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33 Credit Data Analytics Program

In this Section

The 33 credits of the MS degree program consist of five three-credit core graduate courses, four three‐credit graduate elective courses, and a six-credit capstone course based on a sponsored project work. The core courses are:

IA 510 - Database Modeling, Design and Implementation
IA 520 - Optimization Methods for Analytics
IA 530 - Probability and Statistics for Analytics
IA 640 - Information Visualization
IA 650 - Data Mining

Some of the core courses may be waived if the students can demonstrate that their previous undergraduate or graduate coursework contains equivalent material. In those cases, students will be required to take a greater number of elective courses to satisfy 33-credit program requirement.

Elective courses are offered in a variety of areas and they include but are not limited to the following:


IA 605 - Data Warehousing
IA 505 – Tabular Data Analytics
IA 630 - Modeling for Insight (pre-requisite: Tabular Data Analytics) 
IA 670 - Geospatial Systems
CS 549 - Computational/Machine Learning
CS 551 - Artificial Intelligence
CS 559 - Human Computer Interaction
EC 611- Econometrics
EE 501 - Digital Signal processing
ES 505 - Design of Experiments and Analysis of Data
EE 574 - Pattern Recognition
ME 529, Stochastic Processes for Engineers
OM 680 - Strategic Project Management
EC 611 - Econometrics
MK 696 – Marketing Research Methods

IA690 - Capstone Project is a course centered on a sponsored data analytics projects with interdisciplinary teams. Capstone projects, depending on project parameters could consist of a 2 unit seminar w/4 unit project (consistent with engineering curriculum as currently offered) and/or be a mentored capstone of 6 total units. Depending on the nature of the capstone and its sponsorship, projects could be on-site fieldwork intensive.