Data Science, Master of Science

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.

Courses provide focus on data acquisition, data management, data mining, data governance, data analytics, artificial intelligence, machine learning, deep learning, and programming for computing, big data systems, and mathematics and statistics needed to deliver Data Science projects, as well as maintaining the professional skills required to advance in the Data Science field. Students will emerge with the experience and leadership identity required to influence the way that Data Science is implemented and consumed in any corporation or government organization.

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 the degree to real work problems at a for-profit or a non-profit organization.

The capstone is the platform that exhibits the synthesis of student’s 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.

Program Outcomes

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

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

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

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

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

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

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

Admission Requirements

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 SOL/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.

Total Required Credits (39-59 Credits)

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/NO SQL 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 & Computing Components I	3
CS 11B	Technology & Computing Components II	3

Depth-First Preparatory Courses (20 Credits)

CS 132	Computer Science I	5
	(or)
IS 201	Fundamentals of Computing	5
CS 330	Network Communications *	5
CS 340	Operating Systems *	5
IS 360	Database Technologies *	5

Pre-Entry Requirement (0 Credit)

Students must take this course in the first quarter of enrollment. Students may take another program requirement concurrently.

CS 500

STC MS Orientation to Master's Programs *

Core Requirements (24 Credits)

DS 510	Artificial Intelligence for Data Science	3
DS 515	Data Science Overview	3
DS 520	Data Mining	3
DS 522	Data Acquisition and Analytics	3
DS 524	Data Management and Governance	3
DS 620	Machine Learning & Deep Learning	3
CS 506	Programming for Computing	3
CS 622	Discrete Math and Algorithms for Computing	3

Depth of Study: Data Science (6 Credits)

DS 623	Math & Statistics for Data Science	3
DS 625	Big Data Architectures and Systems	3

Elective (6 Credits)

Students may select two elective courses from any other disciplines within the School of Technology & Computing or complete the internship after taking three CS 650 seminar courses for their internship preparation if necessary for their skill advancement.

Seminar

Students can take three CS 650 seminar courses after taking 6 credit hours and before taking either DS 680 Data Science Internship or DS 687 Data Science Capstone. Each enrollment must be pre-approved by the Program Manager.

CS 650A	Master's Seminar I in Special Technology *	1
CS 650B	Master's Seminar II in Special Technology *	1
CS 650C	Master's Seminar III in Special Technology *	1

Internship

This internship is repeatable for credit. Each enrollment must be pre-approved by the Program Manager.

DS 680

Data Science Internship *

Capstone (3 Credits)

DS 687

Data Science Capstone *