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.
Program Requirements
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:
- 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.
- 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.
- AIT 580 - Analytics: Big Data to Information (3 credits)
- One of the following:
- CS 504 - Principles of Data Management and Mining (3 credits) (for all concentrations except Data Mining)
- CS 584 - Theory and Applications of Data Mining (3 credits) (for the Data Mining concentration only)
- OR 531 - Analytics and Decision Analysis (3 credits)
- One of the following:
- STAT 515 - Applied Statistics and Visualization for Analytics (3 credits) (for all concentrations except Statistics for Analytics)
- STAT 554 - Applied Statistics I (for the Statistics for Analytics concentration only)
- DAEN 690 - Data Analytics Project (3 credits)
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:
- IT 106 - Introduction to IT Problem Solving Using Computer Programming (3 credits)
- MATH 108 - Introductory Calculus with Business Applications (3 credits)
- STAT 250 - Introductory Statistics I (3 credits)
Required Concentration Courses
- AIT 581 - Problem Formation and Solving in Big Data (3 credits)
- AIT 582 - Applications of Metadata in Complex Big Data Problems (3 credits)
- AIT 665 - Managing Information Technology Programs in the Federal Sector (3 credits)
- AIT 679 - Law and Ethics of Big Data (3 credits)
- AIT 697 - Leading Organizations Through Change (3 credits)
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:
- BENG 320 - Bioengineering Signals and Systems (3 credits)
- MATH 113 - Analytic Geometry and Calculus I (4 credits)
- MATH 114 - Analytic Geometry and Calculus II (4 credits)
- MATH 213 - Analytic Geometry and Calculus III (3 credits)
- MATH 214 - Elementary Differential Equations (3 credits)
- STAT 346 - Probability for Engineers (3 credits)
Required Concentration Courses
- BENG 501 - Bioengineering Research Methods (3 credits)
- BENG 551 - Translational Bioengineering (3 credits)
- ECE 528 - Introduction to Random Processes in Electrical and Computer Engineering (3 credits)
- ECE 535 - Digital Signal Processing (3 credits)
- One course selected from the following:
- BENG 525 - Neural Engineering (3 credits)
- BENG 538 - Medical Imaging (3 credits)
- ECE 537 - Introduction to Digital Image Processing (DIP) (3 credits)
- BENG 550 - Advanced Biomechanics (3 credits)
- BENG 636 - Advanced Biomedical Signal Processing (3 credits)
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:
- CS 310 - Data Structures (3 credits)
- CS 330 - Formal Methods and Models (3 credits)
- CS 367 - Computer Systems and Programming (3 credits)
- CS 465 - Computer Systems Architecture (3 credits)
- MATH 125 - Discrete Mathematics I (3 credits)
Required Concentration Courses
- CS 657 - Mining Massive Datasets with MapReduce (3 credits)
- Four courses selected from the following:
- CS 550 - Database Systems (3 credits)
- CS 580 - Introduction to Artificial Intelligence (3 credits)
- CS 650 - Advanced Database Management (3 credits)
- CS 674 - Data Mining on Multimedia Data (3 credits)
- CS 688 - Pattern Recognition (3 credits)
- CS 775 - Advanced Pattern Recognition (3 credits)
- CS 782 - Machine Learning (3 credits)
- CS 787 - Decision Guidance Systems (3 credits)
- INFS 623 - Web Search Engines and Recommender Systems (3 credits)
- INFS 740 - Database Programming for the World Wide Web (3 credits)
- SYST 664 - Bayesian Inference and Decision Theory (3 credits)
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:
- In computer operating systems:
- In computer networking:
Required Concentration Courses
- CFRS 500 - Introduction to Forensic Technology and Analysis (3 credits)
- CFRS 660 - Network Forensics (3 credits)
- Three courses selected from the following:
- CFRS 510 - Digital Forensics Analysis (3 credits)
- CFRS 661 - Digital Media Forensics (3 credits)
- CFRS 663 - Operations of Intrusion Detection for Forensics (3 credits)
- CFRS 664 - Incident Response Forensics (3 credits)
- CFRS 698 - Independent Reading and Research (1-3 credits)
- CFRS 761 - Malware Reverse Engineering (3 credits)
- CFRS 762 - Mobile Device Forensics (3 credits)
- CFRS 763 - Registry Forensics - Windows (3 credits)
- CFRS 764 - Mac Forensics (3 credits)
- CFRS 767 - Penetration Testing in Computer Forensics (3 credits)
- CFRS 768 - Digital Warfare (3 credits)
- CFRS 780 - Advanced Topics in Computer Forensics (3 credits)
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
- SYST 538 or OR 538 - Analytics for Financial Engineering and Econometrics (3 credits)
- SYST 588 or OR 588 - Financial Systems Engineering I: Introduction to Options, Futures, and Derivatives (3 credits)
- SYST 688 or OR 688 - Financial Systems Engineering II: Derivative Products and Risk Management (3 credits)
- Two of the following:
- SYST 568 or OR 568 - Applied Predictive Analytics (3 credits)
- SYST 573 - Decision and Risk Analysis (3 credits)
- SYST 664 - Bayesian Inference and Decision Theory (3 credits)
- SYST 671 or OR 671 - Judgment and Choice Processing and Decision Making (3 credits)
- OR 604 - Practical Optimization (3 credits)
- OR 645 - Stochastic Processes (3 credits)
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:
- CS 222 - Computer Programming for Engineers (3 credits)
- MATH 113 - Analytic Geometry and Calculus I (4 credits)
- STAT 344 - Probability and Statistics for Engineers and Scientists I
Required Concentration Courses
- OR 604 - Practical Optimization (3 credits)
- SYST 542 - Decision Support Systems Engineering (3 credits)
- SYST 568 or OR 568 - Applied Predictive Analytics (3 credits)
- SYST 573 - Decision and Risk Analysis (3 credits)
- One course selected from the following:
- OR 603 - Sports Analtytics (3 credits)
- STAT 663 - Statistical Graphics and Data Exploration I (3 credits)
- SYST 508 - Complex Systems Engineering Management (3 credits)
- SYST 584 - Heterogeneous Data Fusion (3 credits)
- SYST 664 - Bayesian Inference and Decision Theory (3 credits)
- SYST 670 or OR 670 - Metaheuristics for Optimization (3 credits)
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:
- MATH 111 - Linear Mathematical Modeling (3 credits)
- MATH 113 - Analytic Geometry and Calculus I (4 credits)
- MATH 114 - Analytic Geometry and Calculus II (4 credits)
- MATH 213 - Analytic Geometry and Calculus III (3 credits)
- MATH 351 - Probability (3 credits)
Required Concentration Courses
- STAT 544 - Applied Probability (3 credits)
- STAT 654 - Applied Statistics II (3 credits)
- STAT 663 - Statistical Graphics and Data Exploration I (3 credits)
- STAT 672 - Statistical Learning and Data Analytics (3 credits)
- One course selected from STAT courses numbered 540-775
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.