MATHEMATICS Courses

MA–411G INTRODUCTION TO ABSTRACT ALGEBRA I
FALL ONLY/ALL YEARS

3 credit hours

This course offers a study of modern algebra with topics from group theory and ring theory. Prerequisites: Grades of C or better in both MA205, MA302, and MA341.

 

MA–412G INTRODUCTION TO ABSTRACT ALGEBRA II
SPRING ONLY/ALL YEARS

3 credit hours

This is the second course in a two-semester sequence of introductory courses in abstract algebra. Topics covered include field theory, Sylow theorems, introductory Galois theory, and some of advanced group theory, module and ring theory. Prerequisites: Grades of C or better in MA341 and MA411.

 

MA–431G TOPICS IN ADVANCED MATHEMATICS
SPRING ONLY/EVEN YEARS

3 credit hours

This course offers selected topics in advanced mathematics such as topology, mathematical induction, non-Euclidean geometries. With different subject matter may be repeated for credit. Prerequisite: Grades of C or above in MA205 and MA302.

 

MA–461G NUMERICAL ANALYSIS
FALL ONLY/ODD YEARS

3 credit hours

This course covers: root finding for non-linear equations, numerical integration, numerical methods for ordinary differential equations, interpolation theory, and approximation functions. The course makes use of numerical software libraries. Prerequisites: Grade of C or better in MA205 and MA302.

 

MA–500 INTRODUCTION TO R
FALL ONLY/ALL YEARS

1 credit hour

This course will help build an understanding of the basic syntax and structure of the R language for statistical analysis and graphics.

 

MA–501 DATA SCIENCE BRIDGE
SUMMER/ALL YEARS

3 credit hours

The bridge course will cover calculus, linear algebra and statistics topics for data science courses. It does not count towards MS in Data Science degree. Prerequisite: MA-203

 

MA–505 INTRODUCTION TO SAS
FALL ONLY/ALL YEARS

1 credit hour

This course introduces students to basic knowledge in programming, data management, and exploratory data analysis using SAS software. Topics covered include data import and export, data cleaning and validation, basic statistical analysis, and data visualization.

 

MA–510 PYTHON FOR DATA SCIENCE
SPRING ONLY/ALL YEARS

2 credit hours

In this course, students will learn the basic syntax and data structures of the Python language required for Data Science, and how to utilize Numpy and Pandas for data analysis and statistical processing. Additionally, students will learn basic SQL for manipulating data in databases.

 

MA–541 REGRESSION MODELS AND APPLICATIONS
FALL ONLY/EVEN YEARS

4 credit hours

This course includes: linear models, including t-tests, ANOVA, regression, and multiple regression. Residual analyses, transformations, goodness of fit, interaction and confounding. Introduction to generalized linear models: mixed, hierarchical and repeated measures. Binary regression, extensions to nominal and ordinal milticategory responses, count data, Poisson and negative binomial regression, log-linear models. Prerequisites: MA-341 and MA387, BI412 or BI507.

 

MA–551 INTRODUCTION TO PROBABILITY THEORY
FALL ONLY/EVEN YEARS

3 credit hours

This course covers probability spaces; combinatorial analysis; independence and conditional probability; discrete and continuous random variables including binomial, Poisson, exponential and normal distributions; expectations; joint, marginal and conditional distribution functions; moment generating functions; law of large numbers; central line theorems. Prerequisite: MA-205.

 

MA–552 INTRODUCTION TO MATHEMATICAL STATISTICS
SPRING ONLY/ODD YEARS

3 credit hours

This course covers the teaory and practical applications of the theory of sampling, statistical inference, including sufficiency, estimation, and testing. Topics include common statistical distributions, sampling, maximum likelihood and moment estimators, unbiased estimators, hypothesis testing, and Bayesian inference. Prerequisites: MA-551 and instructor's consent.

 

MA–564 MULTIVARIATE ANALYSIS
SPRING ONLY/ODD YEARS

3 credit hours

An introduction to multivariate statistical analysis, such as Multivariate ANOVA, Principal Component analysis, factor analysis, cluster analysis, discriminant analysis, possibly structural equation modeling (SEM). Prerequisites: MA-541 and instructor's consent.

 

MA–571 STATISTICAL RESEARCH AND CONSULTING
FALL/SPRING/ALL YEARS

1 - 3 credit hours

This course is designed to teach students the skills and techniques needed to conduct statistical research and provide statistical consulting services. Students will learn how to design studies, collect and analyze data, and communicate results effectively to clients. Through campus-wide consulting program, students will work with researchers from various disciplines providing recommendations for statistical methodologies appropriate for their research: analyzing client data, preparing written reports and manuscripts.

 

MA–581 MACHINE LEARNING FOR DATA SCIENCE
FALL ONLY/ODD YEARS

3 credit hours

This course focuses on the practical applications of machine learning techniques to real-world problems. Students will gain knowledge on how to apply and evaluate different machine learning algorithms, including linear models, k-means, support vector machines, decision trees, random forests, neural networks, and more. They will also learn how to analyze and manipulate real-world datatasets, design learning algorithms, train, and assess machine learning models. Prerequisite: MA-541.