• You have successfully added MATH 4341 to Course list.
  • You have successfully added MATH 3337 to Course list.

Mathematics

Department Chair: Dr. Jack Follis, follisj@stthom.edu

Mathematics is one of the most permanent and universal of the liberal arts and sciences. The courses offered by the department recognize mathematics as the universal tool for the life, natural, and social sciences. The program’s core consists of topics chosen to ensure students understand and appreciate the nature of mathematical thought and the role abstraction and logic play in it.

Computer science is one of the most dynamic and integral of the modern sciences. The courses offered by the department recognize computer science as a universal tool for innovation in various fields, including life, natural, and social sciences. The program’s core consists of topics chosen to ensure students understand and appreciate the nature of computational thought and the role algorithms and programming play in it.

The Distinguished Student of Mathematics Scholarship Endowment Fund and the Dr. William A. and Margaret Reddie Endowed Scholarship in Mathematics provide financial assistance to majors in mathematics.

Degrees and Certificates

Classes

COMSC 1450: Introduction to Programming and Computer Science

Students will learn to analyze computational problems and develop solutions to them as algorithms. The algorithms will be implemented in Python, a modern programming language. Students will learn the fundamental principles of computer science, basic hardware and software components of a computer system, computational thinking, basic algorithms, and programming. Students will get hands-on experience in problem solving by designing, writing, testing and debugging Python programs.
Add to Comparison

COMSC 1451: Object Oriented Programming

Software is everywhere, including enterprise systems, mobile devices, avionics, sensors, and big data. This course focuses on Object Oriented Programming (Java) and its key concepts: object, classes, encapsulation, abstraction, polymorphism, and inheritance. In addition, topics such as generics, interfaces, threads and events/listeners complement the software development process.
Add to Comparison

COMSC 2351: Data Structures

Continuation of COMSC 1351: Introduction to abstract data types, records, linked lists, stacks, queues and trees and graphs; recursion; analysis of algorithms; additional sorting and searching techniques.
Add to Comparison

COMSC 3371: Introduction to Data Analytics

Data analytics is a process that turns data into usable information for answering questions. This course will introduce the process of acquiring, managing and analyzing data. Readily available real-world data sets will be analyzed using supervised and unsupervised learning methods.
Add to Comparison

COMSC 3372: Data Visualization

Appropriate visualizations of data are a key to revealing patterns and communicating important findings in research. This course will build on statistical and analytical thinking by emphasizing the role and use of visualizations in the analysis of data. Theories, techniques and software for managing, exploring, analyzing, displaying and communicating information about various types of data will be introduced. Visualizations will be produced using readily available real-world data sets.
Add to Comparison

COMSC 3375: Database Systems

Organization concepts and terminology of data models and the underlying data structures needed to support them. Thorough presentation of the relational database management system including an introduction to SQL programming, normalization and database design. Introduction to the programming interface to databases.
Add to Comparison

COMSC 4191: Internship

Practicum of on–the–job experience under the guidance of a practicing specialist in the field. This course is designed to provide opportunities for students to enhance their practical skills through application of classroom concepts and theories to real life situations. To be supervised individually by a department faculty member with the approval of the department chair.
Add to Comparison

COMSC 4340: Computer Networks

An introduction to the design and analysis of computer communication networks. Topics included application layer protocols, Internet protocols, network interfaces, local and wide area networks, wireless networks, bridging and routing, and current topics. Prerequisites: COMSC 1351
Add to Comparison

COMSC 4345: Foundations of Data Science

Data science is an emerging discipline whose main goal is extracting information and knowledge from datasets and using it for decision-making, answering questions, or understanding phenomena. The fundamentals of Data Science will be studied from three perspectives; 1) as a collection of disciplines: exploring the interconnections between computing, mathematics, statistics, visualization, and other domains; 2) as a process: learning the life cycle in a data science project; and 3) understanding its computational foundation. This course also addresses the potential negative impact algorithms can have on people and society.
Add to Comparison

COMSC 4391: Internship

Practicum of on–the–job experience under the guidance of a practicing specialist in the field. This course is designed to provide opportunities for students to enhance their practical skills through application of classroom concepts and theories to real life situations. To be supervised individually by a department faculty member with the approval of the department chair.
Add to Comparison

MATH 1324: Math for Business & Social Sciences

The application of common algebraic functions, including polynomial, exponential, logarithmic, and rational, to problems in business, economics, and the social sciences are addressed. The applications include mathematics of finance, including simple and compound interest and annuities; systems of linear equations; matrices; linear programming; and probability, including expected value.
Add to Comparison

MATH 1325: The Nature of Mathematics

This course is an exploration of great ideas of mathematics. The course describes the nature of mathematics and provides insights into various strategies used by mathematicians in solving problems. The course emphasizes creative and effective thinking through an introductory examination of a wide variety of topics such as number theory, geometry, infinity, topology, chaos and fractals, and decision making. Prerequisite: Acceptance in the Mendenhall Summer Institute. Co-Requisite: ENGL 1311.
Add to Comparison

