This master’s degree program serves students who are interested in statistics and machine learning. 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 probability/statistics.

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.

Coursework

Must include 4 core courses with at least one from each category:

• Statistics
  • Math 352 Nonparametric Statistics
  • Math 353 Asymptotic Methods in Statistics with Applications
  • Math 355 Linear Statistical Models
• Machine Learning
  • Math 364 Machine Learning for Asset Pricing
  • Math 454 Statistical Learning
  • Math 462 Mathematics of Machine Learning
• Applied Statistics
  • Math 359 Computational Statistics
  • Math 366 Data Mining
  • Math 452 Large-Scale Inference
  • 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 251 Probability (rerequisite for Math 252)
• Math 252 Statistical Theory (prerequisite for all 300+ level statistics courses)
• Math 256 Stochastic Processes
• Math 262 Methods of Applied Probability and Statistics
• Math 293-393 Mathematics Clinic
• Math 306 Optimization
• Math 351 Time Series Data Analysis
• Math 365 Statistical Methods in Molecular Biology
• Math 389 Advanced Topics in Mathematics (if appropriate, with advisor’s approval)

2 unrestricted electives

• Any CGU Mathematics course
• CGU 301 Biostatistics
• Any relevant course from other programs (see below)
  • 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.
  • Subject to approval by their academic advisor, students may choose as unrestricted electives one or two graduate course(s) from within other departments at CGU or at KGI in which statistics and/or machine learning are extensively applied. The fields may include Economics, Finance, Community and Global Health, Information Science and Technology, Evaluation, Education, Psychology, etc.

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.