The MS in Data Analytics Engineering is a multidisciplinary degree program in the College of Engineering and Computing, and 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 an earned GPA of 3.00 or better in their 60 highest-level credits. Applicants are expected to have completed a degree in engineering, business, computer science, statistics, mathematics, or information technology, with demonstrated foundational competence in calculus, statistics, and computer programming. Applicants without that formal academic preparation but with clear evidence of strong and extensive work experience in data or analytics, may also be considered on a case by case basis. DAEN 500 Data Analytics Fundamentals may be required for domestic students without a basic foundation in Data Analytics.
There may be additional admission requirements based on a chosen concentration.
Application Requirements and Deadlines are available from https://cec.gmu.edu/admissions/graduate-admissions/application-requirements-and-deadlines.
Policies
Students are strongly encouraged to meet with their academic advisor during their first semester and design an approved plan of study. Students should seek out their advisor when questions arise and when their plan of study needs to be revised. Any changes to the plan of study are to be approved by the DAEN advisor.
Degree Requirements
Total credits: 30
Core Courses
The following core coursework covers the basic elements of data analytics at the graduate level.
| Code | Title | Credits |
|---|---|---|
| AIT 580 | Analytics: Big Data to Information | 3 |
| CS 504 | Principles of Data Management and Mining 1 | 3 |
| or CS 584 | Theory and Applications of Data Mining | |
| DAEN 690 | Data Analytics Project | 3 |
| OR 531 | Introduction to Analytics and Modeling | 3 |
| or OR 541 | Operations Research: Deterministic Optimization | |
| STAT 515 | Applied Statistics and Visualization for Analytics 2 | 3 |
| or STAT 554 | Applied Statistics I | |
| Total Credits | 15 | |
- 1
CS 504 (for all concentrations except Data Mining) or CS 584 (for the Data Mining concentration only)
- 2
STAT 515 (for all concentrations except Statistics for Analytics) or STAT 554 (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.
Available Concentrations
- Concentration in Applied Analytics (APAN)
- Concentration in Bioengineering (BIOE)
- Concentration in Business Analytics (BUSA)
- Concentration in Computational Linguistics (CMPL)
- Concentration in Cyber Analytics (CYBA)
- Concentration in Data Mining (DTM)
- Concentration in Financial Engineering (FNNE)
- Concentration in Health Data Analytics (HDAN)
- Concentration in the Internet of Things (INOT)
- Concentration in Mechanical Engineering (ME)
- Concentration in Naval Engineering (NAVL)
- Concentration in Predictive Analytics (PRAN)
- Concentration in Statistical Analytics (STLA)
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:
| Code | Title | Credits |
|---|---|---|
| IT 106 | Introduction to IT Problem Solving Using Computer Programming | 3 |
| MATH 108 | Introductory Calculus with Business Applications (Mason Core) | 3 |
| STAT 250 | Introductory Statistics I (Mason Core) | 3 |
Required Concentration Courses
| Code | Title | Credits |
|---|---|---|
| AIT 524 | Database Management Systems | 3 |
| Select four from the following: | 12 | |
| Introduction to Natural Language Processing | ||
| Metadata Analytics for Big Data | ||
| Topics in Applied Information Technology | ||
| Big Data Essentials | ||
| Determining Needs for Complex Big Data Systems | ||
| Knowledge Mining from Big-Data | ||
| Interpretable Machine Learning | ||
| Information: Representation, Processing and Visualization | ||
| Theories and Models in Geo-Social Data Analytics | ||
| Data Analytics in Social Media | ||
| Natural Language Processing with Deep Learning | ||
| Applied Machine Learning | ||
| Applied Deep Learning | ||
| Data Analytics Research Project | ||
| Total Credits | 15 | |
Concentration in Bioengineering (BIOE)
Focuses on data collection from medical devices/sensors and conditioning, filtering, extraction, and preparing the data.
