Program Overview

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Overview of the Online Master of Computer Science – Cybersecurity Program

The online Master of Computer Science with a concentration in cybersecurity program at Arizona State University is designed to equip students with the skills needed to understand information assurance and security problems, as well as develop potential solutions. Upon graduation, students will be prepared to work in a variety of in-demand positions, including cybersecurity engineer, cybersecurity risk analyst, software development engineer, security engineer, and security analyst.

Program Details

• The program consists of 10 courses, each lasting 7.5 weeks.
• The total credit hours required for the program is 30.
• The program can be completed in 18-36 months.

Degree Questions Answered

The program provides answers to frequently asked questions about the Computer Science – Cybersecurity (MCS) degree, including information on the program's structure, admission requirements, and career opportunities.

Prepare for In-Demand Jobs in Cybersecurity

The program prepares students for career advancement and increased pay in the field of cybersecurity, which is projected to continue growing for years to come. According to an estimate by the U.S. Bureau of Labor Statistics, the global cybersecurity market is projected to grow 86%, from around $145 billion in 2018 to $270 billion by 2026.

What You'll Learn in This Cybersecurity Master's Concentration

In this program, students will learn how to protect and defend information and information systems by ensuring their availability, integrity, authentication, confidentiality, and non-repudiation through protection, detection, and reaction practices. Students will also learn how to analyze and apply key theories, algorithms, and software modules used in the field of computer science.

What's a Concentration and How is This Different from a Master's in Cybersecurity?

This cybersecurity master's concentration is designed for graduate students looking to pursue a thorough education in the area of cybersecurity through the lens of computer systems and algorithms. While a master's in cybersecurity focuses all studies on cybersecurity topics, this concentration provides a holistic education of computer science with nine credit hours of cybersecurity-specific courses.

Professional Certification and Nondegree Enrollment Opportunities

As a nondegree graduate student, students can take master's-level computer science courses without being admitted to the program. This provides the opportunity to establish a high graduate GPA, try out courses, meet the English proficiency requirement, or earn a professional certification credential.

Top 25% of All Accredited Engineering Programs in the Nation

The Ira A. Fulton Schools of Engineering is dedicated to providing a dynamic learning environment and supporting all students on the paths to their degrees. The school has received numerous peer-reviewed programmatic honors from U.S. News & World Report.

Tuition Calculator

The total cost of the degree program is $15,000, or $1,500 per 3-credit course. There are no textbooks required for the courses, and no additional fees.

Course Details

The program includes a range of courses, including:

• Foundations of Algorithms
• Knowledge Representation and Reasoning
• Distributed and Multiprocessor Operating Systems
• Mobile Computing
• Software Verification, Validation and Testing
• Software Project, Process and Quality Management
• Artificial Intelligence
• Data Mining

Foundations of Algorithms

This course covers the fundamental concepts and techniques of algorithms, including:

• Amortized analysis
• Divide-and-conquer
• Dynamic programming
• Greedy algorithms
• Introduction to randomized and approximation algorithms
• Network flows
• NP-completeness
• Stable matching

Knowledge Representation and Reasoning

This course introduces the fundamental concepts and techniques of knowledge representation and reasoning, including:

• Classical logic and knowledge representation
• Answer set programming
• Reasoning about actions and planning
• Ontology, semantic web languages, and knowledge graph
• Combining logic and probability

Distributed and Multiprocessor Operating Systems

This course covers the fundamental concepts and techniques of distributed and multiprocessor operating systems, including:

• Architecture
• Coordination
• Communication
• Consistency and replication

Mobile Computing

This course introduces the fundamental concepts and techniques of mobile computing, including:

• Mobile programming
• Internet of Things (IoT)
• Edge and cloud computing
• Mobile networking
• Mobile information access
• Adaptive applications enabled by machine learning and AI
• Energy-aware systems
• Location-aware computing
• Mobile security and privacy

Software Verification, Validation and Testing

This course covers the fundamental concepts and techniques of software verification, validation, and testing, including:

• Testing background
• Testing process activities
• Requirements-based testing techniques
• Structure-based testing techniques
• System testing
• Testing tools
• Reliability models
• Statistical testing
• Test planning
• Tracking testing progress
• Test documentation
• Test process improvement

Software Project, Process and Quality Management

This course introduces the fundamental concepts and techniques of software project, process, and quality management, including:

• Software development process models
• Software configuration management
• Risk management
• Software project management
• Software acquisition management
• Software process management
• Software quality management

Artificial Intelligence

This course covers the fundamental concepts and techniques of artificial intelligence, including:

• Neural Networks
• Classical Planning
• Modeling & Reasoning
• Reinforcement Learning
• Markov Decision Processes (MDPs)
• Partially Observable Markov Decision Processes (POMDPs)
• Bayesian Networks
• Sensors for Perception
• Perception based Recognition
• Real-world Applications
• Robotics

Data Mining

This course introduces the fundamental concepts and techniques of data mining, including:

• Data collection
• Data mining algorithms
• Data mining fundamentals
• Data visualization
• Deep learning
• Machine learning
• Reinforcement learning

Admission Requirements

The admission requirements for the program include:

• A minimum cumulative GPA of 3.00 in the last 60 credit hours (last two years) of a four-year undergraduate degree
• Completion of an undergraduate degree in computer science from an accredited university
• Prerequisite knowledge in mathematics and computer science, including:
  • Calculus I and Calculus II
  • Discrete math
  • Programming knowledge in a variety of languages, including C/C++, Java, Python, and HTML
  • Data structures and algorithms, including sorting algorithms, hash tables, binary search trees, heaps, and red-black trees
  • Graph algorithms, including depth-first search, breadth-first search, minimum spanning trees, and shortest-paths

English Proficiency

If all college degrees are from a country outside of the U.S., students may need to demonstrate English proficiency through:

• TOEFL: 575 paper-based / 90 internet-based
• IELTS (academic version): 7
• PTE: 65
• Duolingo: 115

Professional Certification

The program offers professional certification opportunities in areas such as:

• AI and machine learning
• Big data
• Cybersecurity
• Software engineering

Faculty

The program is taught by award-winning faculty members in the field of computer science, including:

• National Academy of Engineering members
• National Academy of Sciences member
• National Academy of Inventors members
• National Academy of Construction members

Career Opportunities

The program prepares students for a range of career opportunities, including:

• Cybersecurity engineer
• Cybersecurity risk analyst
• Security analyst
• Security engineer
• Software development engineer

Accreditation

The program is accredited by the Computing Accreditation Commission of ABET.