The MS in Data Analytics Engineering is designed to provide students with an understanding of the technologies and methodologies necessary for data-driven decision-making. Students study topics such as data mining, information technology, statistical modeling, predictive analytics, optimization, risk analysis, and data visualization. It is aimed at students who wish to become data scientists and analysts in finance, marketing, operations, business/government intelligence and other information intensive groups generating and consuming large amounts of data.

Admissions

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.  DAEN 500 Data Analytics Fundamentals may be required for students without a basic foundation in Data Analytics.

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

In addition to fulfilling Mason's admission requirements for graduate study, applicants must provide:

  • Two letters of recommendation, preferably from academic references or references in industry or government who are familiar with the applicant's professional or academic accomplishments.
  • Resume.
  • 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.

Banner Code: VS-MS-DAEN

Degree Requirements

Total credits: 30

Core Courses

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

AIT 580Analytics: Big Data to Information3
CS 504Principles of Data Management and Mining 13
or CS 584 Theory and Applications of Data Mining
DAEN 690Data Analytics Project3
OR 531Analytics and Decision Analysis3
STAT 515Applied Statistics and Visualization for Analytics 23
or STAT 554 Applied Statistics I
Total Credits15
1

CS 504 Principles of Data Management and Mining (for all concentrations except Data Mining) or CS 584 Theory and Applications of Data Mining (for the Data Mining concentration only)

2

STAT 515 Applied Statistics and Visualization for Analytics (for all concentrations except Statistics for Analytics) or STAT 554 Applied Statistics I (for the Statistics for Analytics concentration only)

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.

Concentration in Applied Analytics (APAN)

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

Additional Admission Requirements

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

IT 106Introduction to IT Problem Solving Using Computer Programming3
MATH 108Introductory Calculus with Business Applications (Mason Core)3
STAT 250Introductory Statistics I (Mason Core)3
Required Concentration Courses
All students are required to take one fundamental course:3
Database Management Systems
In addition, students in this concentration may choose four courses from the following list: 12
Applications of Metadata in Complex Big Data Problems
Big Data Essentials
Determining Needs for Complex Big Data Systems
Knowledge Mining from Big-Data
Information: Representation, Processing and Visualization
Data Analytics Research Project
Total Credits15

Concentration in Bioengineering (BIOE)

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

Additional Admission Requirements

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

BENG 320Bioengineering Signals and Systems3
MATH 113Analytic Geometry and Calculus I (Mason Core)4
MATH 114Analytic Geometry and Calculus II4
MATH 213Analytic Geometry and Calculus III3
MATH 214Elementary Differential Equations3
STAT 346Probability for Engineers3

Note:

Students with some deficiencies in preparation may be admitted provisionally pending completion of foundation courses in mathematics or computer science. Undergraduate credit earned for this purpose may not be applied toward the graduate degree.

Required Concentration Courses
BENG 501Bioengineering Research Methods3
BENG 551Translational Bioengineering3
ECE 528Introduction to Random Processes in Electrical and Computer Engineering3
ECE 535Digital Signal Processing3
Select one from the following:3
Neural Engineering
Medical Imaging
Introduction to Digital Image Processing (DIP)
Advanced Biomechanics
Advanced Biomedical Signal Processing
Data Analytics Research Project
Total Credits15

Concentration in Business Analytics (BUSA)

Additional Admission Requirements

Students entering the program must have successfully completed STAT 515 Applied Statistics and Visualization for Analytics or STAT 554 Applied Statistics I with a grade of B or better.

Required Concentration Courses
GBUS 720Marketing Analytics3
GBUS 721Marketing Research3
GBUS 738Data Mining for Business Analytics3
GBUS 739Advanced Data Mining for Business Analytics3
GBUS 744Fraud Examination3
Total Credits15

Concentration in Data Mining (DTM)

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.