Additional Admission Requirements
Students entering the program should have completed the following George Mason undergraduate courses or their equivalents:
| Code | Title | Credits |
|---|---|---|
| BENG 320 | Bioengineering Signals and Systems | 3 |
| MATH 113 | Analytic Geometry and Calculus I (Mason Core) | 4 |
| MATH 114 | Analytic Geometry and Calculus II | 4 |
| MATH 213 | Analytic Geometry and Calculus III | 3 |
| MATH 214 | Elementary Differential Equations | 3 |
| STAT 346 | Probability for Engineers | 3 |
Note:
Domestic 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
| Code | Title | Credits |
|---|---|---|
| BENG 501 | Bioengineering Research Methods | 3 |
| BENG 575 | Intellectual Property, Regulatory Concepts and Product Development | 3 |
| ECE 528 | Introduction to Random Processes in Electrical and Computer Engineering | 3 |
| ECE 535 | Digital Signal Processing | 3 |
| Select one from the following: | 3 | |
| Neural Engineering | ||
| Medical Imaging | ||
| Advanced Biomechanics | ||
| Data Analytics Research Project | ||
| Image Processing and Computer Vision | ||
| Total Credits | 15 | |
Concentration in Business Analytics (BUSA)
Students will learn to effectively analyze data through the hands-on use of decision modeling and other techniques using popular software tools. This concentration utilizes a wide array of methodologies and techniques — from data collection, organization, reporting and mining to extraction of useful and actionable information for decision makers.
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
| Code | Title | Credits |
|---|---|---|
| GBUS 738 | Data Mining for Business Analytics | 3 |
| Select four from the following: | 12 | |
| Marketing Analytics | ||
| Marketing Research | ||
| Advanced Data Mining for Business Analytics | ||
| People Analytics | ||
| Fraud Examination | ||
| Total Credits | 15 | |
Concentration in Computational Linguistics (CMPL)
This concentration focuses on applying big data approaches to natural language data problems. Students will be introduced to a wide range of natural language systems and phenomena and be prepared to design robust data analytic methods that scale to accommodate the diversity of language systems and questions of language use.
Required Concentration Courses
| Code | Title | Credits |
|---|---|---|
| Linguistics Foundation Courses: | 6 | |
| Syntax B | ||
| Morphology | ||
| Computational Linguistics Core: | 6 | |
| Computational Linguistics I | ||
| Computational Linguistics II | ||
| Select one from the following: | 3 | |
| Sociolinguistics | ||
| Typology | ||
| Data Analytics Research Project | ||
| Total Credits | 15 | |
Concentration in Cyber Analytics (CYBA)
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:
| Code | Title | Credits |
|---|---|---|
| Computer Operating Systems | ||
| CYSE 211 | Operating Systems and Lab | 3 |
| Computer Networking | ||
| IT 341 | Data Communications and Network Principles | 3 |
| or TCOM 535 | The TCP/IP Suite of Internet Protocols | |
| IT 445 | Advanced Networking Principles | 3 |
| or TCOM 515 | Internet Protocol Routing: Lecture and Laboratory Course | |
Note:
Domestic 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
| Code | Title | Credits |
|---|---|---|
| DFOR 510 | Digital Forensics Analysis | 3 |
| DFOR 660 | Network Forensics | 3 |
| Select three from the following: | 9 | |
| Data Analytics Research Project | ||
| Digital Media Forensics | ||
| Operations of Intrusion Detection for Forensics | ||
| Incident Response Forensics | ||
| Independent Reading and Research | ||
| Malware Reverse Engineering | ||
| Penetration Testing in Digital Forensics | ||
| Digital Warfare | ||
| Advanced Topics in Digital Forensics | ||
| Learning From Data | ||
| Total Credits | 15 | |
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:
| Code | Title | Credits |
|---|---|---|
| CS 310 | Data Structures | 3 |
| CS 330 | Formal Methods and Models | 3 |
| CS 367 | Computer Systems and Programming | 4 |
| CS 465 | Computer Systems Architecture | 3 |
| MATH 125 | Discrete Mathematics I (Mason Core) | 3 |
Note:
Domestic 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
| Code | Title | Credits |
|---|---|---|
| CS 657 | Mining Massive Datasets with MapReduce | 3 |
| Select four from the following: 1 | 12 | |
| Database Systems | ||
| Introduction to Artificial Intelligence | ||
| Advanced Database Management | ||
| Machine Learning | ||
| Advanced Pattern Recognition | ||
| Advanced Machine Learning | ||
| Decision Guidance Systems | ||
| Data Analytics Research Project | ||
| Web Search Engines and Recommender Systems | ||
| Database Programming for the World Wide Web | ||
| Bayesian Artificial Intelligence | ||
| Total Credits | 15 | |
- 1
Note: all prerequisites must be met.
