Course Descriptions

Bachelor of Science in Data Science

For a full list of all courses offered by the Department of Mathematics, visit the course catalogue.

Students will learn the basic programming techniques needed to create simple scripts/program to automate and perform simple computer operations. Students will learn the skills needed to implement algorithms to solve computing problems using select scripting languages. Course will review common structures between scripting languages to include Python, Ruby, PERL, PowerShell, SQL and BASH. Topics will include basic performance optimization and security practices in developing scripts and programs using Python.

Prerequisite: CIS 2320

Learn the basic programming techniques needed to create simple scripts/program to automate and perform simple computer operations. Learn the skills needed to implement algorithms to solve computing problems using select scripting languages. Course will review common structures between scripting languages to include Python, Ruby, PERL, PowerShell, SQL and BASH. Topics will include basic performance optimization and security practices in developing scripts and programs using Python.

Prerequisite: CIS 2330 or CSEC 2330

A course designed to give students an introduction to the fundamentals of data science. Students will learn the essential skills necessary for work in data science. This course assumes no previous computer programming experience, and it assumes no prior knowledge in statistics.

Prerequisite: MATH 1304

This three-hour course provides a broad introduction to machine learning and statistical pattern recognition, with a focus on mathematical foundations and programming techniques. Students may learn to use a variety of software packages, such as R, Python, MATLAB, Minitab, JMP, or SAS. The course will also discuss application of Machine Learning to Data Mining.

Prerequisite: MATH 3332
This three-hour course provides a broad introduction to Deep Learning, with a focus on mathematical foundations and programming techniques. By the end of the semester, students will understand how to build neural networks, and learn how to lead successful machine learning projects. Students may learn to use a variety of software packages, such as R, Python, MATLAB, Minitab, JMP, or SAS.

Prerequisites: MATH 2340 and DATA 4365
This course seeks to aid development of academic maturity required for students majoring in Data Science. As such, it is intended for students who have completed the major requirements. Students who complete this course will have the fundamental tools to begin solving real-world problems. The course is the culmination of preparation for work as a Data Scientist and requires permission of the instructor.

This is a three-hour course, which includes functions, limits, derivatives, indeterminate forms, and integrals, exponential and logarithmic functions; inverse trigonometric functions, and applications.

Prerequisite:  MATH 1311

Math course descriptions

This course includes techniques of integration, applications of integration, improper integrals, infinite series and calculus using polar and parametric curves.

Prerequisite: MATH 2312

This course covers vector spaces, linear transformations and matrices.

Prerequisite: MATH 1304 or MATH 1311 or MATH 2312 or MATH 2313 or MATH 2314

Math course descriptions

This is an introductory course in C programming for mathematics, sciences and engineering majors. Topics include: data types, and related operations, floating errors, input/output, control structures, functions, arrays, data structure, files and strings processing. Program design, debugging techniques and good programming practices will also be discussed. Programming exercises and projects will emphasize problems and applications in mathematics, sciences and engineering fields.

This course covers vectors, differential calculus of functions of several variables, multiple integrals, and applications.

Prerequisite: MATH 2313

This three-hour course covers probability, fundamentals of statistics, functions of random variables, discrete and continuous distributions, moments and moment-generating functions. It is part one of a two-course sequence with MATH 3332, Foundations of Statistical Inference. Students should have either completed MATH 2313, Calculus II, or be enrolled in MATH 2313 in the same semester with this course.

Prerequisite: MATH 2313

This three-hour course covers techniques of statistical inference including sampling theory, estimation procedures, hypothesis testing, and method of maximum likelihood. It is part two of a two-course sequence with MATH 3331 Foundations of Probability and Statistics.

Prerequisite: MATH 3331

 

This three-hour course covers the theory and basic applications of regression analysis. Topics covered include simple linear regression, multiple regression, model selection processes, and basic nonlinear models, such as logistic and probit regression. The course also covers the use of software packages R, Minitab, and SAS.

Prerequisite: MATH 3332