Additional Admission Requirements

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

CS 310Data Structures3
CS 330Formal Methods and Models3
CS 367Computer Systems and Programming4
CS 465Computer Systems Architecture3
MATH 125Discrete Mathematics I (Mason Core)3

Note:

Students with some deficiencies in preparation may be admitted provisionally pending completion of foundation courses in mathematics or computer science. Undergraduate credit earned for this purpose may not be applied toward the graduate degree.

Required Concentration Courses
CS 657Mining Massive Datasets with MapReduce3
Select four from the following: 112
Database Systems
Introduction to Artificial Intelligence
Advanced Database Management
Data Mining on Multimedia Data
Pattern Recognition
Advanced Pattern Recognition
Machine Learning
Decision Guidance Systems
Data Analytics Research Project
Web Search Engines and Recommender Systems
Database Programming for the World Wide Web
Bayesian Inference and Decision Theory
Total Credits15
1

Note: all prerequisites must be met.

Concentration in Digital Forensics (DFOR)

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.

Additional Admission Requirements

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

Computer Operating Systems
IT 342Operating Systems Fundamentals3
Computer Networking
IT 441Network Servers and Infrastructures3
IT 341Data Communications and Network Principles3
IT 445Advanced Networking Principles3
or TCOM 515 Internet Protocol Routing: Lecture and Laboratory Course

Note:

Students with some deficiencies in preparation may be admitted provisionally pending completion of foundation courses in mathematics or computer science. Undergraduate credit earned for this purpose may not be applied toward the graduate degree.

Required Concentration Courses
CFRS 500Introduction to Forensic Technology and Analysis3
CFRS 660Network Forensics3
Select three from the following:9
Digital Forensics Analysis
Digital Media Forensics
Operations of Intrusion Detection for Forensics
Incident Response Forensics
Independent Reading and Research
Malware Reverse Engineering
Mobile Device Forensics
Registry Forensics - Windows
Mac Forensics
Penetration Testing in Computer Forensics
Digital Warfare
Advanced Topics in Computer Forensics
Data Analytics Research Project
Total Credits15

Concentration in Financial Engineering (FNNE)

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.

Additional Admission Requirements

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

CS 112Introduction to Computer Programming (Mason Core)4
MATH 113Analytic Geometry and Calculus I (Mason Core)4
STAT 344Probability and Statistics for Engineers and Scientists I3
Required Concentration Courses
SYST/OR 538Analytics for Financial Engineering and Econometrics3
SYST/OR 588Financial Systems Engineering I: Introduction to Options, Futures, and Derivatives3
SYST/OR 688Financial Systems Engineering II: Derivative Products and Risk Management3
Select two from the following:6
Data Analytics Research Project
Applied Predictive Analytics
Decision and Risk Analysis
Bayesian Inference and Decision Theory
Judgment and Choice Processing and Decision Making
Practical Optimization
Stochastic Processes
Total Credits15

Concentration in Health Data Analytics (HDAN)

Required Concentration Courses:
HAP 720Health Data Integration3
HAP 725Statistical Process Control in Healthcare3
HAP 780Data Mining in Health Care3
or HAP 880 Advanced Health Data Mining
Select two from the following:6
Data Analytics Research Project
Health Data: Vocabulary and Standards
Advanced Statistics in Health Services Research I
Health Care Decision Analysis
Medical Decision Making and Decision Support Systems
Advanced Statistics in Health Services Research II
Comparative Effectiveness Analysis using Observational Data
Total Credits15

Concentration in Predictive Analytics (PRAN)

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.

Additional Admission Requirements

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

CS 112Introduction to Computer Programming (Mason Core)4
MATH 113Analytic Geometry and Calculus I (Mason Core)4
STAT 344Probability and Statistics for Engineers and Scientists I3
Required Concentration Courses
OR 604Practical Optimization3
SYST 542Decision Support Systems Engineering3
SYST 568Applied Predictive Analytics3
or OR 568 Applied Predictive Analytics
SYST 573Decision and Risk Analysis3
Select one from the following:3
Data Analytics Research Project
Sports Analytics
Statistical Graphics and Data Exploration I
Complex Systems Engineering Management
Heterogeneous Data Fusion
Bayesian Inference and Decision Theory
Metaheuristics for Optimization
Metaheuristics for Optimization
Total Credits15

Concentration in Statistical Analytics (STLA)

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.