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. 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:
| Code | Title | Credits |
|---|---|---|
| CS 112 | Introduction to Computer Programming (Mason Core) | 4 |
| MATH 113 | Analytic Geometry and Calculus I (Mason Core) | 4 |
| STAT 344 | Probability and Statistics for Engineers and Scientists I | 3 |
Required Concentration Courses
| Code | Title | Credits |
|---|---|---|
| SYST/OR 538 | Analytics for Financial Engineering and Econometrics | 3 |
| SYST/OR 588 | Financial Systems Engineering I: Introduction to Options, Futures, and Derivatives | 3 |
| SYST/OR 688 | Financial Systems Engineering II: Derivative Products and Risk Management | 3 |
| Select two from the following: | 6 | |
| Data Analytics Research Project | ||
| Operations Research: Deterministic Optimization | ||
| Operations Research: Stochastic Models | ||
| Data-driven Large-scale Optimization | ||
| Stochastic Processes | ||
| Applied Predictive Analytics | ||
| Decision and Risk Analysis | ||
| Bayesian Artificial Intelligence | ||
| Metaheuristics for Optimization | ||
| Total Credits | 15 | |
Concentration in Health Data Analytics (HDAN)
This concentration focuses on analytics for health care. It provides coverage across several data analytics areas with specific application to the health care domain.
| Code | Title | Credits |
|---|---|---|
| HAP 720 | Health Data Integration | 3 |
| HAP 725 | Statistical Process Control in Healthcare | 3 |
| HAP 780 | Data Mining in Health Care | 3 |
| or HAP 880 | Advanced Health Data Mining | |
| Select two from the following: | 6 | |
| Data Analytics Research Project | ||
| Health Care Databases | ||
| Advanced Statistics in Health Services Research I | ||
| Health Care Decision Analysis | ||
or HAP 770 | Medical Decision Making and Decision Support Systems | |
| Advanced Statistics in Health Services Research II | ||
| Causal Analysis Comparative Effectiveness | ||
| Total Credits | 15 | |
Concentration in the Internet of Things (INOT)
The focus of this concentration is driven by sensors that collect the staggering amount of data that exists today. The Internet of Things (IoT) is expanding, at a geometric level, the number of devices that collect, forward, and offer for analysis data. IOTA looks at Data Analytics from the perspective of IoT and sensors. Analog and digital sensing design and deployment, hardware options, power consumption, security, sampling and quantization, Fourier transform, time analysis, and synchronization are topics covered in this concentration.
Additional Admission Requirements
Students entering the program should have completed the following George Mason undergraduate courses or their equivalents:
| Code | Title | Credits |
|---|---|---|
| CS 222 | Computer Programming for Engineers | 3 |
| ECE 301 | Digital Electronics | 3 |
| ECE 321 | Continuous-Time Signals and Systems | 3 |
| MATH 113 | Analytic Geometry and Calculus I (Mason Core) | 4 |
| MATH 114 | Analytic Geometry and Calculus II | 4 |
| STAT 346 | Probability for Engineers | 3 |
Required Concentration Courses
| Code | Title | Credits |
|---|---|---|
| Select five courses from the following: | 15 | |
| Data Analytics Research Project | ||
| Internet of Things | ||
| Learning From Data | ||
| Sensor Engineering | ||
| Digital Signal Processing | ||
| Big Data Technologies | ||
| Real-Time Embedded Systems | ||
| Total Credits | 15 | |
Concentration in Mechanical Engineering (ME)
This concentration will take advantage of mechanical engineering course offerings which will give students an opportunity to apply data analytics methods to real world engineering problems.
