Online Masters in Computer Science

100% online computer science program

100% Online

No GRE Required for the online computer science program

No GRE required

Ranked 42 by US News and World Report

#42 Most Innovative Schools

Online Masters in Computer Science

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, Data Science, and Cybersecurity. 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 in computer science or a math-focused undergrad 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.
# 1

Data Scientist ranked as the
#1 job in the U.S. for 2019


Data science jobs increased
by 29% from 2017-18

3 Million

Current active job postings
in Computer Science

Burning Glass Technologies, 2019

3.6 Million

Current active professionals
in the Computer Science field

Burning Glass Technologies, 2019


Salary range of
computer science careers


Demand for Cybersecurity jobs
since 2013

Burning Glass Technologies

Program Features

  • Earn your Master of Computer Science degree from Baylor University, ranked #79 among national universities by U.S. News & Report.
  • 100% online.
  • Courses are 15 weeks, with three intakes per year.
  • Students learn from nationally recognized faculty.
  • Total credits: 33 (Bachelor's in Computer Science required).
  • Total credits: 48 with foundation courses (Bachelor's in other relevant STEM degrees).
Advanced Object-Oriented Development

Prerequisite(s): CSI 4344.

Object‐oriented development brings many instruments and constructs, involving composition, inheritance, polymorphism, templates, etc. However, how do you use them effectively to solve engineering problems? In this course, you will learn how to apply the best software industry practices to object‐oriented design and programming. We will examine basic and more advanced design patterns that are applicable in conventional programming as well as for enterprise solutions. You will learn to recognize design issues and refactor them using the best practice.

Intro to Algorithms

This course will provide a comprehensive introduction to computer algorithms taken from diverse areas of application. It will concentrate on algorithms of fundamental importance and on analyzing the efficiency of these algorithms.

Admissions Requirements

  • 3.0 GPA in Bachelor's degree 
  • Three letters of recommendation 
  • Resume 
  • Proficient in a high-level programming language such as Python, C, C++, C#, or Java
Big Data Analytics

This course will explore the advances in large‐scale data repositories. Students will be exposed to advanced topics in the analytical approaches to handling the five Vs — volume, velocity, variability, veracity, and value — of big data. Parallel programming based on the MapReduce paradigm within the Hadoop Ecosystem is used to address these needs.

Intro to Database Design

Current relational database design concepts, including ER diagrams and normalization. Database access techniques, such as SQL and JDBC. Database issues, including performance and security. Web-database applications. Fee: $50.

Applicants should have a Bachelor of Science (B.S.) degree in computer science or a closely related field. We expect successful applicants to have the equivalent training of a B.S. degree in computer science from Baylor University with knowledge of fundamental theory (math, algorithms, data structures), computer systems (operating systems, network, databases), software engineering, and programming.

For those applying with less than the standard preparation, the quality and adequacy of the admissions record will be evaluated by the Graduate Committee of the Department of Computer Science after reviewing the application for admission. The requirements, which must be met before admission, will be determined by that committee. These requirements will be in addition to the requirements for the M.S. degree.

Data Mining

An introduction to the concepts, techniques, and applications of data warehousing and data mining. Topics include the design and implementation of data warehouse and OLAP operations; data mining concepts and methods, such as association rule mining, pattern mining, classification, and clustering; and the applications of data-mining techniques to complex types of data in various fields. Advanced topics in machine learning and statistics will be covered.

Intro to Software Engineering

An engineering approach to software development emphasizing design patterns and techniques for enterprise application development. Completing a software project by applying a development process. Fee: $50.

Online Masters in Computer Science Curriculum

Designed to provide students with greater skills in software systems, this curriculum focuses on software systems development, data science, and cybersecurity.

Intro to Data Communications

Prerequisite(s): CSI 3344. Fundamentals of computer networking, including data transmission, communication software, protocols, simple networks, and internetworking.

Secure Systems, Software Architecture

Defensive programming, secure development operations, secure ecosystem, etc. Networking, database, operating system, and server fundamentals and the application of security principles in authentication, authorization, and design. Cloud/mobile/IoT/security.

Intro to Computation Theory

Prerequisite(s): CSI 3344. This course is an introduction to the theory of computation. It covers the formal models of computation, computability, complexity, and related topics. This course forms a foundation for much of the subsequent work/research you will do in computer science. As you'll see, computation theory is fascinating because there are problems that we can describe simply, which have not yet been solved.

