SCHOOL OF TECHNOLOGY AND COMPUTING

Master of Science in Data Science

Enrollment 

All Quarters

Course Format

Online

Total Credits

39-59

Make Data-Driven Decisions

The Master of Science in Data Science (MSDS) is an inherently interdisciplinary field that requires effective integration of a domain to provide data and a context for its exploration, statistics, and computer science.  The Program follows the Body of Knowledge of the Association for Computing Machinery (ACM) Data Science. This degree prepares graduates for a myriad of opportunities to work in and advance their Data Science careers. The graduates can understand their data and know what they can expect to learn from the data. They can select the right tools and techniques to get the job done. 

Program Overview

Courses focus on data mining, analytics, machine learning, cloud computing, big data, and natural language processing. These courses drive data science projects and deliver the professional technical skills required to advance in the field. Students will emerge with the experience and leadership necessary to influence how data science is implemented and consumed in organizations.     

The Depth of Study (DOS) sequence prepares students to demonstrate expertise in a specific area. The elective courses allow students to expand their interests in other disciplines. The internship provides students with a vehicle to apply what they have learned in their degree to real-world problems at a for-profit or a non-profit organization.   

The capstone is the platform that exhibits the synthesis of students’ academic accomplishments and experiential internship learnings. Under the guidance of an advisor, the capstone can be a project, research paper, thesis or poster, and a public presentation designed to demonstrate mastery. 

Learning Outcomes

The Master of Science in Data Science will prepare students to:

Integrate a foundational knowledge of all areas of advanced data science (General Data Science Knowledge).

Apply fundamental principles and practices of advanced data science (Data Science Principles and Practices)

Apply critical and ethical thinking to solve problems in advanced data science (Critical and Ethical Thinking).

Evaluate data to inform decisions and solve problems in advanced data science (Quantitative Literacy).

Create the ability to develop and express ideas while applying a variety of delivery models, genres, and styles (Communication).

Collaborate effectively on diverse teams to accomplish a common goal (Collaboration).

Admissions Requirement

A bachelor’s degree from an accredited university is required to enroll in this program.

In addition to City University of Seattle’s graduate admission requirements, found under Admissions in the catalog menu, applicants to this program must also meet the following requirements: 

  • An earned bachelor’s degree and evidence of completion of undergraduate courses or their equivalent in: 
    • Equivalency of 5-quarter hour credits at the intermediate level in at least one computer programming language; and 
    • Equivalency of 5-quarter hour credits in networking (TCP/IP from physical through applications layers); and 
    • Equivalency of 5-quarter hour credits of data management, including basic database design and SQL/NoSQL Queries; and 
    • Equivalency of 5-quarter hour credits of operating systems, including OS theory, process management, and memory management; OR 
  • An earned bachelor’s degree and successful completion of CityU’s Undergraduate Certificate in Foundations of Systems Development. 

Preparatory Courses (6 or 20 credits)

Preparatory courses may be required for students entering the MSDS degree program without sufficient related experience in programming, SQL/NoSQL databases, networking, and operating systems. The breadth-first preparatory courses are recommended for students who want to study a broad spectrum of all topics within 1 quarter. The depth-first preparatory courses are recommended for students who want to study each topic in depth. Please see the program admissions criteria in the City University of Seattle catalog for specific information. 

Breadth-First Preparatory Courses (6 Credits)

CS 11A Technology and Computing Components I
CS 11B Technology and Computing Components II

OR

Depth-First Preparatory Courses (20 Credits)

CS 132 Computer Science I *  OR  IS 201 Fundamentals of Computing
CS 330 Network Communications *
CS 340 Operating Systems *
IS 360 Database Technologies *

Core Requirements

Total Required Credits: 39 

Core Requirements (24 Credits)

AI 520 Natural Language Processing for Artificial Intelligence
DS 520 Data Mining
DS 522 Data Acquisition and Analytics
DS 524 Data Management and Governance
DS 620 Machine Learning and Deep Learning
CS 506 Programming for Computing
CS 519 Cloud Computing
CS 622 Discrete Math and Algorithms for Computing

Depth of Study (6 Credits)

Depth of Study: Data Science 
DS 623 Math and Statistics for Data Science 
DS 625 Big Data Architectures and Systems

Elective (6 Credits) 

Students may select two elective courses from any graduate courses within the School of Technology & Computing OR complete the internship.

Internship

DS 680 Data Science Internship * (3 credits)
This course is repeatable for credits.  Each enrollment must be pre-approved by the Program Manager.

Capstone (3 Credits) 

DS 687 Data Science Capstone 

Career Opportunities

Program Manager

Brian Maeng Ph.D.
maengjooyol@cityu.edu

With over 90 % of organizations already using data and analytics to steer business decisions and a global Big Data & Analytics market now valued above $348 billion, the need for experts who can turn raw data into strategic advantage is accelerating every year.

Upon graduation, Graduates of CityU’s Master of Science in Data Science (MSDS) can step confidently into roles such as:

  • Data Scientist: Frame business questions as analytical problems, build statistical & machine-learning models, and translate findings into actionable recommendations.
  • Data Engineer: Architect and maintain high-throughput data pipelines, lakehouses, and real-time streaming platforms that deliver trustworthy, governed data at scale.
  • Machine-Learning Engineer (Data Focus): Operationalize models—managing feature stores, automating retraining, and deploying resilient, low-latency inference services in cloud or edge environments.
  • Analytics Engineer / BI Developer: Transform raw datasets into analysis-ready tables, craft interactive dashboards, and enable self-service insights for decision-makers across the enterprise.
  • Data Product Manager: Define vision, roadmap, and success metrics for data-centric products, balancing user needs, technical feasibility, and regulatory constraints.
  • Data Governance & Ethics Specialist: Establish policies for data quality, privacy, and compliance; monitor bias and fairness to ensure responsible, transparent use of data assets.
 

Our graduates consistently secure positions at the forefront of innovation, with alumni contributing to renowned companies such as Adobe, AWS, Google, Meta, and Microsoft, among others. They apply their skills to transform various industries, including healthcare (AI diagnostics), manufacturing (predictive maintenance), education (personalized learning AI), finance (algorithmic trading), green energy (smart grid optimization), and the broader technology sector, where they drive the next wave of innovation.

Click HERE for admissions and tuition information.

Click HERE for complete program catalog.

Updated: 7/30/2025