Required Concentration Courses
| Code | Title | Credits |
|---|---|---|
| Select five courses from the following: | 15 | |
| AI Design and Deployment Risks | ||
| AI: Ethics, Policy, and Society | ||
| Human Robot Interaction | ||
| Mechanical Engineering Decision Making | ||
| Foundations of Fluid Mechanics | ||
| Fracture Mechanics | ||
| Advanced Fluid Mechanics | ||
| Finite Element Analysis for Solids | ||
| Mechanics and Properties of Materials | ||
| Nanomaterials Enabled Renewable Energy | ||
| Advanced Materials for Water Treatment | ||
| Tribology and Surface Engineering | ||
| Introduction to Nano-Materials | ||
| Optofluidics | ||
| Nano Bio Sensors | ||
| Data Analytics Research Project | ||
| Total Credits | 15 | |
Concentration in Naval Engineering (NAVL)
This concentration will take advantage of the naval engineering course offerings which will give students an opportunity to apply data analytics methods to real world engineering problems.
Required Concentration Courses
| Code | Title | Credits |
|---|---|---|
| ME 551 | Naval Engineering | 3 |
| ME 552 | Fundamentals of Naval Architecture | 3 |
| ME 553 | Ship Design Process and Tools | 3 |
| ME 554 | Naval Project Management | 3 |
| DAEN 698 | Data Analytics Research Project | 3 |
| Total Credits | 15 | |
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, taking 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:
| Code | Title | Credits |
|---|---|---|
| CS 112 | Introduction to Computer Programming (Mason Core) | 4 |
| MATH 113 | Analytic Geometry and Calculus I (Mason Core) | 4 |
| STAT 344 | Probability and Statistics for Engineers and Scientists I | 3 |
Required Concentration Courses
| Code | Title | Credits |
|---|---|---|
| OR 604 | Data-driven Large-scale Optimization | 3 |
| SYST 542 | Decision Support Systems Engineering | 3 |
| SYST/OR 568 | Applied Predictive Analytics | 3 |
| SYST 573 | Decision and Risk Analysis | 3 |
| Select one from the following: | 3 | |
| Data Analytics Research Project | ||
| Operations Research: Deterministic Optimization | ||
| Operations Research: Stochastic Models | ||
| Sports Analytics | ||
| Deep Learning for Predictive Analytics | ||
| Statistical Graphics and Data Visualization | ||
| Complex Systems Engineering Management | ||
| Heterogeneous Data Fusion | ||
| Model-based Systems Engineering | ||
| Bayesian Artificial Intelligence | ||
| Metaheuristics for Optimization | ||
| Total Credits | 15 | |
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:
| Code | Title | Credits |
|---|---|---|
| MATH 203 | Linear Algebra | 3 |
| MATH 213 | Analytic Geometry and Calculus III | 3 |
| STAT 346 | Probability for Engineers | 3 |
| or MATH 351 | Probability | |
Required Concentration Courses
| Code | Title | Credits |
|---|---|---|
| STAT 544 | Applied Probability | 3 |
| Select four from the following: | 12 | |
| Data Analytics Research Project | ||
| Applied Statistics II | ||
| Multivariate Analysis and Statistical Learning | ||
| Statistical Graphics and Data Visualization | ||
| Statistical Learning and Data Analytics | ||
| Total Credits | 15 | |
- Applied Computer Science, BS/Data Analytics Engineering, Accelerated MS
- Applied Science, BAS (Data Analytics Concentration)/Data Analytics Engineering, Accelerated MS
- Bioengineering, BS/Data Analytics Engineering, Accelerated MS
- Computer Science, BS/Data Analytics Engineering, Accelerated MS
- Mechanical Engineering, BS/Data Analytics Engineering, Accelerated MS
- Statistics, BS/Data Analytics Engineering, Accelerated MS
- Systems and Industrial Engineering, BS/Data Analytics Engineering, Accelerated MS
Applied Computer Science, BS/Data Analytics Engineering, Accelerated MS
Overview
Highly-qualified students in the Applied Computer Science, BS can complete both a BS-ACS and a Data Analytics Engineering, MS in five years through the BS-MS accelerated (BAM) program.
General BAM policies are in the catalog under AP.6.7 Bachelor's/Accelerated Master's Degrees. Policies governing all graduate degrees are in the catalog under AP.6 Graduate Policies.
Admission Requirements
Students in the Applied Computer Science, BS program are encouraged to apply to the BAM program after earning 60 undergraduate credits with an overall GPA of at least 3.30. Students must also have successfully completed CS 310 Data Structures and CS 330 Formal Methods and Models.
