Program Overview

Introduction to the M.S. in Artificial Intelligence Program

The M.S. in Artificial Intelligence at Nova Southeastern University offers a rich, distinctive, and innovative graduate educational experience. The program provides the opportunity for students to learn to develop depth in all the core concepts that build a foundation for artificial intelligence theory and practice.

Program Overview

In artificial intelligence, there has been a global workforce shortage, and demand continues to outpace the supply of talent. The global artificial intelligence workforce needs to grow at least 75% to keep up with what is needed to support and defend organizations' critical assets. This M.S. in Artificial Intelligence program at NSU prepares responsible, well-rounded graduates capable of protecting and defending artificial intelligence systems and users.

Concentrations

The program offers two concentrations:

• Cybersecurity: Students develop depth in core concepts that build a foundation for artificial intelligence theory and practice and its application to cybersecurity defense.
• Data Science: Develop an in-depth understanding of data analysis in courses that focus on data storage, data mining, data analytics, and data visualization.

Quick Facts

Curriculum

• 30 total credits (10 courses)
• Earn your degree in 1 year

Start Dates

• August (Fall)
• January (Winter)
• May (Summer)
• Preferred deadlines are one month prior to the start of the term

Delivery Options

• ONLINE
• ON-CAMPUS at the Fort Lauderdale/Davie campus

Tuition

• Estimated Total Tuition: $31,470

Why Earn Your M.S. in Artificial Intelligence at NSU?

• Fast Track: Advance your future with new 8-week courses and complete your degree in 1 year.
• STEM Designation: An integrated approach to learning in the areas of science, technology, engineering, and mathematics.

Learning Outcomes

A graduate with an M.S. in Artificial Intelligence will have the ability to:

1. Communicate artificial intelligence concepts, designs, and solutions effectively and professionally
2. Apply knowledge of artificial intelligence and computing to produce effective designs and solutions for specific problems
3. Identify, analyze, and synthesize scholarly literature relating to the field of artificial intelligence and computing
4. Implement artificial intelligence and computing solutions using software development tools, software systems, and modern computing platforms

Artificial Intelligence Curriculum

Prerequisite Courses

• MSIT 501: Foundations of Programming, Data Structures, and Algorithms (3 credits)
• CISC 501: Computer Organization and Architecture (3 credits)
• CISC 502: Mathematics in Computing (3 credits)
• CISC 503: Data Structures and Algorithms (3 credits)

Core Courses (30 credits)

• CISC 615: Design and Analysis of Algorithms (3 credits)
• CISC 680: Software Engineering (3 credits)
• CISC 662: Data Mining and Knowledge Discovery in Databases (3 credits)
• CISC 670: Artificial Intelligence (3 credits)
• MMIS 671: Data Analytics and AI (3 credits)
• MSIT 675: Deep Learning (credits not specified)

Concentration Requirements

Cybersecurity Concentration

Required Courses (12 credits, choose 4 courses):

• ISEC 615: Fundamentals of Cybersecurity (3 credits)
• ISEC 620: Applied Cryptography (3 credits)
• ISEC 640: Database Security (3 credits)
• ISEC 650: Computer and Network Forensics (3 credits)
• ISEC 660: Advanced Network Security (3 credits)

Data Science Concentration

Required Courses (12 credits, choose 4 courses):

• CISC 660: Database Management Systems (3 credits)
• CISC 664: Information Retrieval and Web Search Engine Technology (3 credits)
• CISC 672: Data Visualization (3 credits)
• CISC 681: Computer Graphics (3 credits)
• CISC 685: Interaction Design (3 credits)

No-Concentration Option

Required Courses (12 credits, choose any 4 courses from the Cybersecurity concentration, Data Science concentration, or electives):

• CISC 610: Programming Languages (3 credits)
• CISC 640: Operating Systems (3 credits)
• CISC 650: Computer Networks (3 credits)
• CISC 665: Distributed Systems (3 credits)
• CISC 668: Mobile Application Development (3 credits)
• CISC 682: Software Requirements Engineering (3 credits)
• CISC 683: Object-Oriented Design (3 credits)
• CISC 684: Software Testing and Verification (3 credits)

Additional Information

• Applicants who do not have adequate academic backgrounds may be required to take one or more of the 500-level graduate prerequisite courses during the first two terms of the program.
• Students who opt to do a thesis will replace two of the elective courses with these credits. Plans for the thesis option must be made with and approved by the program office.
• Courses at the 500 level, when required, must be completed prior to taking courses at the 600 level. All 500-level courses must be completed with a grade of B or higher to continue in the artificial intelligence degree program.