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2014-2015 University Catalog
Data Analytics Engineering, MS
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Banner Code: VS-MS-DAEN
School: Volgenau School of Engineering
Department: Interdisciplinary Programs
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 models, 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 intelligence and other information intensive groups generating and consuming large amounts of data. The focus of the degree is on the technologies and methodologies of data analytics and areas of expertise within the Volgenau School of Engineering.
Admission Requirements
Applicants must have completed a baccalaureate degree from an accredited program with a reputation for high academic standards and an earned GPA of 3.00 or better in their 60 highest-level credits. While no specific undergraduate degree is required, a background in engineering, business, computer science, statistics, mathematics, or information technology, is desirable, or alternatively strong work experience with data or analytics may be used.
For some of the concentrations there are additional admission requirements. These are listed below in the descriptions of the individual concentrations.
In addition to fulfilling Mason’s admission requirements for graduate study, applicants must:
- Provide three letters of recommendation, preferably from academic references or references in industry or government who are familiar with the applicant’s professional accomplishments.
- Provide a resume.
- Provide a detailed statement of career goals and professional aspirations.
- Complete a self-evaluation form.
- If their native language is not English, students must earn a minimum TOEFL score of 575 for the paper-based exam or 230 for the computer-based exam.
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Degree Requirements (30 credits)
Core Courses (15 credits)
The following core course work covers the basic elements of data analytics at the graduate level.
Note: CS 659 is required for the Data Mining concentration; STAT 554 is required for the Statistics for Analytics concentration Concentrations (15 credits)
Students will elect one of five concentrations that correspond 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 to government protocols.
All students in this concentration must take the following five courses: ▲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: Required Concentration Courses
and four courses selected from the following:
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: Required Concentration Courses
and three courses selected from the following:
▲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 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
and two courses selected from the following:
▲Concentration in Statistics for Analytics (STAN)
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 must have completed three semesters of calculus, a calculus-based probability course, and matrix algebra. Required Concentration Courses
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