M.S. in Data Analytics and Visualization

Overview

The M.S. in Data Analytics and Visualization curriculum equips students with the full spectrum of skills modern data professionals need, from the technical to the strategic, preparing graduates to turn complex data into decisions that drive real organizational impact. Students develop fluency in structured data management, analytics programming in high-level languages, and cloud-based distributed data systems, while also mastering the mathematical foundations in probability, statistics, and linear algebra that separate analysts who understand their models from those who simply run them. Courses in Data Science bring predictive techniques like classification, regression, clustering, and natural language processing within reach, and the AI Product Studio prepares students to design and deploy AI-augmented analytics pipelines — including LLM-powered tools, RAG-based knowledge retrieval, and agentic workflows — that represent the cutting edge of how analytics is practiced today.

Equally distinctive is the program's emphasis on context and communication. Courses in Data-Driven Organizations, Talent Analytics, and Data Product Design teach students to frame analyses within business strategy and customer value creation, ensuring their work gets adopted, not just admired. Visual Design and Storytelling builds the narrative skills to make data compelling to any audience, while Project Management prepares graduates to lead cross-functional initiatives from scoping through delivery. The program culminates in a capstone where students integrate these competencies into portfolio-ready projects, demonstrating end-to-end analytical capability. Graduates emerge as versatile, forward-thinking professionals who are equally at home designing data architecture, building predictive models, and presenting insights to an executive team.

The master's in data analytics can be completed full time in just 15 months or part time at a pace that makes sense for you. Review degree requirements below and download course descriptions here.

Course Descriptions

Degree Requirements 

To earn the M.S. in Data Analytics and Visualization, you must complete 21 credits of required foundational courses, including a capstone project as well as nine credits of elective courses, which may include an internship. Courses are 3 credits, unless otherwise noted.

Core Requirements (21 credits) 

• DAV 5000 Business Modeling and Data Analysis
• DAV 5100 Structured Data Management
• DAV 5200 Visual Design and Storytelling
• DAV 5300 Computational Math and Statistics
• DAV 5400 Analytics Programming
• MAN 5580 Project Management
• DAV 6500 Capstone

Electives (9 credits)

• AIM 5010  AI Product Studio
• DAV 6000 Talent Analytics
• DAV 6050 Data-Driven Organizations
• DAV 6100 Information Architectures
• DAV 6150 Data Science
• DAV 6200 Data Product Design
• DAV 6300 Special Topics (1-3 credits)
• DAV 6400 Internship (1-3 credits)
• DAV 6450 Independent Study (1-3 credits) 

Note: Recent special topics courses include Recommendation Systems, Automated Machine Learning, Cloud Computing and Experimental Optimization. Qualified Data Analytics and Visualization students often take electives in other graduate programs, including marketing and artificial intelligence. Electives offerings will vary each semester, so some choices will not be available for a particular cohort.