Degree Requirements (9 courses + capstone, 33 credits)

Prerequisites

Free introductory courses are offered online for accepted candidates from undergraduate backgrounds not covering calculus, programming and statistics

Required Courses (five):

  • Database Modeling, Design and Implementation
  • Optimization Methods for Analytics
  • Probability and Statistics for Analytics
  • Information Visualization
  • Data Mining

Elective Courses:

Elective courses are offered in a variety of areas, including but not limited to the following:

  • Tabular Data Analytics
  • Modeling for Insight
  • Geospatial Systems
  • Computational/Machine Learning
  • Artificial Intelligence
  • Human Computer Interaction
  • Econometrics
  • Digital Signal Processing
  • Design of Experiments and Analysis of Data
  • Pattern Recognition
  • Stochastic Processes for Engineers
  • Strategic Project Management
  • Marketing Research Methods
  • Data Warehousing
  • Big Data Processing
  • Big Data Architecture

Capstone:

The capstone project is a course centered on a sponsored data analytics project with interdisciplinary teams. Capstone projects, depending on the project parameters, could consist of a 2-unit seminar with a 4-unit project (consistent with engineering curriculum currently offered) and/or be a mentored capstone with 6 total units. Depending on the nature of the capstone and its sponsorship, projects could be on-site with intensive fieldwork.

Course summaries can be found in the course catalog.