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Education / Graduate Education / PhD Programs / Statistics / Master of Arts in Statistics
 

Master of Arts in Statistics

Other Available Graduate Programs
PhD in Statistics (traditional program)
PhD in Statistics (bioinformatics concentration)
Master of Science in Biostatistics

Program Overview

The Master of Arts (MA) degree in Statistics requires satisfactory completion of at least 32 credits and a final comprehensive written examination; no thesis is required. Of the 32 credits, at least 24 must be in departmental courses primarily at the 400 level or above.

A typical program for the MA consists of PhD-level courses taken in Semesters 1 (three courses), 2 (three courses), and 3 (two courses); however, MA students have the option of completing the program in two semesters (four courses per semester). A balanced program is worked out with the student’s advisor. The final comprehensive examination is administered during the summer following completion of the first year of study.

Students must begin the MA program during the Fall semester.

Prerequisites

The requirements for entry into the MA program in Statistics are the same as those for entry into the Statistics PhD program. Entering MA students should have a strong background in mathematics, including three semesters of calculus (through multivariable calculus), a course in linear and/or matrix algebra, and a year of probability and mathematical statistics. A course in real analysis is encouraged; a course in statistical methods is also recommended.

Typical Program of Study

1.5 year program completion option:

First Fall Semester (12 credits)

  • Probability Theory (4 credits)
  • Statistical Inference I (4 credits)
  • Biostatistical Methods I (4 credits) or Introduction to Statistical Computing (3 credits) & Introduction to SAS (1 credit)

Spring Semester (12 credits)

  • Biostatistical Methods II (4 credits)
  • Linear Models (4 credits)
  • One elective:
    • Statistical Inference II (4 credits)
    • Bayesian Inference (4 credits)
    • Design of Clinical Trials (4 credits)

Second Fall Semester (8 credits)

  • Two electives:
    • High Dimensional Data Analysis (4 credits)
    • Generalized Linear Models (4 credits)
    • Introduction to Statistical Computing (3 credits) & Introduction to SAS (1 credit)

1 year program completion option:

Fall Semester (16 credits)

  • Probability Theory (4 credits)
  • Statistical Inference I (4 credits)
  • Biostatistical Methods I (4 credits)
  • Introduction to Statistical Computing (3 credits)
  • Introduction to SAS (1 credit)

Spring Semester (16 credits)

  • Biostatistical Methods II (4 credits)
  • Linear Models (4 credits)
  • Two electives
    • Statistical Inference II (4 credits)
    • Bayesian Inference (4 credits)
    • Design of Clinical Trials (4 credits)