Master of Science in Statistics and Machine Learning

Claremont Graduate University (CGU)

In an increasingly data-driven world, the MS in Statistics and Machine Learning prepares you for a range of career possibilities in the statistical/data sciences and helps you stand out in the marketplace. The need for professionals trained in the mathematical techniques of data science is greater than ever. In this program, you’ll work closely with faculty who have extensive backgrounds in applied mathematics, data science/analytics, computational science, statistics, industrial modeling, and much more. In lieu of one formal course, students may conduct a semester’s worth of independent study with a research advisor that leads to publication of a quality technical report in an area of statistical/data sciences. CURRICULUM Core Courses: 4 courses (16 units)At least one core course must be selected from each category. Statistics Math 352 Nonparametric and Computational Statistics Math 353 Asymptotic Methods in Statistics with Applications Math 355 Linear Statistical Models Machine Learning Math 454 Statistical Learning Math 462 Mathematics of Machine Learning Math 364 Machine Learning for Asset Pricing Applied Statistics Math 359 Computational Statistics Math 466 Advanced Big Data Analysis Math 452 Large-scale Inference Math 366 Data Mining Restricted Electives: 2 electives (8 units)Two 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 (prerequisite for Math 252) Math 252 Statistical Theory (prerequisite for all 300+ level statistics courses) Math 256 Stochastic Processes 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) Unrestricted Electives: 2 electives (8 units) Any CGU Math course CGH 301 Biostatistics Any relevant course from other programs Independent StudyIn 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.

Institute of Mathematical Sciences