Preparing Innovators for Worldwide Impact with an Online Masters in Computer Science
Baylor University’s Master of Computer Science online degree program prepares its graduates for the highest-demand roles in software engineering. Our curriculum emphasizes technical proficiency, innovative thinking, ethical considerations, and the trends shaping science and technology on a global scale.
Designed for students with a Bachelor’s degree in Computer Science, Engineering, Mathematics, Physics, or a closely-related degree, this program will prepare you to become an expert in computing.
Developed for maximum demand—earn advanced credentials and skills for leading computer science roles.
Instills innovative thinking—gain comprehensive knowledge and thoughtful skill-building for technology experts.
Globally connected, ethically centered—consider the human aspects surrounding technology in an increasingly connected and diverse world.
Data Scientist ranked as the
#1 job in the U.S. for 2019
Data Scientist jobs openings are projected to increase by
by 36% through 2031
Current active job postings
in Computer Science
Current active professionals
in the Computer Science field
The median annual wage for
a Computer Scientist
Demand for Cybersecurity jobs
Burning Glass Technologies
Prepare for Vital Roles in Technology with a Master’s Degree in Computer Science
Opportunities in technology continue to grow for professionals. Baylor University’s Master of Computer Science online program provides an advanced educational experience that emphasizes mastery from both technical and professional perspectives.
Foundational courses (prerequisite track) prepare you for your master’s degree by teaching key computing fundamentals
Core computer science concepts build your knowledge of topics, such as algorithms, software languages, databases, and networking
Foundations of Algorithms
This course provides a comprehensive introduction to computer algorithms taken from diverse areas of application. The course concentrates on algorithms of fundamental importance and on analyzing the efficiency of these algorithms. This course is considered a leveling course and it will not count toward the online master’s degree.
Intro to Computation Theory
Several models of computation (including finite automata, pushdown automata, and Turing machines) and their related languages. Topics include closure properties, regular languages, context‐free languages, decidability and recognizability, and time and space complexity (including NP‐completeness and randomized complexity).
Software Verification & Validation
Pre-requisite(s): CSI 5303, or equivalent; or consent of instructor. Advanced topics in software engineering research, including techniques used in software verification and validation with a particular focus on software specification and testing.
Is a Computer Science Degree Required to Qualify?
No. Applicants with a Bachelor of Science (B.S.) degree in Engineering, Mathematics, Physics or another closely related field are also eligible.
Pre-requisite(s): Students are expected to have competency equivalent to CSI 5301, or equivalent course. Advanced data structures, algorithm design, and analysis. Topics include common data structures, algorithms, implementation, classes of algorithms, algorithm analysis, computational tradeoffs, and adaptation of familiar algorithms to new problems.
An in-depth exploration of the techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, psychology, and cognitive sciences. Explores how to better understand data, present clear findings, and tell engaging data stories.
Design and implementation of distributed systems with up-to-date software architecture and relevant development frameworks. Topics include inter-module communication, asynchronous processing, security, concurrency, parallelism, and an overview of contemporary enterprise technology and challenges.
Foundations of Database
The course covers current relational database design concepts including ER diagrams, database access techniques such as SQL, database issues including performance and security, and web‐database applications.
Advanced Object-Oriented Development
Object-oriented design and development with best practices in solving recurring engineering problems. Topics include core object‐oriented concepts, such as composition, inheritance, polymorphism, and templates; an overview of design pattern‐based problem solving and design practices; and advanced design patterns applicable for enterprise solution development.
Data Mining and Analysis
Advanced topics in Data Mining are presented. These include the pattern analysis of numerical, categorical, time, and textual data. The course focuses on algorithms for clustering and predictive modeling with special attention to extracting useful information from raw data, and methods for data validation.
Foundations of Software Engineering (for Software Engineering Track)
Co-requisite(s): CSI 5301 (or any similar Introduction to Algorithms course) and basic Java programming skill required.
Pre-requisite(s): CSI 5301 is preferred. Fundamentals of software engineering; software development processes, requirements analysis, modular design, design patterns, software testing and evolution, configuration management, and implementation of software systems. A small project to illustrate and extend concepts from lectures.
Software Engineering (Required for Software Engineering Track)
Pre-requisite(s): Students are expected to have competency equivalent to CSI 5303, or equivalent course. Methods for developing and maintaining software systems; system software life cycle, requirements elicitation, specification and design methods, planning, maintenance, configuration management, documentation and coding standards, cost estimation, metrics and quality attributes; class project.