Additional Admission Requirements

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

MATH 203Linear Algebra3
MATH 213Analytic Geometry and Calculus III3
STAT 346Probability for Engineers3
or MATH 351 Probability
Required Concentration Courses
STAT 544Applied Probability3
STAT 554Applied Statistics I3
And three courses from the following:9
Data Analytics Research Project
Applied Statistics II
Multivariate Statistical Methods
Statistical Graphics and Data Exploration I
Statistical Learning and Data Analytics
Total Credits15

BS (selected)/Data Analytics Engineering, Accelerated MS

Overview

Qualified undergraduate students have the option of obtaining an accelerated Data Analytics Engineering, MS with a concentration in predictive analytics. 

For more detailed information, see AP.6.7 Bachelor's/Accelerated Master's Degrees. For policies governing all graduate degrees, see AP.6 Graduate Policies.

Admission Requirements

While no specific undergraduate degree is required, Mason undergraduate students majoring in systems engineering or any other engineering, business, computer science, statistics, mathematics, or information technology may apply to this option if they have earned 90 undergraduate credits with an overall GPA of at least 3.30.

For the predictive analytics concentration, students 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.

Accelerated Option Requirements

Students must complete all credits that satisfy requirements for the BS and MS programs, with six credits overlap chosen from the courses in the following table. For BS candidates, these graduate courses replace the corresponding undergraduate courses listed. The undergraduate version of these courses may not be applied toward the MS degree.

Undergraduate Graduate  
SYST 473 SYST 573 Credit may not be received for both courses.
OR 441 OR 541 Credit may not be received for both courses.

For the predictive analytics concentration, any other 500-level course may be applied to both the undergraduate and graduate degrees with approval of the advisor and SEOR department chair.

OR 541 Operations Research: Deterministic Models will substitute for the OR 531 Analytics and Decision Analysis core requirement in the MS DAE program.

Degree Conferral

Students must apply the semester before they expect to complete the BS requirements to have the BS degree conferred. In addition, at the beginning of the student's final undergraduate semester, students must complete a Bachelor's/Accelerated Master's Transition form that is submitted to the Office of the University Registrar and the VSE Graduate Admissions Office. At the completion of MS requirements, a master's degree is conferred.

Applied Computer Science, BS/Data Analytics Engineering, Accelerated MS

Overview

Highly-qualified students in the Applied Computer Science, BS have the option of obtaining an accelerated Data Analytics Engineering, MS.

For more detailed information, see AP.6.7 Bachelor's/Accelerated Master's Degrees. For policies governing all graduate degrees, see AP.6 Graduate Policies.

Admission Requirements

Students in the Applied Computer Science, BS program may apply to this option if they have earned 90 undergraduate credits with an overall GPA of at least 3.30. Students must have successfully completed:

CS 310Data Structures3
CS 330Formal Methods and Models3
CS 367Computer Systems and Programming4
Total Credits10

Accelerated Option Requirements

Students must complete all requirements for the BS and MS programs, with 6 credits overlap.

Students must register for 6 credits of CS 500-level basic courses in place of the corresponding CS 400-level courses required for the undergraduate degree requirements. Specifically, students in all concentrations of the Applied Computer Science, BS program must register for:

CS 584Theory and Applications of Data Mining3
Total Credits3

Students in the Software Engineering and Bioinformatics concentrations of the Applied Computer Science, BS program must also register for:

CS 550Database Systems3
Total Credits3

Students in the Computer Game Design and Geography concentrations of the Applied Computer Science, BS program must also register for one of the following courses:

CS 550Database Systems3
CS 580Introduction to Artificial Intelligence3

Note:

For students in the Computer Game Design and Geography concentrations of the Applied Computer Science, BS program, one of the 500 level courses will count as an elective towards their undergraduate degree.

