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 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.
Data Scientist ranked as the
#1 job in the U.S. for 2019
Data science jobs increased
by 29% from 2017-18
Current active job postings
in Computer Science
Burning Glass Technologies, 2019
Current active professionals
in the Computer Science field
Burning Glass Technologies, 2019
Salary range of
computer science careers
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
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.
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:
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.
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.
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 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.
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
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
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.
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.
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.
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
Programming and data storage with cloud architectures. Topics include the Apache Hadoop Ecosystem and related programming frameworks.
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
Introduction to concepts in cybersecurity, including cryptography; instruction detection/prevention; attacking/defending; cybersecurity tools; malware and reverse engineering; defensive programming.
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.