Background

Web Development I: An introduction to the design, development, and maintenance of dynamic web pages and websites, including coverage of HTML, CSS, and the PHP programming language

Web Development II: Advanced design, development, and maintenance of dynamic web pages and websites, including coverage of Javascript, third-party Javascript tools such as jQuery and jQuery Mobile, as well as AJAX, and PHP/MySQL

Visual Basic Programming: Introduction to object-oriented, event-driven, and procedural programming to develop business and e-commerce applications

Management Information Systems: A survey of the components, functions, and processes of Information Systems as they relate to managing modern organization for increased efficiency and competitiveness

Advanced Languages I: An introduction to object-oriented programming, with an emphasis on working with classes, objects, and event-driven programming

Business Statistical Methods I: Methods of describing numerical data; probability in business decisions; random variables; sampling distributions; introduction to estimation and hypothesis testing; computer statistical packages applied

Business Statistical Methods II: Reviewing estimation and hypothesis testing; correlation and regression; chi-square tests; analysis of variance; non-parametric concepts; index numbers; time series analysis; computer statistical packages applied

Introduction to CSE: Introduction to the computer science and software engineering curricula, profession, and career opportunities. Historical perspective; support role of the department. Ethics, team building, problem solving

Computer Programming with C: Problem-solving methods, algorithm development, debugging, and documentation in the C Programming language; applications

Computer Programming with Java: Problem-solving methods, algorithm development, debugging, and documentation in the Java programming language; applications

Introduction to Computer Programming: Introductory problem-solving and computer programming using object-oriented techniques. Theoretical and practical aspects of programming and problem-solving

Intermediate Computer Programming: Object-oriented problem solving, design, and programming. Introduction to data structures, algorithm design, and complexity

Methods and Tools in Software Development: Intro to key concepts, methods, and tools in software development not introduced in the introductory programming courses. Includes techniques for team-based SW development, agile design, implementation, testing patterns and strategies, and data retrieval using SQL

Introduction to Software Engineering: Introduction to software engineering; planning, requirements, analysis and specification, design; testing; debugging; maintenance; documentation. Alternative design methods, software metrics, software project management, reuse, and reengineering

Data Structures and Analysis of Algorithms: Non-linear data structures and their associated algorithms. Trees, graphs, hash tables, relational data model, file organization. Advanced software design and development

Discrete Structures: Concepts of algorithms, induction, recursion, proofs, topics from logic, set theory, combinatorics, and graph theory fundamental to the study of computer science

Systems Programming: Overview of contemporary systems programming concepts, tools, and techniques. Shell programming, systems administration tools, distributed systems, and internet concepts

Distributed Client/Server Programming: Design of software systems for distributed environments. Multithreaded and server-side programming, client/server

Introduction to Cybersecurity: Basic security concepts and analysis. Cryptography basics. Computer and network attacks and defense techniques

Information and Computer Security: Topics include encryption systems, network security, electronic commerce, systems threats, and risk avoidance procedures

Computer Organization: How computer programs are executed by stored program computers. Topics include Boolean logic, design of combinational and sequential logic circuits, number systems and computer arithmetic, HW design and organization of a CPU, machine, and assembly language programming

AI Fundamentals: Provides students with an introduction to the foundational concepts, techniques, and applications of Artificial Intelligence(AI). This course discusses the evolution of AI, problem-solving and search methods, knowledge representation, rule-based systems, machine learning

Artificial Intelligence: Study of the computer in context with human thought processes. Heuristic programming;search programming; search strategies; knowledge representation; natural language understanding; perception; learning

AI Robotics: Introduction to artificial intelligence methods for mobile robots. Focus on the theory and practice of robot sensing, localization, navigation, and intelligent task execution

Introduction to Machine Learning: Provides an overview of the most important machine learning and data mining methods, and how to apply them to large data sets

AI for Cybersecurity: The use of artificial intelligence and machine learning to solve cybersecurity problems, including advanced topics in applying these techniques to real-world datasets to learn about Cyber Threat Intelligence (CTI), malware analysis, and classification

Intermediate Algebra: Real numbers, algebraic expressions, factoring, algebraic fractions, linear equations/inequalities, quadratic equations, Pythagorean Theorem

Math in Your World: Topics will include but are not limited to ratios & proportions, unit conversions, formula manipulation, logical reasoning, financial literacy, general number sense, and the use of Excel to solve real-world problems

College Algebra: Review of fundamentals; linear and quadratic equations; inequalities; functions; simultaneous equations; topics in the theory of equations

Trigonometry: The trigonometric functions: identities; trigonometric equations; applications