Students are permitted to take additional graduate basic courses in their undergraduate programs. In such cases, those classes cannot be counted toward requirements for the MS.

Degree Conferral

Students must apply the semester before they expect to complete the BS requirements to have the BS degree conferred. In addition, at the beginning of the student's final undergraduate semester, students must complete a Bachelor's/Accelerated Master's Transition form that is submitted to the Office of the University Registrar and the VSE Graduate Admissions Office. At the completion of MS requirements, a master's degree is conferred.

Bioengineering, BS/Data Analytics Engineering, Accelerated MS

Overview

Highly-qualified students in the Bioengineering, BS have the option of obtaining an accelerated Data Analytics Engineering, MS with a concentration in Bioengineering.

Students in an accelerated degree program must fulfill all university requirements for the master's degree. For policies governing all graduate degrees, see AP.6 Graduate Policies.

Admission Requirements

Students in the Bioengineering, BS program may apply to this option if they have earned 95 undergraduate credits with an overall GPA of at least 3.30. Students must have successfully completed CS 222 Computer Programming for Engineers and BENG 320 Bioengineering Signals and Systems. Criteria for admission are identical to criteria for admission to the Bioengineering concentration of the Data Analytics Engineering, MS program.

Accelerated Option Requirements

Students must complete all requirements for the BS and MS programs, with 6 credits overlap.

Students register for 6 credits of 500-level basic courses in place of the corresponding BENG 400-level courses required for the undergraduate degree requirements. Specifically, students must register for:

BENG 501Bioengineering Research Methods3
CS 504Principles of Data Management and Mining (in place of BENG 420)3
Total Credits6

Note:

Students are permitted to take additional graduate basic courses in their undergraduate programs. In such cases, those classes cannot be counted toward requirements for the MS.

Degree Conferral

Students must apply the semester before they expect to complete the BS requirements to have the BS degree conferred. In addition, at the beginning of the student's final undergraduate semester, students must complete a Bachelor's/Accelerated Master's Transition form that is submitted to the Office of the University Registrar and the VSE Graduate Admissions Office. At the completion of MS requirements, a master's degree is conferred.

Computer Science, BS/Data Analytics Engineering, Accelerated MS

Overview

Highly-qualified students in the Computer Science, BS have the option of obtaining an accelerated Data Analytics Engineering, MS.

For more detailed information, see AP.6.7 Bachelor's/Accelerated Master's Degrees. For policies governing all graduate degrees, see AP.6 Graduate Policies.

Admission Requirements

Students in the Computer Science, BS program may apply to this option if they have earned 90 undergraduate credits with an overall GPA of at least 3.30. Students must have successfully completed CS 310 Data Structures, CS 330 Formal Methods and Models and CS 367 Computer Systems and Programming

Accelerated Option Requirements

Students must complete all requirements for the BS and MS programs, with 6 credits overlap.

Students register for 6 credits of CS 500-level basic courses in place of the corresponding CS 400-level courses required for the undergraduate degree requirements. Specifically, students must register for:

CS 584Theory and Applications of Data Mining3
Select one of the following courses in place of the corresponding 400-level courses:3
Database Systems
Introduction to Artificial Intelligence
Total Credits6

Note:

Students are permitted to take additional graduate basic courses in their undergraduate programs. In such cases, those classes cannot be counted toward requirements for the MS.

Degree Conferral

Students must apply the semester before they expect to complete the BS requirements to have the BS degree conferred. In addition, at the beginning of the student's final undergraduate semester, students must complete a Bachelor's/Accelerated Master's Transition form that is submitted to the Office of the University Registrar and the VSE Graduate Admissions Office. At the completion of MS requirements, a master's degree is conferred.