Program Summary

MSDS Program Summary

The Master of Science in Data Science program offers a curriculum based on theoretical foundations and practical applications. The core curriculum for the data science degree is based in applications and theory from statistics, computer science and data analysis. These foundational courses will present topics within the larger context of data science methodology to ensure that students develop the necessary skills to apply their knowledge. 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. 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.

The 45-credit curriculum consists of ten required 3-credit courses, three 3-credit elective courses, and two 3-credit capstone courses. Electives include such topics as time series modeling, machine learning, geographic information systems, text processing, web analytics, and social media analytics. Students will be able to complete the program on a two-year (six quarter) or longer schedule.

Courses for the Seattle U MSDS program are taught in the evenings and the program is designed to accommodate full-time students as well as working professionals.

Industry-Partnered Capstone Project

The data science degree culminates in a two-quarter capstone experience. Students will integrate lessons from multiple courses to solve a problem for a real-world enterprise.

Students will work in small teams to define and carry out a data analysis project from beginning to end. This will require the planning and execution of a real-world application of data science for an industry partner. Key steps may include:

  • Formalizing a question
  • Finding data sources
  • Determining the method of analysis
  • Implementing the analytical procedure
  • Communicating the results to the client


Sample Course Plan

Quarter 1 - Fall

  • DATA 5100 - Foundations of Data Science
  • DATA 5300 - Applied Statistical Inference and Experimental Design
  • CPSC 5070 - Programming for Data Science

Quarter 2 - Winter

  • CPSC 5071 - Data Management for Data Science
  • DATA 5321 - Statistical Machine Learning I
  • DATA 5111 - Probability for Data Science

Quarter 3 - Spring

  • DATA 5120 - Data Science, Law and Ethics
  • DATA 5322 - Statistical Machine Learning II
  • CPSC 5330 - Big Data Analytics

Quarter 4 - Fall

  • DATA 5310 - Data Visualization
  • Elective: Advanced Foundations of Data Science course

Quarter 5 - Winter

  • Elective 1
  • DATA 5901 - Capstone 1

Quarter 6 - Spring

  • DATA 5901 - Capstone 2
  • Elective 2

 More Information: Course Catalog

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