Structure of the Real Number System: The nature of mathematics; introductory logic; structure and development of the real number system

Problem Solving with Real Numbers: Proportions, percent problems, probability, counting principles, statistics

Precalculus: Properties, applications, and graphs of linear, quadratic, polynomial, exponential, logarithmic, and trigonometric functions, identities, equations, and inverses

Calculus for Business and Life Sciences I: Algebraic and some transcendental functions, solutions of systems of linear equations, limits, continuity, derivatives, applications

Calculus I: Analytic geometry; functions; limits; continuity; derivatives of algebraic and transcendental functions; applications of the derivative

Calculus II: Anti-differentiation; the definite integral; applications of the definite integral; integration of transcendental functions; other techniques of integration

Calculus III: Parametric and Polar Equations; infinite series; introduction to vectors; vector functions

Calculus IV: Differential calculus of functions of several variables; multiple integration; vector calculus

Introduction to Modern Scientific Computing: Basic programming skills and applications to scientific computing; iteration and recursion; accuracy and efficiency issues; matrix operations; data interpolation; unconstrained optimization; regression analysis; multiple local minima problems

Foundations of Mathematics: The logical structure of mathematics; the nature of a mathematical proof; applications to the basic principles of algebra and calculus

Introduction to Linear Algebra: Basic principles of linear algebra; vector spaces; matrices; matrix algebra; linear transformations; systems of linear equations; eigenvalues and eigenvectors; orthogonality and Gram-Schmidt process

Differential Equations I:  Origin and solutions of first and second-order differential equations; Laplace Transform methods; applications

Differential Equations II: Systems of differential equations; matrix representations; infinite series solution of ordinary differential equations; selected special functions; boundary-value problems; orthogonal functions: Fourier series

Discrete Mathematics: Sets, relations, functions, combinatorics, review of group and ring theory, Burnside's theorem, Polya's counting theory, group codes, finite fields, cyclic codes, and error-correcting codes

Graph Theory: Basic concepts, graphs, and matrices, algebraic graph theory, planarity and non-planarity, Hamiltonian graphs, digraphs, network flows, and applications

Matrices and Linear Algebra:  Linear transformations and matrices; eigenvalues and similarity transformations; linear functionals, bilinear and quadratic forms; orthogonal and unitary transformations; normal matrices; applications of linear algebra

Data Analysis I: Data description and descriptive statistics, probability and probability distributions, parametric one-sample and two-sample inference procedures, simple linear regression, one-way ANOVA. Use of SAS

Data Analysis II: Multiple linear regression; fixed, mixed, and random effect models; block designs; two-factor analysis of variance;three-factor analysis of variance; analysis of covariance. Use of SAS

Introduction to Probability: Basic concepts of probability, conditional probability, independence, random variables, discrete and continuous probability distributions, moment generating function, moments, special distributions, central limit theorem

Introduction to Probability and Random Processes: Probability, law of large numbers, central limit theorem, sampling distributions, confidence intervals, hypothesis testing, linear regression, random processes, correlation functions, frequency and time domain analysis

Introduction to Statistics: Introduction to descriptive statistics, random variables, probability distributions, estimation, confidence intervals, & hypothesis testing. Computer instruction for analysis

Introduction to Statistical Inference: Basic concepts and methods of statistics, including descriptive statistics, probability random variables, sampling distribution, estimation, hypothesis testing, introduction to analysis of variance, simple linear regression

Introduction to Logic: A development of practical ability in the major forms of valid argumentation concluding with a consideration of the universal and existential operators

Introductory Psychological Statistics: An introduction to the techniques and practices in statistical analyses used in psychological experimentation and evaluation along with practical experience in statistical software packages

Game Theory: An introduction to decision theory and game theory. Focuses on modeling strategic decision-making by rational agents with applications to practice and real-world cases

Computer Applications: The course introduces basic computer technology and software applications for educational, business, and personal use

Programming for Applied Data Science: Computer programming and data wrangling through practical application of Python and other data science tools to clean, format, and work with real datasets

Applied Data Visualization: Explore and understand data visually, communicate meaning visually, and create interactive visualizations using industry-standard tools and programming languages

Applied Statistical Methods for Data Science: Select and apply appropriate statistical methods and data science technologies to achieve analytical objectives. Write code to apply descriptive, inferential, predictive, and prescriptive statistical techniques for a variety of data types and purposes

Applied Machine Learning for Data Science: Select and apply appropriate machine learning methods and data science technologies to implement and optimize non-artificial neural network approaches to inference, planning, and classification projects. Learn to use GPUs for computing and estimation