Who should apply?
The detailed, experiential program is best for professionals who seek a career managing big data. They’ll take on roles with titles such as data scientist, engineer, or analyst, seeking to create systems and extract data sets that can be turned into strategic solutions. The ideal candidate will have strong math skills. While no specific undergraduate degree is required, a background in engineering, business, computer science, statistics, or information technology is desirable.
Why choose Mason?
- George Mason’s Volgenau School of Engineering has worked with big data and cybersecurity for more than 25 years.
- The program is top-ranked. It was named “The 20 Best Schools to Study Big Data Analytics” by the TechRepublic in March 2016 and one of the “Top 50 Best-Value, Big Data Graduate Programs” by Value Colleges in 2018.
- The program’s online format allows the best analytical professionals to learn and advance this science together.
- You can earn your degree in as few as 2 years and be on your way to advancing to roles like data scientist, engineer, or analyst.
- World-renowned faculty with industry experience teach a career-focused curriculum that provides deep conceptual and practical knowledge.
Program requirements for the online MS in Data Analytics Engineering program are subject to change.
Requirements: 30 total credit hours
- 15 credits in required courses
- 15 credits in elective courses
- Bridge Course:
- DAEN 500 – Data Analytics Foundation
- Provides a foundation in data analytics from which the student will build. Focuses on a dataset where students will use analytics tools and apply statistical methodologies in order to extract information of value.
- Core Courses:
- AIT 580 – Big Data to Information
- Course provides an overview of Big Data and its use in commercial, scientific, governmental and other applications. Topics include technical and non-technical disciplines required to collect, process and use enormous amounts of data available from numerous sources. Lectures cover system acquisition, law and policy, and ethical issues. It includes brief discussions of technologies involved in collecting, mining, analyzing and using results.
- STAT 515 – Applied Statistics and Visualization for Analytics
- Introduces multivariate regression and random forests for modeling data. Addresses data access, variable selection and model diagnostics. Introduces foundations for visual thinking. Reviews common statistical graphics such as dot plots, box plots, q-q plots. Addresses more advanced methods such as scatterplot matrices enhanced by smoothed or density contours, and search tools for finding graphics with suggestive patterns. Notes: Course will introduce R software for analysis. A final project will involve visualization of a real data set.
- OR 531 – Analytics and Decision Analysis
- Course focus is predominantly on prescriptive analytics with some parts focused on predictive analytics. Topics include operations research techniques and their application to decision making such as mathematical optimization, networks modeling, stochastic modeling, and multi-objective modeling. Other topics such as PERT, CPM, computer simulation, decision analysis using decision trees and quantitative value functions, and heuristic methods are covered, as well as use of contemporary computer software for problem solving. In particular, the course will extensively use MS Excel for solving the decision making problems. Case-study approach to problem solving is used.
- CS 504 – Principles of Data Management and Mining
- Techniques to store, manage, and use data including databases, relational model, schemas, queries and transactions. On Line Transaction Processing, Data Warehousing, star schema, On Line Analytical Processing. MOLAP, HOLAP, and hybrid systems. Overview of Data Mining principles, models, supervised and unsupervised learning, pattern finding. Massively parallel architectures and Hadoop. Notes: This course cannot be taken for credit by students of the MS CS, MS ISA, MS SWE, CS PhD or IT PhD programs.
- DAEN 690 – Data Analytics Project
- Capstone project course for MS in Data Analytics program. Key activity is completion of a major applied team project resulting in an acceptable technical report and oral briefing. Student should plan to take this course in the last semester of studies.
- Elective Courses:
- AIT 614 – Big Data Essentials
- Hands-on course discusses emerging technologies for big data analytics and their applications in real-world environments. Students apply learned concepts and best practices using several emerging technology tools simulating development, implementation, and use of big data analytical systems. Topics include RDBMS, SQL, NoSQL, R, MapReduce Programming paradigm, Hadoop, HDFS, HIVE, PIG and others in the Hadoop ecosystem for unstructured data analytics.
- AIT 524 – Database Management Systems
- Relational database management systems. Covers logical and physical database design; query languages and database programming; and examines commercial systems. Computing lab. Notes: This course does not count towards MS programs offered in the Computer Science Department and cannot be used to satisfy course requirements for PhD IT students.
- SYST 664 – Bayesian Inference and Decision Theory
- Introduces decision theory and relationship to Bayesian statistical inference. Teaches commonalities, differences between Bayesian and frequentist approaches to statistical inference, how to approach statistics problem, and how to combine data with informed expert judgment to derive useful and policy relevant conclusions. Teaches theory to develop understanding of when and how to apply Bayesian and frequentist methods; and practical procedures for inference, hypothesis testing, and developing statistical models for phenomena. Teaches fundamentals of Bayesian theory of inference, including probability as a representation for degrees of belief, likelihood principle, use of Bayes Rule to revise beliefs based on evidence, conjugate prior distributions for common statistical models, and methods for approximating the posterior distribution. Introduces graphical models for constructing complex probability and decision models from modular components.
- SYST 568 – Applied Predictive Analytics
- Introduces predictive analytics with applications in engineering, business, and econometrics. Topics include time series and cross-sectional data processing, correlation, linear and multiple regressions, time series decomposition, predictive modeling and case study. Provides a foundation of basic theory and methodology with applied examples to analyze large engineering and econometric data for predictive decision making. Hands-on experiments with R will be emphasized.
