MS in Computer Science
With AI Focus
At a Glance
- 100% online coursework
- Learn from practitioners in artificial intelligence
- Access to a cutting edge virtual lab on your own laptop
- 30 credits
- No GMAT/GRE required
Enhance Your Online CS Degree with an AI Specialization
Artificial intelligence is reshaping industries and creating exciting career opportunities for those who develop the skills to work with it. For computer science professionals, the possibilities are near endless: from developing the machine learning models that power AI tools, to managing the implementation of low- or no-code AI solutions in software development teams.
Job postings that mention AI skills have salaries that are 28% higher than postings that do not mention these skills.
At Pace University, we want to give students cutting-edge skills for stronger careers and higher earning potential. That’s why we offer an optional Artificial Intelligence focus area in the online MS in Computer Science.
This master’s in computer science AI focus area equips students with advanced skills to meet the growing demand for AI specialists across industries. You should consider this focus area if you want to lead innovation in one of technology’s most transformative and rapidly expanding fields.
AI Focus Area
Core Skills
Through this focus area, students will gain expertise in:
- Artificial intelligence models and capabilities
- Grid and cloud computing
- Machine learning
- Deep learning
- Python programming
- Computing theory
- Internet and web computing
- Database systems
Get Started
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Information
To learn more about the online Master of Science in Computer Science program, fill out the fields in this form to download a free brochure. If you have any questions at any time, please contact an enrollment specialist at (914) 758-1080.
Course Structure
Below is an outline of what your master’s in computer science might look like if you choose an AI focus area. For comprehensive curriculum information, see our curriculum page.
Core Requirements (15 Credits)
This course provides an integrated survey of fundamental ideas in the areas of computer architecture; operating systems; and programming language specification and translation. The focus of this course is on the computer organization of a computer system, including the processor architecture and the memory system. In particular, we will discuss the internal representation of information, instruction set architectures and implementation techniques for computer arithmetic, control path design, and pipelining. We examine the hardware and software components required to go from a program expressed in a high-level programming language like C to the computer actually running the program. This course takes a bottom-up approach to discovering how a computer works, and introduces basic concepts of operating systems, compilers and interpreters.
This course includes the following: applications of abstraction and divide-and-conquer in computer science (hardware, software, theory), essential algorithms including searching, sorting, hashing and graphs; popular algorithms such as string machine, Map Reduce and RSA and their applications, complexity, computability, NP-hard problems, NP-complete problems, and undecided problems, and finite state automata vs. regular expressions.
This course focuses on the following: parallel computing theory, Parallel Random-Access Machines (PRAMs), Amdahl’s law for theoretical speedup limits, Petri Nets, parallel vs. distributed computing, speedup, fault-tolerance, resource-sharing, parallel architectures, data flow, instruction-level pipelining, embedded multicore systems, shared-memory multiprocessors, distributed-memory multicomputers, interconnection networks, distributed systems: client-server systems, cluster computing, computing grids, cloud computing, parallel and distributed programming with industry standard MPI (Message Passing Interface), and parallel algorithms.
This course focuses on the integrated hands-on coverage of fundamental concepts and technologies for enterprise and Internet computing. Topics include data storage, XML data specification, parsing and validation, data and language translation, networking and Web technology overview, software framework technology for controlling software system complexity, and a roadmap for the enterprise computing technologies. CS612 students need a Windows computer to complete course projects.
This course includes the following topics: database management system installation and configuration, database’s role as a middleware in system hierarchy, Entity Relationship (E-R) model for logical design, schema normalization and performance tradeoffs, database management with SQL through database console, database programming through JDBC, event-processing with triggers, efficient data processing with stored-procedures, transactions management and ACID properties, database security, and crash recovery.
Artificial Intelligence Focus (9 Credits)
This course focuses on theory and data structures and algorithms related to artificial intelligence and heuristic programming. Topics include description of cognitive processes, definition of heuristic vs. algorithmic methods, state space and problem reduction, search methods, theorem proving, natural language processing, and pattern recognition techniques.
This course introduces students to the Python programming language with an emphasis on Python’s data analytics libraries. Students will learn the fundamentals of Python and key modules including: scipy, numpy, scikit-learn, pandas, statsmodels, and matplotlib. The course covers basic language syntax, object types, variables, reading data from files, and writing to files. Building on these concepts, students will create functions, and learn how to control program flow. Students will use Python to clean and prepare data, conduct exploratory data analysis, and build predictive models.
Students will learn how to design, implement, and evaluate a pipeline for supervised classification of structured data, using a variety of Machine Learning techniques (e.g., Logistic Regression). Apply Deep Learning techniques (e.g., Convolutional Neural Networks, Recurrent Neural Networks) to classify unstructured data, including images and text. Describe important considerations for applying Machine Learning in practice.
Elective (3 Credits)
AI focus area students will choose one additional elective of their choice.
Capstone Project (3 Credits)
Students choose either a project (3 credits) or a thesis (6 credits). If AI focus area students choose a thesis, they are not required to take an additional elective.
Career Outcomes
AI skills are increasing in demand across all industries, not just technology. With business leaders, manufacturing experts, marketing professionals and more seeking to leverage AI tools in their work, computer science professionals who can develop AI-forward software technologies are in higher demand than ever. Professionals in computer science and software development roles benefit greatly from understanding how to leverage artificial intelligence. An online MS in AI can help you prepare for several careers, including the following:
| Job Title | Median Annual Salary* |
|---|---|
| Machine learning engineer | $189,200 |
| Deep learning engineer | $207,400 |
| Director of technology | $173,800 |
| Data scientist | $142,100 |
| Artificial intelligence engineer | $179,100 |
| Natural language processing (NLP) engineer | $185,100 |
*Lightcast
Frequently Asked Questions
An online MS in Computer Science gives you technical skills to take on AI challenges and solve problems directly, as well as theoretical knowledge to understand how systems work. With a focus area in AI, you can take that expertise even further and build the algorithms and machine learning models that power artificial intelligence.
Full-time students can complete the online master’s in computer science program in as little as 1.5 years. Some students choose to complete the degree on a part-time schedule so they can continue to work full time.
Computer science is about developing new software solutions and technologies to empower businesses efficiency and societal improvement. Information technology emphasizes the deployment, integration, management and security of business technologies. Information systems professionals use existing computing technologies to support efficient business operations and maximize profits.
Computer science professionals are the software engineers who work behind the scenes, empowering IT and IS professionals to do their work. Without computer science graduates, other technology professionals would not have sufficient software to do their jobs effectively.
WHY PACE?
Micro-Internships and Virtual Work Simulations
- Designed by Fortune 500 companies and other top organizations
- Explore proprietary systems and software in a self-paced, online project
Pace Online Learning Experience
- Pursue a career-focused degree with a flexible schedule
- Attend class from anywhere and save on tuition
- Learn from the same expert faculty who teach on-campus at Pace
Personal Advisement
- Gain access to a dedicated advisor from day one through graduation
- Get support customizing your academic experience and navigating the University ecosystem
New York Networking Opportunities and Online Events
- Access a variety of events and career development opportunities
- Connect with alumni and engage with industry professionals
- Benefit from online workshops designed to help you stand out to employers
Are you interested in the Pace online MS in Computer Science, but want to specialize in something different? Learn more about our customization options on our curriculum page, or begin your application now.