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Graduate Certificate in Data Analytics


Data analytics (i.e., the process of acquiring, extracting, integrating, transforming, and modeling data with the goal of deriving useful information) is becoming an important quantitative methodology in a wide variety of applications. The need for data analytics is due to the massive accumulation of "Big Data" in all industries to include but not limited to healthcare, finance, government (federal, state, and local), and cyber defense.

This certificate program provides a broad overview of the end-to-end value chain for Big Data Analytics, from the capture and management of the data, through the analytics that harness the data to create value. The program is designed to provide a framework for the methodologies for organizing and integrating disparate data, analyzing and visualizing the integrated data, and determining what decisions or actions should be taken to generate value from the data. The program is comprised of 12 credits of required coursework.

The certificate is intended for students who are interested in addressing the challenge of transforming the massive data arising in applications such as business analytics, cyber defense/forensics, energy, finance, genomics, healthcare, intelligence, law enforcement, or transportation, into meaningful information. The program is intended for those who work in areas where applications of big data may arise.

Admission Requirements

Applicants should have an undergraduate degree from an accredited institution with a GPA of at least 3.00 in their last 60 credits of study. While no specific undergraduate degree is required, a background in in engineering, business, computer science, math, or information technology, is desirable, or alternatively strong work experience with data or analytics may be used.

Certificate Requirements

The following four courses (12 credits) must be completed.  A total GPA of 3.0 is required with no more than 1 grade of C.

  • AIT 580 Analytics: Big Data to Information
  • STAT 515 Applied Statistics & Visualization for Analytics
  • CS 504 Principles of Data Management and Mining
  • OR 531 Analytics & Decision Analysis

For more information, contact Robert Osgood.