Mathematics

Dylan Helliwell, PhD, Chair
McLean Sloughter, PhD, Graduate Program Director

Web: https://www.seattleu.edu/scieng/math/

Objectives

Data science is the study of data-driven decision making. It has emerged as a unique discipline out of a history that draws from a wide variety of fields. Statistics and computer science are both domains which develop and implement methods for data analysis and data communication. Many fields, including, for example, biology, sociology, or marketing, then leverage these methods to answer domain-specific questions. Recent decades have seen a ballooning of both the breadth of areas in which data is collected, and the magnitude of that data. As our access to data continues to grow, there has been an increasing need for people whose primary expertise is in the process of extracting knowledge from data. Data science has emerged as a unique discipline that combines the developmental aspects of statistics and computer science with the contextual framework, application, and real-world problem solving seen in a variety of fields, to create a complete methodology for data-driven decision making, from conceptualizing a question, to data collection, cleaning, and storage, to analysis, and finally to communication.

The Master of Science in Data Science has a core curriculum based in applications and theory from statistics and computer science. These foundational courses present topics within the larger context of data science methodology to ensure that students develop the necessary skills to apply their knowledge. Consistent with the mission of Seattle University to empower leaders for a just and humane world, the program places an emphasis on ethical and legal issues in data science. To support their professional formation, students are provided many opportunities, via electives and team-based capstone projects, to gain domain-specific knowledge of how data science is utilized in a variety of fields.

Upon completion of this degree, successful students will be able to

Apply appropriate analytical and computational methods to solve real-world problems effectively.

Communicate technical information effectively to a specific audience via speech, writing, and data visualization.

Identify and propose well-reasoned means of addressing moral or ethical challenges in the practice of data science.

Exhibit constructive and inclusive collaboration and teamwork skills.

Demonstrate, by means of a capstone project, deep contextual experience of goal-setting and delivery of recommendations in at least one specific domain.

Admission Requirements

Before starting the program, students should have proficiency with the following topics:

Elementary Probability and Statistics (such as MATH 2310)
Programming course in Python (such as CPSC 1220)
Integral Calculus (such as MATH 1335)

Students seeking admission to this graduate program should contact the Mathematics Department at (206) 296-5930 or Seattle University Graduate Admissions at (206) 220-8010. See Graduate Admissions Programs for admission materials. Documents required for admission to the Master of Science in Data Science program include the following:

Completed Application for Graduate Admission
A 300 word essay on one of the following topics:

the job you would like to have after graduating;
a problem you would like to solve, or have already solved using analytical skills

Official transcripts of all post-secondary education institutions attended in the last 90 quarter/60 semester credits of the bachelor’s degree, including any transfer credits earned during this time, and any post-baccalaureate coursework
Students who have earned degrees from institutions issuing non-graded transcripts must submit ofﬁcial results from the GRE (Code 4695) or GMAT (Code 5613).
Current résumé
If English is not the applicant’s native language, official English proficiency scores meeting the University entrance requirements are necessary. See Admissions Policy (2008-01) for details.

Programs

Master of Science in Data Science