Pre-requisite(s): Students are expected to have competency equivalent to CSI 5304, or equivalent course. Survey of current and seminal research in networking.
Advanced Software Engineering
Prerequisite(s): CSI 5324 or consent of instructor. Advanced topics in software engineering research, including techniques used in the modeling and analysis of complex systems.
Foundations of Data Communications
Introduction to the fundamentals of computer networking, including communicating issues/solutions at various layers, socket programming, and internet protocols.
Pre-requisite(s): Students are expected to have competency equivalent to CSI 5302, or equivalent course. A continuation of database system implementations to include object-oriented and knowledge-based systems. Additional topics covered are physical-data organization, database integrity, security, transaction management, and distributed database management.
Foundations of Operating Systems
Operating system design and implementation. Topics include process control and synchronization, memory management, processor scheduling, file systems, and security. Course projects implement parts of an operating system.
Traditional machine learning algorithms, neural networks, etc., are pieces of a greater puzzle required for machines to qualitatively learn, rather than just statistically remember. Therefore, students learn new AI approaches and AI architectures: autonomy, deep sensing, measuring trust, complexity analysis, security, ethics, multi-state, and quantum for producing systems for challenging human settings like deep-sea, space, and disaster recovery
Foundations of Math for Computer Science
A survey of mathematical topics for computer scientists. An introduction to differential and integral calculus, matrices, proof techniques, and statistics.
Earn your Master of Computer Science degree from Baylor University, ranked #76 among national universities by U.S. News & Report
Courses are 15 weeks, with three intakes per year
Students learn from nationally recognized faculty
Total credits: 30 (up to 42 with foundation courses)
3.0 GPA in Bachelor's degree
Three letters of recommendation
Proficient in a high-level, object-oriented programming language such as Python, C, C++, C#, or Java
Online Masters in Computer Science Curriculum
Designed to provide students with greater skills in software systems, this curriculum focuses on software systems development and data science cybersecurity.
Foundation Courses (Prerequisite Track)
Below are the core course options that the program offers. Contact us to learn more about which courses you will take as part of your program.
Programming and data storage with cloud architectures. Topics include the Apache Hadoop Ecosystem and related programming frameworks.
Cybersecurity – Core Elective
Cybersecurity Concepts is a regularly offered course that can be taken to satisfy a core course requirement. It is the standard core elective. Contact us to learn more about which courses you will take as part of your program.
Applied Data Science
This course surveys practical areas of data science using an application-based approach. Additionally, students are introduced to new content and coding paradigms for developing more intelligent data processing environments. Students participate in guided projects intended to replicate the integration of scalable computing, integration of very large passive and active high-speed data sets, and new analytic approaches.
Introduction to concepts in cybersecurity, including cryptography; instruction detection/prevention; attacking/defending; cybersecurity tools; malware and reverse engineering; and defensive programming.
How long is the online Masters in Computer Science program?
Students with a Computer Science background typically complete the program in just over 1.5 years. Students with a non-Computer Science background typically complete the program in just over 2 years. Length to program completion may vary by student.
Is a Computer Science degree required to qualify?
No. Applicants with a Bachelor of Science (B.S.) degree in Engineering, Mathematics, Physics, or another closely related field are also eligible.
Is programming experience required?
Yes, you must be proficient in a high-level, object-oriented programming language such as Java, Python, C, C++, or C#. Programming experience that is self-taught would not apply and proficiency must be acquired via work experience or education.
Am I eligible for the program if I am a self-taught programmer without formal programming education?
Programming experience can only be acquired via education or gained through work experience. If gained through work experience, you must prove your proficiency with programming.
What if I have experience with only one of the five programming languages required?
Experience and proficiency with any one of the five (Java, Python, C, C++, or C#) is sufficient.
If I do NOT have a degree in Computer Science or something similar, which math classes must I have completed to be eligible for the program?
Calculus II AND Linear Algebra.
Connect With Your
Baylor Enrollment Advisor
Whether you have a simple question or need advice to determine if this program is the best for you, our friendly advisors are here to help. They can chat with you on your schedule and guide you through the entire admissions process so that you feel confident in moving forward with Baylor University.