Accelerated Option Requirements
Students accepted to the BAM program may earn up to 9 credits of graduate coursework that count towards both the BS and MS degrees. They may begin taking graduate courses after completing 75 undergraduate credits and successfully completing CS 367 Computer Systems and Programming.
The following graduate courses can replace the corresponding undergraduate courses. For all students in the Applied Computer Science, BS program:
| Code | Title | Credits |
|---|---|---|
| CS 584 | Theory and Applications of Data Mining (to replace CS 484) | 3 |
Students in the Software Engineering and Bioinformatics concentrations of the Applied Computer Science, BS program may also register for:
| Code | Title | Credits |
|---|---|---|
| CS 550 | Database Systems (to replace CS 450) | 3 |
Students in the Computer Game Design and Geography concentrations of the Applied Computer Science, BS program may also register for one or both of the following courses:
| Code | Title | Credits |
|---|---|---|
| CS 550 | Database Systems (to replace CS 450) | 3 |
| CS 580 | Introduction to Artificial Intelligence (to replace CS 480) | 3 |
Notes:
- 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 may not use both the graduate course and the undergraduate alternative for their BS degree.
- Students must satisfy all recommended and required prerequisites for the graduate courses they take.
- Students must still take the DAEN core courses (AIT 580 Analytics Big Data to Information, OR 531 Analytics and Decision Analysis, and STAT 515 Applied Statistics and Visualization for Analytics).
- Students also have the option to take up to 6 additional credits of graduate coursework on reserve, which can be used for the MS degree only.
Degree Conferral
Students must apply for degree conferral the semester before they expect to complete their BS requirements. At the beginning of their final undergraduate semester, students must submit a completed Bachelor's-Accelerated Master's Transition form to the CS department office. The master’s degree will be conferred after the student completes the MS requirements.
Applied Science, BAS (Data Analytics Concentration)/Data Analytics Engineering, Accelerated MS
Overview
Highly-qualified students in the Applied Science, BAS, Data Analytics Concentration 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 Science, BAS Data Analytics concentration may apply to this option if they have earned 60 undergraduate credits with an overall GPA of at least 3.30. Students may begin taking the master's level courses once they have earned 75 undergraduate credits.
Accelerated Option Requirements
Students must complete all credits that satisfy requirements for the BAS and MS programs. The following six credits may overlap:
| Code | Title | Credits |
|---|---|---|
| AIT 580 | Analytics: Big Data to Information | 3 |
| CS 504 | Principles of Data Management and Mining | 3 |
| or CS 584 | Theory and Applications of Data Mining | |
| OR 531 | Introduction to Analytics and Modeling | 3 |
| or OR 541 | Operations Research: Deterministic Optimization | |
| STAT 515 | Applied Statistics and Visualization for Analytics | 3 |
| or STAT 554 | Applied Statistics I | |
In addition to the six credits above, students may select up to 6 additional credits to overlap. These credits must be selected in consultation with both the BAS and DAEN advisors. Credits selected will depend on which DAEN concentration the student intends to pursue.
Bioengineering, BS/Data Analytics Engineering, Accelerated MS
Overview
Highly-qualified undergraduates may be admitted to the bachelor's/accelerated master's program and obtain a BS in Bioengineering and MS in Data Analytics Engineering with a concentration in Bioengineering in an accelerated time-frame.
See AP.6.7 Bachelor's/Accelerated Master's Degrees for policies related to this program.
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.
BAM Pathway Admission Requirements
Applicants to all graduate programs at George Mason University must meet the admission standards and application requirements for graduate study as specified in the Graduate Admissions Policies and Bachelor's/Accelerated Master's Degree policies.
Students majoring in the Bioengineering, BS will be considered for admission into the BAM Pathway after completion of a minimum of 60 undergraduate credits with an overall GPA of at least 3.0. 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.
