George Mason University
George Mason University Mason
George Mason University

Data Analytics Engineering, MS

Program Overview

The Masters in Data Analytics Engineering is designed to provide students with an understanding of the technologies and methodologies necessary for data-driven decision-making. Topics cover data mining, information technology, statistical models, predictive analytics, optimization, risk analysis, and data visualization. Aimed at students who wish to become data scientists and analysts in finance, marketing, operations, business intelligence and other information intensive groups generating and consuming large amounts of data, the program also has wider applications, including concentrations in digital forensics, financial engineering, and business analytics.

Highlights

Please see the University Catalog for complete information on program requirements and policies. Additional specifications may apply.

Admission Requirements 

Applicants must have completed a baccalaureate degree from a regionally 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 each 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 must provide:

  • Three letters of recommendation, preferably from academic references or references in industry or government who are familiar with the applicant’s professional or academic accomplishments.
  • Résumé.
  • Detailed statement of career goals and professional aspirations.
  • Completed self-evaluation form.
  • If the applicant’s native language is not English, proof of English competency with a minimum TOEFL score of 575 for the paper-based exam or 230 for the computer-based exam.

Degree Requirements (30 credits)

Core Courses

The following core course work covers the basic elements of data analytics at the graduate level.

Concentrations 

Students can elect a concentration that corresponds to a specialized technical area. Students not interested in a concentration can work with an advisor to select 15 credits of electives from among courses allowed in all the concentrations.

  • Applied Analytics 
  • Bioengineering
  • Business Analytics
  • Data Mining 
  • Digital Forensics 
  • Financial Engineering
  • Predictive Analytics 
  • Statistics for Analytics 

Concentration in Applied Analytics

Focuses on the practical elements of adapting big data approaches to common analytic problems and government operations.

Students entering the program should have completed the following George Mason undergraduate courses or their equivalents:

Required Concentration Courses

Concentration in Bioengineering

Bioengineering, whether it is mapping the human genome or computer aided diagnosis, is an exercise in data analytics.

Students entering the program should have completed the following George Mason undergraduate courses or their equivalents:

Required Concentration Courses

Concentration in Business Analytics

Students entering the program must have successfully completed STAT 515 or STAT 554 with a grade of B or better. 

Required Concentration Courses 

  • GBUS 720 - Marketing Analytics (3 credits)
  • GBUS 721 - Marketing Research (3 credits)
  • GBUS 738 - Data Mining for Business Analytics (3 credits)
  • GBUS 739 - Advanced Data Mining for Business Analytics (3 credits)
  • GBUS 744 - Fraud Examination (3 credits)

Concentration in Data Mining

Aimed at students who are interested in understanding data mining, advanced database systems, MapReduce programming, pattern recognition, decision guidance systems, and Bayesian inference as they relate to data analytics.

Students entering the program should have completed the following George Mason undergraduate courses or their equivalents:

Required Concentration Courses

Concentration in Digital Forensics 

Deals with the process of acquiring, extracting, integrating, transforming, and modeling data with the goal of deriving useful information that is suitable for presentation in a court of law. Digital forensics is a key component in criminal, civil, intelligence, and counter-terrorism matters. Students will be able to apply data analytics to such areas as digital media, intercepted (network) data, mobile media, unknown code, and leverage that analysis in order to determine, intent, attribution, cause, effect, and context.

Students entering the program should have completed the following George Mason undergraduate courses or their equivalents:

Required Concentration Courses

Concentration in Financial Engineering:  

The concentration emphasizes both analytical and practical aspects of financial and econometric data analytics. Students are expected to demonstrate proficiency in several quantitative modeling disciplines. Students are also expected to understand issues relevant to practical aspects of investment and hedging decision making, derivative valuation, and risk analysis. The students will learn the techniques to analyze large financial and economic data to derive meaningful knowledge, which will be useful for developing effective business and risk mitigation strategies and making sound financial, marketing, and investment decisions. The concentration prepares students for careers in business analytics with a focus on practical applications in financial operations, investment, and risk mitigation strategy development. 

Students entering the program must submit evidence of:

  • Satisfactory completion of courses in calculus, applied probability and statistics, and a scientific programming language.
  • Familiarity with analytical modeling software, such as spreadsheets or math packages.

Required Concentration Courses 

Concentration in Predictive Analytics

The ultimate goal of analytics of Big Data is to derive value by suggesting effective actions for the future. Predictive analytics focuses on the methods for deciding on the best course of action, taken into account possible constraints and risks. The concentration will provide students with skills that drive effective decision making and optimization. Students will learn the techniques to analyze both structured and unstructured data to derive meaningful knowledge, which will be useful for developing effective strategies and making optimal decisions.

The concentration emphasizes both analytical and practical aspects of predictive analytics. Students are expected to master the practical aspects of modeling and methods for optimization. Students are also expected to demonstrate proficiency in decision making, design of decision support systems, and risk analysis. The program prepares students for careers in big data analytics with a focus on strategic decision making in practical applications including financial engineering, health care, transportation, and intelligence

Students entering the program should have completed the following George Mason undergraduate courses or their equivalents:

Required Concentration Courses

Concentration in Statistics for Analytics

Provides students with skills necessary for gaining insight from data. Enables students to evaluate large data-sets from a rigorous statistical perspective, including theoretical, computational, and analytical techniques. Emphasis will be placed on developing deep analytical talent in the two areas of statistical modeling and data visualization. “Big Data” are well-known to encompass high levels of uncertainty and complex interactions and relationships. To gain knowledge from these data and hence inform decisions, elucidation of the core interactions and relationships must be done in a manner that acknowledges uncertainties in order to both minimize false signals and maximize true discoveries. Statistical modeling does exactly this – it accounts for uncertainty while identifying relationships. Visualization is often a critical component of modeling, but visualization also stands alone as an important tool for presentation of information, decision analysis, and process improvement.

Students entering the program should have completed the following George Mason undergraduate courses or their equivalents: 

Required Concentration Courses

 

This information is being provided here for your planning purposes only. For official catalog information, please refer instead to the official George Mason University Catalog Website at http://catalog.gmu.edu.

Opportunities

Graduates with a master’s degree in data analytics engineering are part of a new class of engineers that deploy an interdisciplinary approach of statistical science, computer science, systems analytics, and another field of study such as business, operations research, geoscience, or bioscience. These specialized engineers build the structures that work to contain and organize gigantic fields of data so that it can be used to predict consumer behaviors, social trends such as extremism, disease threats, and factors influencing and influenced by climate change. Mason’s graduates benefit from our extensive history in sociological research, information technology, and global studies, which lends this program its unique strength. The employment outlook for this field is new and growing rapidly.

This information is being provided here for your planning purposes only. For official catalog information, please refer instead to the official George Mason University Catalog Website at http://catalog.gmu.edu.
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