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.
A part-time student must complete the program in no more than four years. Before choosing the part-time option, review the fall course schedule to see if the times will work for you. Our online courses tend to run during early morning hours and in-person virtual attendance depends upon the course instructor.
All online students must be present for quizzes and exams.
Required Core Courses
Students must complete four required core courses and six elective courses, with a total of ten minimum classes for the degree. Of the six electives, at least three must be selected from courses offered by the Statistics Department. The other electives may be chosen from a list of approved courses, depending on the student’s area of interest. Students should review this information with their Faculty Adviser. The four core courses are:
- GR5203: Probability (a 1/2 semester course worth 3 points) immediately followed by…
- GR5204 Inference (a 1/2 semester course worth 3 points).
GR5203 and GR5204 are required to be taken online in the first semester.
- GR5205: Linear Regression Models (3 points) – Required to be taken online in the first semester.
Students starting Fall 2016 only – Required “Capstone Courses” to be taken in the last semester of the program. Choose one only:
- GR5291: Advanced Data Analysis (3 points) OR
- GR5242 Advanced Machine Learning (3 points) – New Capstone Course. Please see “Data Science Sequence” below.
Students starting as of Fall 2017 -Required “Capstone Course” to be taken in the last semester of the program. GR5291 Advanced Data Analysis (3 points).
Please note: Core courses cannot be waived regardless of prior background.
In addition to the four core courses, students must also complete at least six electives approved by their Faculty Adviser. At least three electives must be selected from the Statistics Department, upon approval by the Faculty Adviser. Electives may be chosen based upon a student’s area of interest.
For a thorough grounding in data science, an incoming student is strongly advised to take W4300 (GR5206): Statistical Computing and Intro to Data Science (3 points) in the first semester online. A partial list of approved electives may be found here.
Data Science Sequence
- In order to qualify to take GR5242 Advanced Machine Learning, one must have taken the prerequisite: GR5241 Statistical Machine Learning.
- In order to qualify to take GR5241 Statistical Machine Learning, one must complete the prerequisite: GR5206 Statistical Computing.
Data Science Sequence:
GR5206 – Statistical Computing (recommended to take fall semester 1 to qualify for subsequent courses in the sequence)
GR5241 – Statistical Machine Learning
GR5242 – Advanced Machine Learning