MATH 1331: Pre-Cal Algebra & Trigonometry

An integrated review course in pre–calculus algebra and trigonometry covering function concepts and symbols, rectangular coordinates, linear and quadratic functions, polynomial and rational functions, trigonometric functions, inequalities, systems of equations, complex numbers and analytic geometry.
Add to Comparison

MATH 1351: Finite Mathematics

Topics from contemporary mathematics, their development, applications and role in society. Some typical topics, to be chosen by the instructor, include graph theory, mathematical finance, critical path analysis, statistical inference, coding, game theory and symmetry. Applications are in the management, natural and social sciences.
Add to Comparison

MATH 1355: Fundamentals of Statistics

An introduction to statistical reasoning focused on data collection, descriptive statistics, exploratory data analysis and simple linear regression. Other topics will include basic probability, normal distributions and fundamentals of hypothesis testing. This course is open only to students in the AAS-GENB program.
Add to Comparison

MATH 1425: Success Through Enhancement of Mathematical Skills (STEMS)

Topics covered will include numerical evaluation of logarithms and the use of methods for working with them, the interpretation and manipulation of numbers in scientific notation; trigonometric functions; the quantitative interpretation and generation of graphs; the evaluation of ratios of numbers with integer exponents; the simplification of rational expressions, and the use of percentages; estimation methods without the use of calculators; dimensional analysis; trigonometric functions. The course emphasizes creative and effective problem solving techniques in a real world context and an enhancement of mathematical skills leading to greater achievement in STEM (Science, Engineering, Technology and Mathematics) courses. Prerequisite: Acceptance in the Mendenhall Summer Institute. Co-requisite: UNIV 1201.
Add to Comparison

MATH 1430: Pre-Calculus Algebra & Trigonometry

An integrated review course in pre–calculus algebra and trigonometry covering function concepts and symbols, rectangular coordinates, linear and quadratic functions, polynomial and rational functions, trigonometric functions, inequalities, systems of equations, complex numbers and analytic geometry.
Add to Comparison

MATH 2330: Introduction to Statistics for Nursing Research

Provides students with the methods and logic to perform elementary statistical analysis used in clinical research including: descriptive meansures, probability, sampling, normal distribution, Student t and Chi squared distributions, estimation and hypothesis testing, analysis of variance, regression and correlation.
Add to Comparison

MATH 2370: Introductory Statistics for Nursing Research

Provides students with the methods and logic to perform elementary statistical analysis used in clinical research including: Descriptive measures, probability, sampling, normal distribution, Student t and Chi squared distributions, estimation and hypothesis testing, analysis of variance, regression and correlation.
Add to Comparison

MATH 2431: Calculus III

Vectors and the geometry of space, vector functions, partial derivatives, multiple integrals, Green’s Theorem, curl and divergence, Stokes’ Theorem, The Divergence Theorem.
Add to Comparison

MATH 2435: Introduction to Statistical and Quantitative Analysis

An introduction to quantitative and statistical analyses focusing on applications of algebraic and statistical methods. Topics to be covered include functions and graphs, break-even analysis, descriptive statistics, probability distributions, estimation, simple linear regression and basic hypothesis testing will be covered. This course may not be used as part of the mathematics courses required of mathematics majors.
Add to Comparison

MATH 2437: Contemporary Mathematics for Elementary Teachers

A modern approach to the mathematics commonly taught to young children. Theoretic bases for computation and measurement are established. The geometric properties of shapes and solids are explored both visually and through computations. The basics of theoretical and empirical probability are developed using models and manipulatives. Additional topics such as problem solving algorithms, elementary logic and statistics are introduced. Prerequisites: successful completion of a University core mathematics course. Does not satisfy the University core requirements in mathematics.
Add to Comparison

MATH 3332: Elementary Statistical Methods for Economics & Business

Basic concepts of statistics with emphasis on statistical inference. Sampling and experimentation, descriptive statistics, confidence intervals, probability, two–sample hypothesis tests for means and proportions, Chi–squared tests, linear and multiple regressions, analysis of variance. Not open to students with credit in MATH 2434 or 3430. This course may not be used as part of the upper–division mathematics courses required of mathematics majors.
Add to Comparison

MATH 3335: A First Course in Probability

An elementary introduction to the mathematical theory of probability for students of mathematics, engineering and the sciences (including the social sciences and management science). Topics include combinatorial analysis, axioms of probability, conditional probability and independence, and random variables. Prerequisite: MATH 1432.
Add to Comparison

MATH 3343: Differential Equations II

Continuation of Math 2343. Power series solutions of differential equations and Bessel functions, Fourier series and transforms, matrices, systems of differential equations, introduction to generalized functions. Recommended for students interested in applications of mathematics and engineering.
Add to Comparison