Students who are accepted into the BAM Pathway will be allowed to register for graduate level courses after successful completion of a minimum of 75 undergraduate credits and course-specific pre-requisites
Accelerated Master's Admission Requirements
Students already admitted in the BAM Pathway will be admitted to the Data Analytics Engineering, MS program, if they have met the following criteria, as verified on the Bachelor’s/Accelerated Master’s Transition form:
- A GPA of 3.0 or better in their 60 highest-level credits
- Successfully meeting Mason’s requirements for undergraduate degree conferral (graduation) and completing the application for graduation.
Accelerated Pathway Requirements
To maintain the integrity and quality of both the undergraduate and graduate degree programs, undergraduate students must complete all credits that satisfy requirements for both the BS and MS programs, with up to three classes (nine credits) overlap depending on their bioengineering concentration chosen from the following graduate courses:
| Code | Title | Credits |
|---|---|---|
| AIT 580 | Analytics: Big Data to Information | 3 |
| BENG 501 | Bioengineering Research Methods | 3 |
| CS 504 | Principles of Data Management and Mining (in place of BENG 420) | 3 |
| OR 531 | Introduction to Analytics and Modeling | 3 |
| STAT 515 | Applied Statistics and Visualization for Analytics | 3 |
| Total Credits | 15 | |
All graduate course prerequisites must be completed prior to enrollment. Each graduate course must be completed with a grade of B or better to apply toward the MS program. The graduate courses may be counted as Technical Electives or, in the case of CS 504 Principles of Data Management and Mining, for BENG 420 Biomedical Data Analytics towards the Bioengineering, BS program requirements with approval by the academic advisors of both the BS and MS programs.
While still in undergraduate status, a maximum of six additional graduate credits may be taken as reserve graduate credit and applied to the master's program. Reserve graduate credits do not apply to the undergraduate degree. These reserve credits can be chosen from the list of graduate level courses given above with approval by the academic advisors of both the BS and MS programs.
For more detailed information on coursework and timeline requirements, see AP.6.7 Bachelor's/Accelerated Master's Degrees policies.
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 are recommended to meet with the Bioengineering academic advisor one year before and must apply to the program one 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. 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 can complete both a BS-CS and a Data Analytics Engineering, MS in five years through the BS-MS accelerated (BAM) program.
General BAM policies are in the catalog under AP.6.7 Bachelor's/Accelerated Master's Degrees. Policies governing all graduate degrees are in the catalog under AP.6 Graduate Policies.
Admission Requirements
Students in the Computer Science, BS program are encouraged to apply to the BAM program after earning 60 undergraduate credits with an overall GPA of at least 3.30. Students must have successfully completed CS 310 Data Structures and CS 330 Formal Methods and Models .
Accelerated Option Requirements
Students accepted to the BAM program may earn up to 9 credits of graduate coursework that count towards both the BS and MS degrees. They may begin taking graduate courses after completing 75 undergraduate credits and successfully completing CS 367 Computer Systems and Programming.
The following graduate courses can replace the corresponding undergraduate courses.
| Code | Title | Credits |
|---|---|---|
| CS 584 | Theory and Applications of Data Mining (to replace CS 484) | 3 |
| CS 550 | Database Systems (to replace CS 450) | 3 |
| CS 580 | Introduction to Artificial Intelligence (to replace CS 480) | 3 |
Notes:
- Students may not use both the graduate course and the undergraduate alternative for their BS degree.
- Students must satisfy all recommended and required prerequisites for the graduate courses they take.
- Students must still take the DAEN core courses (AIT 580 Analytics Big Data to Information, OR 531 Analytics and Decision Analysis, and STAT 515 Applied Statistics and Visualization for Analytics).
- Students also have the option to take up to 6 additional credits of graduate coursework on reserve, which can be used for the MS degree only.
Degree Conferral
Students must apply for degree conferral the semester before they expect to complete their BS requirements. At the beginning of their final undergraduate semester, students must submit a completed Bachelor's-Accelerated Master's Transition form to the CS department office. The master’s degree will be conferred after the student completes the MS requirements.
Mechanical Engineering, BS/Data Analytics Engineering, Accelerated MS
Overview
Highly-qualified students in the Mechanical Engineering, BS program have the option of applying to the accelerated Data Analytics Engineering, MS program.
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 Mechanical Engineering, BS program may apply to the accelerated Data Analytics Engineering, MS program if they have earned 60 undergraduate credits with an overall GPA of at least 3.30.
