The MA Program may be completed full-time or part-time.  Students in F-1 status must complete the program full-time* and in three semesters maximum

*Full-time schedule: 

  • Fall (Year One) – RU – required
  • Spring (Year One) – RU – required
  • Students may opt to take summer courses
  • Fall (Year Two) – ER – Students may opt to take a third semester, but not a fourth. 

Part-time students are required to:

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

There are evening and weekend courses/sections to accommodate students who work full-time.

MA Program Requirements

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

REQUIRED 3 CORE COURSES:

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

Most students take these three core classes in the first semester.  Students with a prior background in probability and inference should take the half-semester versions of Probability and Inference.  Core courses cannot be waived regardless of prior background.

In addition to the three core courses above, one capstone course is required.  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. 

CAPSTONE COURSE OPTIONS:

  • 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*

Electives

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

  • Three (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) electives, etc. 

*All these 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 and Introduction to Data Science – Most incoming students should take this in the first semester as a statistics elective. 
  • GR5241 Statistical Machine Learning (prerequisite:  GR5206) – This would count as another statistics elective.
  • GR5242 Advanced Machine Learning (prerequisite:  GR5241) – This can be taken as a Capstone Course. 
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