Required Courses

HERE is information about requirements for graduation. 

There are

  • Three core courses
  • One capstone course
  • Six electives
    • Three (3-pt) electives must be from the Statistics Department.
    • Three (3-pt) electives from Statistics or Approved Electives list.

This will complete the ten course/30 credit minimum to graduate.  View this PAGE for more details on program requirements. 

There are 3 CORE REQUIRED COURSES for the M.A. Statistics Degree: 

   (1) GR5203:  Probability (3 points)

   (2) GR5204:  Inference (3 points)

GR5203 and GR5204 are usually taken sequentially in the first semester as intensive half-semester courses. 

   (3) GR5205: Linear Regression Models (3 points)

GR5205 is usually taken in the first semester along with probability and inference. 

CAPSTONE COURSE

All students must take at least one of the following two courses to graduate.   If both are taken, one will be counted as the capstone and the other as a statistics elective.   

  • GR5291 Advanced Data Analysis (3 points) – Taken in the second or last semester of the program. 
  • GR5242 Advanced Machine Learning (3 points) – GR5241 is the prerequisite for GR5242; GR5206 is the prerequisite for GR5241. Students are encouraged to take GR5245 Python for Deep Learning (1 point, Pass/Fail) as an accompanying course with GR5242. However, Python for Deep Learning is not required to be taken alongside Advanced Machine Learning.

For any course to count towards the MA Degree, it must be taken for a letter grade evaluation.  P/F or R credit is not acceptable to count for graduation.  Extra courses that do not count for graduation may be taken, as long as the minimum 3.0 GPA is maintained.