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).

Admission. Admission requirements are detailed in the Admissions section of the current Policies and General Information Bulletin.

University Policies. Policies detailed in the current Policies and General Information Bulletin apply.

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 (normally 300-level or higher). A grade of B- or above must be earned in gamma courses.

• Must include 4 of the following core courses
  • Math 351 Time Series Analysis
  • Math 352 Nonparametric Statistics
  • Math 353 Asymptotic Methods in Statistics with Applications
  • Math 355 Linear Statistical Models
  • Math 359 Computational Statistics
  • Math 454 Statistical Learning
  • Math 462 Mathematics of Machine Learning
  • Math 466 Advanced Big Data Analysis
• 2 restricted electives can be chosen from either the list of core courses, or the following list (not all these courses are offered every year)
  • Math 256 Stochastic Processes
  • Math 293-393 Mathematics Clinic
  • Math 306 Optimization
  • Math 354 Reliability Theory
  • Math 358 Mathematical Finance
  • Math 365 Statistical Methods in Molecular Biology
  • Math 366 Data Mining
  • Math 387 Discrete Mathematical Modeling
  • Math 451 Statistical Mechanics and Lattice Models
  • Math 452 Large-scale Inference
  • Math 389 Advanced Topics in Mathematics (if appropriate, with advisor’s approval)
• In lieu of one formal course, students may take Math 398 Independent Study with a research advisor leading to a publication quality technical report in an area of statistical/data sciences.
• Subject to approval by their academic advisor, students working outside campus on mathematical/statistical projects may also use this professional experience as the basis of a Math 398 Independent Study. At most 2 units per semester can be acquired in this practical type of independent study, which will not be counted as a gamma course.
• In lieu of one of the math courses, students may 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.

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 dismissed from the program.

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