The M.S. in Applied Math with the specialization in Computational Statistics for Data Science is designed to train students in the state-of-art methodology of computational statistics that is the core of data science. The program also exposes students to its applications to science, engineering, finance, social science, etc. This program offers diversified training and research directions within computational statistics, including Bayesian statistics, statistical learning, algebraic statistics, Monte Carlo methods, and network analysis.

Required to take all of the following three course options: 

• Math 540 Probability OR Math 475 Probability
• Math 563 Mathematical Statistics
• Math 564 Applied Statistics

Remaining Elective courses to be chosen in consultation with the academic advisor, or from the following list or any unused core/ required courses listed above:

• Math 483 Design and Analysis of Experiments
• Math 535 Optimization I
• Math 542 Stochastic Process OR Math 481 Intro to Stochastic Processes
• Math 546 OR Math 446 Introduction to Time Series
• Math 561 Algebraic and Geometric Methods in Statistics
• Math 565 Monte Carlo Methods in Finance
• Math 567 Advanced Design of Experiments
• Math 569 Statistical Learning
• Math 574 Bayesian Computational Statistics
• Math 578 Computational Mathematics II
• Math 590 Meshfree Methods
• CS 579 Online Social Network Analysis
• CS 583 Probabilistic Graphical Models
• CS 584 Machine Learning
• CS 585 Natural Language Processing
• BIOL 550 Bioinformatics and Biotechnology
• PHYS 440 Computational Physics
• ECE 566 Statistical Pattern Recognition

PLANS OF STUDY

Here we present a few plans of study for the various options in the MS program. Note that Math 593, a required course, is not listed below under the plans of study as it is a zero credit course offered every semester.

A Sample Course Sequence for the Specialization in `Computational Statistics for Data Science’ with Project: