical Fieldwork (GR 5399) may be counted toward the 30 point minimum for graduation if they have a letter grade and are approved by the Faculty Adviser.

Required:  Three core courses plus one capstone course


  • GR5203: Probability (3 points)
  • GR5204: Inference (3 points)
  • GR5205: Linear Regression Models (3 points)

Full-time hybrid students take these three core classes in the first semester.  Probability and Inference are half-semester versions.  Core courses cannot be waived regardless of prior background.

In addition to the three core courses above, one capstone course is also required for graduation.  Students are welcome to take both capstone courses, if desired.   In that case, one would count as a capstone, the other as an approved elective. 


  • GR5291 Advanced Data Analysis (3 points) – To be taken in the second or last semester.
  • GR5242 Advanced Machine Learning (3 points) – GR5241 is the prerequisite for this course*


In addition to the three core courses and one capstone course, students must also complete the equivalent of at least six (3-pt) graded electives approved by their Faculty Adviser.  At least three (3-pt) electives must be selected from the Statistics Department. 

  • Three approved (1-pt) elective statistics courses can make up for one (3-pt) elective or
  • One (4-pt) elective can join with two (1-pt) electives to make up for two (3-pt) approved electives, etc.

To count for graduation, all courses must have a letter grade and be approved by the Faculty Adviser.  Electives may be chosen based upon a student’s area of interest. In order to count for graduation, an elective must be taken for a grade.  A partial list of approved electives may be found here

*Data Science Sequence

For a thorough grounding in data science that is specifically designed for the students in our Statistics MA Program, it is recommended to take this sequence:

  • GR5206:  Statistical Computing (online full-semester course worth 3 points) – Full-time hybrid students are required to take this course online in their first semester. 
  • GR5241:  Statistical Machine Learning (3 points) – prerequisite is GR5206.
  • GR5242:  Advanced Machine Learning (3 points) – prerequisite is GR5241 – optional capstone course. 

Each course must be successfully completed before taking the next in the series.

**If you are an international student currently in the US in F-1 status, please consult with ISSO