Data Science, MS Analytics and Modeling Specialization

In the world of data science, the analysts and modelers specialize in testing real-world predictions about data. Data analysts and modelers conduct research and take complex factors into account to build predictive models and create forecasts upon which data-driven decisions can be made. With a focus on traditional methods of applied statistics, this specialization prepares data scientists to utilize algorithms for predictive modeling and analytics, developing models for marketing, finance, and other business applications.


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.

Specialization Courses (4 units)

Course Title
MSDS 410-DLSupervised Learning Methods
MSDS 411-DLUnsupervised Learning Methods
Any two electives
Times Series Analysis and Forecasting
Python for Data Analysis
Data Engineering with Go
Foundations for Data Engineering
Analytics Application Engineering
Analytics Systems Engineering
Real-Time Interactive Processing and Analytics
Real-Time Stream Processing and Analytics
Marketing Analytics
Financial Machine Learning
Web and Network Data Science
Natural Language Processing
Applied Probability and Simulation Modeling
Data Visualization
Sports Performance Analytics
Sports Management Analytics
Artificial Intelligence and Deep Learning
Knowledge Engineering
Computer Vision
Intelligent Systems and Robotics
Technology Entrepreneurship
Management Consulting
Accounting and Finance for Technology Managers
Business Process Analytics
Data Governance, Ethics, and Law
Special Topics in Data Science
Independent 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