MATH 3346: History of Mathematics

Evolution of mathematics from earliest to modern times. Major tends in mathematical thought, the interplay of mathematical and technological innovations, and the contributions of great mathematicians. Appropriate for prospective and in–service teachers.
Add to Comparison

MATH 3360: Discrete Mathematics

Analysis topics chosen at the discretion of the instructor from logic, set theory, combinatorics, and graph theory. Methods of enumerative combinatorics: sum, product, and division rules, bijective and recursive techniques, inclusion and exclusion, generating functions, and the finite difference calculus. Advanced topics to be selected from the theory of partitions, Polya theory, designs, and codes, graphs and trees with applications including games of complete information. Combinatorial existence theorems, Ramsey’s theorem.
Add to Comparison

MATH 3371: Introduction to Data Analytics

Data analytics is a process that turns data into usable information for answering questions. This course will introduce the process of acquiring, managing and analyzing data. Readily available real-world data sets will be analyzed using supervised and unsupervised learning methods.
Add to Comparison

MATH 3372: Data Visualization

Appropriate visualizations of data are a key to revealing patterns and communicating important findings in research. This course will build on statistical and analytical thinking by emphasizing the role and use of visualizations in the analysis of data. Theories, techniques and software for managing, exploring, analyzing, displaying and communicating information about various types of data will be introduced. Visualizations will be produced using readily available real-world data sets.
Add to Comparison

MATH 3450: Biostatistics I

Basic concepts leading to advanced applications in biostatistics. Topics include study design, data collection, descriptive statistics, probability and probability distributions, confidence intervals, hypothesis testing, power of statistical tests, and simple regression with an emphasis on applications in the biomedical sciences and biomedical research. Data will be analyzed using statistical software packages. Students may be required to register for MATH 1050 for this course.
Add to Comparison

MATH 4331: Real Analysis I

Introduction to concepts and methods basic to real analysis. Topics such as the real number system, sequences, continuity, uniform continuity, differentiation, infinite series and integration.
Add to Comparison

MATH 4332: Real Analysis II

Continuation of Math 4331 covering such topics as uniform convergence and functions of several variables. Strongly recommended for students planning to enter graduate school or secondary teaching and those interested in applied mathematics.
Add to Comparison

MATH 4335: Topology

The basics of point–set topology. Open and closed sets, limit points, topological spaces, countability, compactness, connectedness, metrics and metric topologies.
Add to Comparison

MATH 4341: Linear Algebra II

Continuation of Math 2341. Linear transformations and similarity, eigenvalues and diagonalization, complex vector spaces, unitary and self–adjoing matrices, Spectral Theorem, Jordon canonical form. Selected topics in linear programming, convexity, numerical methods, and functional analysis.
Remove from Comparison

MATH 4344: Mathematical Modeling

This course provides an introduction to developing mathematics models from real world situations through discussion of a series of examples, and hands-on exercises and projects that make use of a range of continuous and discrete mathematical tools.
Add to Comparison

MATH 4350: Biostatistics II

Application and extension of Biostatistics I with a focus on advanced statistical concepts which recur in biomedical research literature; multiple regression, logistic regression and survival analysis. Other topics may include time series analysis and clinical trials. Practival experience with the widely used statistical research software package R. Emphasis on realistic data typically encountered in applications of biostatistics.
Add to Comparison

MATH 4370: Capstone Project

The student will work with faculty supervision and undertake a research project. The project will culminate in a tangible product such as a paper, a presentation or a research poster.
Add to Comparison

MSDS 5311: Introduction to Analytics

Data analytics is a process that turns data into usable information for answering questions. This course will introduce the process of acquiring, managing and analyzing data. Readily available real-world data sets will be analyzed using supervised and unsupervised learning methods.
Add to Comparison

MSDS 5312: Data Visualization

Appropriate visualizations of data are a key to revealing patterns and communicating important findings in research. This course will build on statistical and analytical thinking by emphasizing the role and use of visualizations in the analysis of data. Theories, techniques and software for managing, exploring, analyzing, displaying and communicating information about various types of data will be introduced. Visualizations will be produced using readily available real-world data sets.
Add to Comparison

MSDS 5315: Databases and Data Management

Organization concepts and terminology of data models and the underlying data structures needed to support them. Presentation of the relational database management system including an introduction to SQL programming, normalization and database design. Introduction to the programming interface to databases.
Add to Comparison

MSDS 5361: Data and Social Issues

An examination of algorithmic bias, legal and privacy issues about data that arise in the phases of a data science project and how data is related to social issues. Case studies from various disciplines will be used to explore these issues.
Add to Comparison

MSDS 6331: Machine Learning

An overview of the key concepts of machine learning through practical examples and applications. Programming projects will be used for learning techniques, for interpreting results and understanding scaling up from thousands of records to millions/billions.
Add to Comparison