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M.S. Data Analytics Engineering

The Masters in Data Become one of a growing number of professionals who know how to extract information from the flood of ‘big data' that is being collected. Study topics such as data mining, information technology, statistical models, predictive analytics, optimization, risk analysis, and data visualization. The program blends sophisticated methodologies from a variety of disciplines as well as practical training within a single degree.

 

The Breakthroughs and Challenges of Building and Using Big Data Solutions in Public Sector Agencies: A presentation by David Knox, Oracle Vice President, National Security Group

 

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Admission Requirements

Applicants must have completed a baccalaureate degree from an accredited program with a reputation for high academic standards and an earned GPA of 3.00 or better in their 60 highest-level credits. While no specific undergraduate degree is required, a background in engineering, business, computer science, statistics, mathematics, or information technology, is desirable, or alternatively strong work experience with data or analytics may be used.

For some of the concentrations there are additional admission requirements. These are listed below in the descriptions of the individual concentrations.

In addition to fulfilling Mason's admission requirements for graduate study, applicants for the masters in data analytics engineering must:

  • Provide three letters of recommendation, preferably from academic references or references in industry or government who are familiar with the applicant's professional accomplishments.
  • Provide a detailed statement of career goals and professional aspirations.
  • Complete a self-evaluation form.
  • If their native language is not English, students must earn a minimum TOEFL score of 575 for the paper-based exam or 230 for the computer-based exam.

Degree Requirements (30 credits)

Core Courses (15 credits)

The following core course work covers the basic elements of data analytics program at the graduate level. Students must select 15 credits from the courses listed below.

  • AIT 580 - Analytics: Big Data to Information
  • CS 504 - Principles of Data Management and Mining  or
  • CS 584 - Theory and Applications of Data Mining (Data Mining concentration only)
  • OR 531 - Analytics and Decision Analysis
  • STAT 515 - Applied Statistics and Visualization for Analytics  or
  • STAT 554 - Applied Statistics (Statistics for Analytics concentration only)
  • DAEN 690 - Data Analytics Project

Concentrations (15 credits)

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If you are interested in enrolling in the MS in Data Analytics Engineering, please complete the following form.

Data Analytics Engineering Interest Form
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