Background: This master’s degree program serves students who are interested in statistics and data sciences. Admission to the program requires a BA/BS in math or statistics; students from other disciplines could be admitted provided they have adequate undergraduate training in mathematics (multivariate calculus, linear algebra), computing (including familiarity with one or more programing languages, e.g., R, MATLAB, Python, …) and upper division probability and statistics (i.e., equivalent to math 151/152 at the Claremont Colleges).

Degree Requirements: A minimum of 32 units of graduate coursework is required, including four core courses (16 units) and two restricted electives (8 units), as outlined below. Students who lack the prerequisite undergraduate coursework may be asked to complete more than 32 units. At least 20 units of coursework must be gamma courses–that is, 300-level and above. A grade of B- or above must be earned in gamma courses.

Satisfactory Academic Progress: The University’s policy on satisfactory academic progress applies. Students who do not maintain a minimum overall grade point average of 3.0 in Mathematics courses are placed on academic probation. Students who remain on academic probation after taking an additional 8 units of Mathematics courses may be excluded from the program.

Advising: Students are assigned an academic advisor upon entering the program.

Outline:

• Requires 8 graduate math courses (32 credits), at least 5 of which (20 credits) must be at the 300-level or higher.
• Must include the following 4 core requirements:
  • Math 351 Time Series Analysis, or Math 453 Financial Time Series
  • Math 352 Nonparametric and Computational Statistics
  • Math 353 Asymptotic Methods in Statistics with Applications
  • Math 355 Linear Statistical Models
• 2 restricted electives from the following list (not all these courses are offered every year)
  • Math 256 Stochastic Processes
  • Math 354 Reliability Theory
  • Math 358 Mathematical Finance
  • Math 359 Simulation
  • Math 365 Computational Methods for Molecular Biology
  • Math 366 Data Mining
  • Math 387 Discrete Mathematical Modeling
  • Math 293-393 Mathematics Clinic
  • Math 451 Statistical Mechanics and Lattice Models
  • Math 452 Large-scale Inference
  • Math 454 Statistical Learning
  • Math 462 Bayesian Inference and Machine Learning
  • Math 466 Mathematical Foundations of Data-Intensive Algorithms
  • Math 389 Advanced Topics in Mathematics (if appropriate, with advisor’s approval)
• In lieu of one formal course, the student may take Math 398 Independent Study with a research advisor leading to a publication quality technical report in an area of statistical/data sciences.
• In lieu of one of the math courses, the student may also choose one graduate course from within other departments at CGU or at KGI in which statistics is extensively applied. The fields may include Economics, Finance, Community and Global Health, Information Science and Technology, Evaluation, Education, Psychology, etc. Typical courses include Biostatistics, Data Science/Analytics, Research Methods, Computational Biology, among others. Substitution of such a course for a math course requires the approval of the academic advisor and the instructor of the course.