Intro to Operating Systems

Prerequisite(s): CSI 3344. 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. Fee: $50.

Important Dates

Next Application Deadline: November 20th, 2020

Next Start Date: January 11th, 2021

Advanced Algorithms

Prerequisite(s): CSI 3344 or graduate standing. Advanced data structures, algorithm design, and analysis. Topics include common data structures, algorithms, implementation, classes of algorithms, algorithm analysis, computational tradeoffs, and the adaptation of familiar algorithms to new problems.

High Demand for Computer Science Roles

Texas is the second-largest market
for computer-related positions

- Burning Glass Technologies, 2019

Software Engineering

The objective is to introduce a software-engineering practice. After the course, you will be able to document software systems using known notation. You will learn to analyze, design, and implement information systems using Java EE. You will recognize best practices and good coding styles as well as the development and testing approaches used in the industry for enterprise systems. In the class, we also investigate alternatives to the mainstream and produce system documentation on a thesis level.

Which Track is Right for You?

Our dedicated enrollment advisors can help you decide which track is best for you. Here’s some information to get the conversation started:

Advanced Data Communications

Prerequisite(s): CSI 4321 or equivalent. Survey of current and seminal research in networking. This is largely a paper class, focusing on timely research topics in networking. Since this class explores issues on the “leading edge” of networking, we may change its procedures dynamically based on experience.

Develops techniques and algorithms for efficient management and analysis of big data. This field employs mathematics, statistics and computer programming disciplines, and incorporates advanced techniques of data mining, machine learning, and visualization.

Advanced Databases

Prerequisite(s): CSI 3334 and 3335. A continuation of database system implementations to include object-oriented and knowledge-based systems. Additional topics covered are the physical-data organization, database integrity, security, transaction management, and distributed database management.

Develops the methods used to produce and maintain high-quality software in a systematic, controlled, and efficient manner. Focus spans across pattern specification, software architecture, microservice-based enterprise application design, code analysis, security assessment, model-driven analysis, and testing.

Intro to Machine Learning

Prerequisite(s): CSI 4336. An introduction to topics in machine learning, including supervised and unsupervised learning, modeling for regression and classification, Naive Bayes methods, kernel-based learning, support vector machines, statistical and mathematical models for learning, and model assessment and prediction.

Driven by the fundamental need to protect systems and information from malicious actors and events. It focuses on a combination of network security, data analytics, and policies and procedures for dealing with potential threats.

Software Verification & Validation

Prerequisite(s): CSI 3372 or consent of instructor. Advanced topics in software engineering research, including techniques used in software verification and validation with a focus on software specification and testing.

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
  • Specialization tracks let you focus on the role you’re most passionate about—software engineering, data science, or cybersecurity
Distributed Systems Development

Distributed systems underlie software in multiple domains, including enterprises for stock trading, health care, online shopping, data processing, and transportation management. These systems are frequently designed using the Microservice Architecture (MSA). MSA splits the overall system into independent self‐contained modules managed by distinct development teams. These low-coupled modules interact on a high level (e.g., through REST calls or messaging), which enables development autonomy, individual module upgrades, or selective redeployment.

Advanced Software Engineering

Prerequisite(s): CSI 5324 or consent of instructor. Advanced topics in software engineering research, including the techniques used in the modeling and analysis of complex systems.

Cloud Computing

This course focuses on advanced topics in cloud environments (AWS, Google, Azure) and economics, history, differences, and importance of architecture decisions such as the decisions that drive analytics, data lakes, arts, and warehouses. The core concepts of virtual private clouds, instance types, microservices, and storage services will be addressed, in addition to deeper architecture concepts.

Visualization of High-Dimensional Data

Visualization as a tool for data analysis, recall, inference, and decision‐making. This advanced class will explore high‐dimensional data spaces and tools for visual description and presentation. Principles of effective visualization, including data‐visual mapping, interaction techniques, color theory, cognitive and perceptual psychology, and the human factors of visual depictions of data.

Cybersecurity Concepts

Introduces topics in cybersecurity, including cryptography.

Advanced Cybersecurity Concepts

Advanced topics in cybersecurity, including information security policy, governance, risk management, and strategic decision making.

Cybersecurity Analytics

The application of data principles to anomaly detection, response, etc.