Data Science, MS

The Master of Science in Data Science program requires the successful completion of 12 courses to obtain a degree. These requirements cover six core courses, a leadership, project management, or data governance course, two required courses corresponding to a declared specialization, two electives, and a capstone project or thesis. A specialization may be declared as part of the application process or may be declared at any time during a student’s tenure in the program. Students also have the option of choosing a general data science curriculum with no declared specialization. There are five specializations: Analytics and ModelingAnalytics ManagementArtificial IntelligenceData Engineering, and Technology Entrepreneurship


Core Courses (8 units)

Course Title
MSDS 400-DLMath For Data Scientists
MSDS 401-DLApplied Statistics with R
MSDS 402-DLData Science and Research Practice 1
or MSDS 403-DL Data Science and Digital Transformation
MSDS 420-DLDatabase Systems and Data Preparation
MSDS 422-DLPractical Machine Learning
MSDS 460-DLDecision Analytics
MSDS 475-DLProject Management
or MSDS 480-DL Business Leadership and Communications
or MSDS 485-DL Data Governance, Ethics, and Law
MSDS 498-DLCapstone Class
or MSDS 590-DL Thesis Research

Which course should students take?

  • Students without a background in data science should select MSDS 402-DL Data Science and Research Practice
  • Students with a background in data science should select MSDS 403-DL Data Science and Digital Transformation.  Students who have at least two years’ experience in the field and have or had a title, such as data scientist, data analyst, statistician, data engineer, business analyst, etc. should select this course.

Electives (4 units)

Course Title
MSDS 410-DLSupervised Learning Methods
MSDS 411-DLUnsupervised Learning Methods
MSDS 413-DLTimes Series Analysis and Forecasting
MSDS 430-DLPython for Data Analysis
MSDS 431-DLData Engineering with Go
MSDS 432-DLFoundations for Data Engineering
MSDS 434-DLAnalytics Application Engineering
MSDS 436-DLAnalytics Systems Engineering
MSDS 440-DLReal-Time Interactive Processing and Analytics
MSDS 442-DLReal-Time Stream Processing and Analytics
MSDS 450-DLMarketing Analytics
MSDS 451-DLFinancial Machine Learning
MSDS 452-DLWeb and Network Data Science
MSDS 453-DLNatural Language Processing
MSDS 454-DLApplied Probability and Simulation Modeling
MSDS 455-DLData Visualization
MSDS 456-DLSports Performance Analytics
MSDS 457-DLSports Management Analytics
MSDS 458-DLArtificial Intelligence and Deep Learning
MSDS 459-DLKnowledge Engineering
MSDS 462-DLComputer Vision
MSDS 464-DLIntelligent Systems and Robotics
MSDS 470-DLTechnology Entrepreneurship
MSDS 472-DLManagement Consulting
MSDS 474-DLAccounting and Finance for Technology Managers
MSDS 476-DLBusiness Process Analytics
MSDS 485-DLData Governance, Ethics, and Law
MSDS 490-DLSpecial Topics in Data Science
MSDS 499-DLIndependent Study

About the Final Project

As their final course in the program, students take either a master's thesis project in an independent study format or a classroom final project class in which students integrate the knowledge they have gained in the core curriculum in a team project approved by the instructor. In both cases, students are guided by faculty in exploring the body of knowledge of data science. The master’s thesis or capstone class project count as one unit of credit.

Course Title
Choose one
Capstone Class
Thesis Research