Master of Computer Science Course Sequence | Concordia University Texas

This course covers the design and analysis of efficient algorithms for various computational problems. Emphasis is placed on algorithmic thinking, computational complexity, NP completeness, sorting, manipulation of data structures, graphs, matrix multiplication, and pattern matching.

This course covers principles of operating systems, process management, threading, synchronization, memory management, virtual memory, file systems, I/O systems, security, and device management.

This course covers methods, principles, and concepts that are relevant to the practice of database software design. Topics include data models, data model theory, optimization and normalization, integrity constraints, query languages, and intelligent database systems.

This course examines advanced software engineering principles, methods, and tools for designing, developing, testing, and maintaining large-scale software systems. Emphasis is placed on requirements analysis, architecture, agile processes, quality assurance, collaboration, and professional practice within the Master of Computer Science program.

This course introduces principles and practices of software project management, including scope definition, scheduling, cost estimation, risk management, quality control, and team leadership, with emphasis on agile and traditional approaches for delivering successful software projects in professional settings.

This course explores principles, methods, and tools for designing, prototyping, and evaluating interactive systems. Topics include user-centered design, usability, accessibility, interface evaluation, and emerging interaction technologies, emphasizing practical projects and ethical considerations in human–computer interaction.

In this first capstone/thesis course in the Master of Computer Science program at Concordia University Texas, students define a project, conduct preliminary research, outline methods, and create a plan in collaboration with a faculty mentor.

This elective explores advanced computer science topics, emphasizing practical application, critical thinking, and ethical considerations while strengthening students' expertise, problem-solving abilities, and readiness for leadership roles within Concordia University Texas's Master of Computer Science program.

In this second capstone/thesis course in the Master of Computer Science program at Concordia University Texas, students implement, evaluate, and document their findings, culminating in a final project.

CSC 53XX Secure Software Development

This course provides a comprehensive overview of security vulnerabilities in information systems and foundational techniques for developing secure applications and promoting safe computing practices. Topics include common attack vectors such as buffer overflows, Trojans, and viruses, as well as platform-specific security considerations. Prerequisites: Advanced Algorithms, Operating Systems, Software Engineering
CSC 53XX Network Security

This course introduces students to the principles and tools of digital forensics used in investigating cybercrime. Topics include evidence collection, preservation, analysis of file systems, memory, and network data, as well as legal and ethical considerations. Prerequisites: Advanced Algorithms, Operating Systems, Software Engineering
CSC 53XX Digital Forensics

This course introduces students to the principles and tools of digital forensics used in investigating cybercrime. Topics include evidence collection, preservation, analysis of file systems, memory, and network data, as well as legal and ethical considerations. Prerequisites: Advanced Algorithms, Operating Systems, Software Engineering
CSC 53XX Secure Architecture Design

This course focuses on architecture-level vulnerabilities, secure architecture design principles and applications to modern microprocessors. Students explore vulnerabilities, countermeasures and new secure design principles. Prerequisites: Capstone Project 1

CSC 53XX Artificial Intelligence

This course provides a comprehensive introduction to the fundamental problems, theories, and algorithms in the field of artificial intelligence (AI). Topics include knowledge representation logic and deduction, expert systems, planning, language comprehension, and machine learning. Prerequisites: Advanced Algorithms, Operating Systems, Software Engineering
CSC 53XX Deep Learning

This course covers deep learning, a branch of machine learning concerned with the development and application of modern neural networks. Students understand deep neural networks, and their applications to various AI tasks, and learn how to lead successful machine learning projects. Prerequisites: Advanced Algorithms, Operating Systems, Software Engineering
CSC 53XX Natural Language Processing

This course covers methods for natural language processing, its related algorithms and ideas. The course presents advanced material on lexical knowledge acquisition, natural language generation, machine translation, and parallel processing of natural language. Prerequisite: Prerequisites: Advanced Algorithms, Operating Systems, Software Engineering
CSC 53XX Generative AI and Language Models

This course examines the evolving landscape of generative artificial intelligence technology with a focus on technologies driven by Large Language Models (LLMs). This class covers the theory, techniques, and practical applications of Generative AI using Large Language Models and their associated applications. Students will explore the intricacies of large language models, developing a comprehensive understanding of their underlying mechanisms. Prerequisite: Artificial Intelligence

CSC 53XX Fundamentals of Data Science

This course covers the practical and theoretical aspects of data science as applied to real-world scenarios. The course examines descriptive, exploratory, inferential, predictive, and causal approaches to data, highlighting methods for addressing complex problems across diverse domains. Topics include the data science process, tools for data exploration and modeling, and the underlying concepts and technologies related to data analysis. Prerequisites: Advanced Algorithms, Operating Systems, Database Systems
CSC 53XX Data Management for Data Science

This course covers fundamental and advanced principles of data management essential for data science applications. It explores the design and use of database management systems (DBMS) with a focus on efficiently storing, querying, and processing large datasets. Prerequisites: Advanced Algorithms, Operating Systems, Database Systems
CSC 53XX Statistical Methods for Data Science

This course covers the foundation in statistical techniques essential for data analysis and modeling. Topics include hypothesis testing, regression, Bayesian inference, and multivariate analysis. The course emphasizes practical applications of statistical methods using real-world datasets and tools like R or Python. Prerequisite: Fundamentals of Data Science
CSC 53XX Data Mining

This course introduces methods for discovering patterns and relationships in large datasets. Topics include classification, clustering, association rule mining, anomaly detection, and model evaluation. Students apply mining algorithms to solve problems in domains such as marketing, fraud detection, and healthcare. Prerequisite: Fundamentals of Data Science.

MCS Course Sequence

Master of Computer Science

Without Concentration

Master of Computer Science

With Concentration

CHOOSE ONE CONCENTRATION