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:
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 and in-person virtual attendance depends upon the course instructor.
- 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
All online students must be present (online) for quizzes and exams. Hybrid students, by program design, are remote during the fall semester and may not be on campus.
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, although some are worth four or more points.
Students must complete:
- Three required core courses,
- One capstone course, and
- Six elective courses.
Three of the six elective courses must be chosen from the offerings of the Statistics Department. The other electives may be chosen from a list of approved courses, depending on the student’s area of interest.
This makes a total of ten (minimum) graded classes for the degree. Courses taken for Pass/Fail or R credit do not count for graduation.
Students must meet with with their assigned Faculty Adviser for approval of their study plan each semester prior to course registration. It is the responsibility of the student to meet with his or her adviser.
During the semester, students struggling academically should contact their Faculty Adviser immediately (See Good Academic Standing). Students must confer with their adviser regarding any change in their course registration.
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)
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 required, although students are welcome to take both.
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*
In addition to the three core courses and one capstone course, students must also complete at least six graded electives approved by their Faculty Adviser. At least three electives must be selected from the Statistics Department.
A partial list of approved electives may be found here. Electives may be chosen based upon a student’s area of interest. Pass/Fail or R Credit is not acceptable for any course to be counted for graduation.
If both capstone courses are taken, then one would be counted as the capstone course and the other counted as one of the three required statistics electives.
*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.