Students who are accepted into the BAM Pathway will be allowed to register for graduate level courses after successful completion of a minimum of 75 undergraduate credits and course-specific pre-requisites.
Accelerated Option Requirements
Students must complete all credits that satisfy requirements for the BS and MS programs, with up to twelve credits overlap chosen from the following courses:
| Code | Title | Credits |
|---|---|---|
| AIT 580 | Analytics: Big Data to Information | 3 |
| CS 504 | Principles of Data Management and Mining | 3 |
| OR 531 | Introduction to Analytics and Modeling | 3 |
| STAT 515 | Applied Statistics and Visualization for Analytics | 3 |
All graduate course prerequisites must be completed prior to enrollment. Each 500-level course must be completed with a grade of B or better to apply toward the MS program. The graduate courses selected for overlap must be approved by the academic advisors of both the BS and MS programs. The graduate courses may be counted as Electives toward the Mechanical Engineering, BS program requirements, with approval of the Mechanical Engineering Department.
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. At the completion of MS requirements, a master's degree is conferred.
Statistics, BS/Data Analytics Engineering, Accelerated MS
Overview
Highly-qualified undergraduates may be admitted to the bachelor's/accelerated master's program (BAM) and obtain the Statistics, BS and the Data Analytics Engineering, MS in an accelerated time-frame after satisfactory completion of a minimum of 138 credits.
Admitted students are able to use up to 12 graduate credits in partial satisfaction of requirements for the undergraduate degree. Upon completion and conferral of the bachelor's degree and with satisfactory performance (grade of 'B' or better) in each of the graduate courses, students are given advanced standing in the master's program.
See AP.6.7 Bachelor's/Accelerated Master's Degrees for policies related to this program.
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.
BAM Pathway Admission Requirements
Applicants to all graduate programs at George Mason University must meet the admission standards and application requirements for graduate study as specified in Graduate Admissions Policies and
Bachelor's/Accelerated Master's Degree policies.
Students will be considered for admission into the BAM Pathway after completion of a minimum of 60 credits with an overall GPA of at least 3.0.
Students who are accepted into the BAM Pathway will be allowed to register for graduate level courses after successful completion of a minimum of 75 undergraduate credits and course-specific prerequisites.
Accelerated Master's Admission Requirements
Students already admitted in the BAM Pathway will be admitted to the Data Analytics Engineering, MS program, if they have met the following criteria, as verified on the Bachelor’s/Accelerated Master’s Transition form:
- Completion of Mason’s requirements for undergraduate degree conferral (graduation) and completion of application for graduation.
- An overall GPA of at least 3.0.
Accelerated Pathway Requirements
To maintain the integrity and quality of both the undergraduate and graduate degree programs, students complete all credits that satisfy requirements for the BS and MS programs, with up to twelve credits overlap chosen from the following graduate courses:
| Code | Title | Credits |
|---|---|---|
| CS 504 | Principles of Data Management and Mining | 3 |
| or CS 584 | Theory and Applications of Data Mining | |
| OR 541 | Operations Research: Deterministic Optimization (Credit may not be received for both OR 441 and OR 541.) | 3 |
| or OR 531 | Introduction to Analytics and Modeling | |
| STAT 515 | Applied Statistics and Visualization for Analytics | 3 |
| or STAT 554 | Applied Statistics I | |
| STAT 663 | Statistical Graphics and Data Visualization | 3 |
All graduate course prerequisites must be completed prior to enrollment. Each graduate course must be completed with a grade of B or better to apply toward the MS program.
The graduate courses may be counted as Technical Electives toward the Statistics, BS program requirements, with approval of the Statistics Department undergraduate coordinator.
While still in undergraduate status, a maximum of six additional graduate credits may be taken as reserve graduate credit and applied to the master's program. Reserve graduate credits do not apply to the undergraduate degree.
For more detailed information on coursework and timeline requirements, see AP.6.7 Bachelor's/Accelerated Master's Degrees policies.
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 and Recruitment office. At the completion of MS requirements, a master's degree is conferred.
