Online Masters in Computer Science

100% online computer science program

100% Online

No GRE Required for the online computer science program

No GRE required

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#25 Most Innovative Schools, 2020

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 and data science. 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.
# 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

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
Advanced Object-Oriented Development

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.

Foundations of Algorithms

This course will provide a comprehensive introduction to computer algorithms taken from diverse areas of application. This course will concentrate 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 and their related languages. Topics will include finite automata and regular languages, push-down automata and context-free languages, linear-bounded automata and context-sensitive languages. Turing machines and phrase structure languages, closure properties, decidability results, and non-determinism.

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.

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 Algorithms

Pre-requisite(s): Students are expected to have competency equivalent to CSI 5301, or equivalent course. Analysis of algorithm performance, time and space complexity. Graph algorithms, vector and matrix algorithms, adversary arguments, optimal algorithms, parallel algorithms, and current research topics. Intense coverage of NP-completeness with emphasis on recognizing NP-complete problems, proving NP-completeness, and creating approximation algorithms.

Applied Data Science

Pre-requisite: CSI 5355 or consent of instructor. This course surveys practical areas of data science using an application-based approach. Students will participate in guided projects intended to replicate the integration of scalable computing, very large data sets, and analytic approaches.

Foundations of Database

Current relational database design concepts including ER diagrams. Database access techniques such as SQL. Database issues including performance and security. Web-database applications.

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.

Data Mining and Analysis

Advanced topics in Data Mining will be presented. These include the pattern analysis of numerical, categorical, time and textual data. It will focus 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

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

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.

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.

Advanced Data Communications

Pre-requisite(s): Students are expected to have competency equivalent to CSI 5304, or equivalent course. Survey of current and seminal research in networking.

Foundations of Data Communications

Introduction to the fundamentals of computer networking, including communicating issues/solutions at various layers, socket programming, and internet protocols.

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.

Advanced Databases

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.

Foundations of Math for Computer Science

A survey of mathematical topics for computer scientists. An introduction to proof techniques, matrices, and statistics.

Intro to Machine Learning

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.

High Demand for Computer Science Roles

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

- Burning Glass Technologies, 2019


Program Features

  • Earn your Master of Computer Science degree from Baylor University, ranked #76 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: 30 (up to 42 with foundation courses)

Admissions Requirements

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


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 particular focus on software specification and testing.

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

Distributed Systems

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.

Core Courses

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.

Data Science Track

Cloud Computing

Programming and data storage with cloud architectures. Topics include the Apache Hadoop Ecosystem and related programming frameworks.

Cybersecurity Track

Data Visualization

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.

Software Engineering Track

Cybersecurity Concepts

Introduction to concepts in cybersecurity, including cryptography; instruction detection/prevention; attacking/defending; cybersecurity tools; malware and reverse engineering; defensive programming.

Cybersecurity Course(s)