# 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

**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

**Prerequisite:**MATH 3332

**Prerequisites:**MATH 2340 and DATA 4365

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

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

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

**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