Systems and Industrial Engineering, BS/Data Analytics Engineering, Accelerated MS
Overview
Highly-qualified undergraduates may be admitted to the bachelor's/accelerated master's program and obtain a Systems and Industrial Engineering, BS and a Data Analytics Engineering, MS in an accelerated time-frame after satisfactory completion of a minimum of 141 credits.
Admitted students are able to use up to 12 credits of approved advanced standing graduate courses in partial satisfaction of requirements for the undergraduate degree. Upon completion and conferral of the bachelor's degree and with satisfactory performance (grade of 'B' or better) in each of the advanced standing graduate courses, the courses are applied to partial satisfaction of requirements for the master's program.
See AP.6.7 Bachelor's/Accelerated Master's Degrees for policies related to this program.
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.
BAM Pathway Admission Requirements
Applicants to all graduate programs at George Mason University must meet the admission standards and application requirements for graduate study as specified in Graduate Admissions Policies and Bachelor's/Accelerated Master's Degree policies.
Systems and Industrial Engineering, BS students will be considered for admission into the BAM Pathway after completion of a minimum of 60 credits with an overall GPA of at least 3.0.
For the predictive analytics and financial engineering concentrations, 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.
Students who are accepted into the BAM Pathway will be allowed to register for graduate level courses after successful completion of a minimum of 75 undergraduate credits and course-specific pre-requisites.
Accelerated Master's Admission Requirements
Students already admitted in the BAM Pathway will be admitted to the Data Analytics Engineering, MS program, if they have met the following criteria, as verified on the Bachelor’s/Accelerated Master’s Transition form:
- An overall GPA of at least 3.0
- Successfully meeting Mason’s requirements for undergraduate degree conferral (graduation) and completing the application for graduation.
Accelerated Pathway Requirements
To maintain the integrity and quality of both the undergraduate and graduate degree programs, undergraduate students interested in taking graduate courses must choose from the following:
Advanced Standing course: Students must complete all credits that satisfy requirements for both the BS and MS programs. Up to four courses (12 credits) of approved master’s level courses taken as part of the undergraduate degree may be applied to the graduate degree.
These courses may be chosen from the list of graduate courses in the following table. For Systems and Industrial Engineering, BS students, these graduate courses replace the corresponding undergraduate courses listed in the table. The undergraduate version of these courses may not be applied toward the Systems Engineering, MS.
| Undergraduate | Graduate | |
|---|---|---|
| OR 441 | OR 541 | Satisfies OR 531 core requirement in the graduate program. Credit may not be received for both courses. |
| OR 442 | OR 542 | This course applies to only certain concentrations; Credit may not be received for both courses. |
| SYST 414 | SYST 514 | This course applies to only certain concentrations; Credit may not be received for both courses |
| SYST 420 | SYST 521/OR 643 | This course applies to only certain concentrations; Credit may not be received for both courses |
| SYST 438 | SYST 538 | This course applies to only certain concentrations; Credit may not be received for both courses |
| SYST 448 | SYST 548 | This course applies to only certain concentrations; Credit may not be received for both courses |
| SYST 460 | SYST 560 | This course applies to only certain concentrations; Credit may not be received for both courses |
| SYST 461 | SYST 660 | This course applies to only certain concentrations; Credit may not be received for both courses |
| SYST 468 | SYST 568 | This course applies to only certain concentrations; Credit may not be received for both courses |
| SYST 473 | SYST 573 | This course applies to only certain concentrations; Credit may not be received for both courses. |
| SYST 488 | SYST 588 | This course applies to only certain concentrations; Credit may not be received for both courses |
For the predictive analytics and financial engineering 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 Optimization will substitute for the OR 531 Introduction to Analytics and Modeling core requirement in the MS DAEN program. Students Bachelor of Science in Systems and Industrial Engineering program are not permitted to take OR 531 Introduction to Analytics and Modeling.
Students must pay attention to the prerequisites required for a course, and the master's degree concentration that the course may satisfy.
While still in undergraduate status, a maximum of 6 additional graduate credits may be taken as reserve graduate credit and applied to the master's program. Reserve graduate credits do not apply to the undergraduate degree.
For more detailed information on coursework and timeline requirements, see AP.6.7 Bachelor's/Accelerated Master's Degrees.
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. At the completion of MS requirements, a master's degree is conferred.