- AIT 664 – Information Representation, Processing and Visualization
- The course explores basic concepts to understand and analyze the design of information systems, and focuses on conceptual understanding of data, information, and knowledge, boundaries in representing and processing information for humans and machines, information theory, and basic techniques to organize, structure, and interact with the information through visualization.
- SYST 542 – Decision Support Systems Engineering
- Studies design of computerized systems to support individual or organizational decisions. Teaches systems engineering approach to decision support system (DSS) development. DSS is end product of development process, and process is key to successfully integrating DSS into organization. Any DSS is built on a theory (usually implicit) of what makes for successful decision support in given context. Empirical evaluation of specific DSS and the underlying theory should be carried on throughout development process. Course examines prevailing theories of decision support, considers issues in obtaining empirical validation for theory, and discusses empirical support that exists for theories considered. Students design decision support system for semester project.
- AIT 582 – Applications of Metadata in Complex Big Data Problems
- Course explores technical and analytical issues, solutions and gaps in processing large volumes of data by leveraging metadata. The goal is to find “facts of interest” (Intelligence) that represent threats to, or even opportunities for, a given industry or domain (e.g., healthcare, finance or national intelligence/national defense) where there is limited time. Notes: Course may be used in other Certificate or Degree programs.
- AIT 624 – Knowledge Mining from Big-Data
- Introduction to methods and tools related to knowledge mining/representation/visualization, and annotation and retrieval for Big-Data Applications from an applied perspective with the focus on emerging research problems. This course combines survey lectures with in-depth presentation of relevant issues through seminars, and hands-on experience using existing technologies and public data sources.
- SYST 573 – Decision and Risk Analysis
- Study of analytic techniques for rational decision making that address uncertainty, conflicting objectives, and risk attitudes. Covers modeling uncertainty; rational decision-making principles; representing decision problems with value trees, decision trees, and influence diagrams; solving value hierarchies, decision trees, and influence diagrams; defining and calculating the value of information; incorporating risk attitudes into the analysis; and conducting sensitivity analysis. Note: Students may not receive credit for both SYST 473 and 573.
- SYST 584 – Heterogeneous Data Fusion
- Introduces the theory, design and implementation of multi-source information fusion systems in various domains. The course covers distinct technologies for combining data from multiple, heterogeneous sources and performing inferences in support to applications such as cyber security, Semantic Web, decision support systems, situational awareness, intrusion detection, crisis management, and others. The technical content is largely multi-disciplinary, encompassing disciplines such as knowledge engineering, ontologies, statistical learning, artificial intelligence, and data mining.
- DAEN 698 – Independent Research
- Conduct a research project to be chosen and completed under guidance of a graduate faculty member that results in an acceptable technical report. Notes: No more than a total of three credits may be taken from within the DAEN program.
Tuition & Fees (2019-2020):
Tuition is $930.00 per credit hour. An additional charge of $35 per credit hour applies for a distance education fee.
For information on loans and scholarships, visit the Office of Student Financial Aid. For information regarding grants, tuition waivers and other merit aid, please inquire with your graduate department.
The online MS in Data Analytics Engineering program combines strong foundational concepts and technical savvy so you can produce actionable solutions from large data sets in your chosen field.
Graduates find work in:
- All sectors: private, government, profit, and non-profit
- Information Science Technology, Systems Engineering, and Statistic industries
- Data scientist, engineering and analyst roles in any field
Data Scientist vs. Data Analyst Titles
Though the titles can often share or overlap responsibilities, and are sometimes used to mean the same thing, in the strictest definitions, data scientists and data analysts are different roles. Mason’s online master’s in Data Analytics Engineering prepares you for both.
- Data Scientists design the systems for handling data. They have a strong background in computer science and experience in building systems.
- Data Analysts are generally subject matter experts. They have input into the systems that are designed, but their job is to mine the systems and leverage them to produce actionable strategy.
If you are unable to find the answers to your questions, submit a Request For Information form or contact our admissions representative via the contact information below.
How do I apply?
Applicants for the online program should apply here. Applicants for the on-campus program should apply here.
Can I take courses without applying?
Only students who are admitted to the Master of Science in Data Analytics Engineering program may take classes in the program.
What are the requirements for admission?
A bachelor’s degree from a regionally accredited U.S. institution or equivalent is required and a GPA of 3.0 or higher is recommended for admission. 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. At a minimum at least one course each in calculus, statistics, and computer programming is required. Data Analytics Fundamentals may be required for students without a basic foundation in Data Analytics. All prospective students must adhere to university standards for English proficiency and degree equivalency.
How long does it take to complete the program?
The online MS in Data Analytics Engineering degree is 30 credit hours and can be completed in as few as 2 years.
How much does the program cost?
Tuition is $930.00 per credit hour. An additional charge of $35 per credit hour applies for a distance education fee.
When are classes held?
Online classes are offered in an asynchronous format, meaning they can be viewed interactively at your convenience. However, students still must meet all study and deliverable requirements and deadlines.
What accreditation does George Mason hold?
George Mason University is accredited by the Commission on Colleges of the Southern Association of Colleges and Schools to award bachelors, masters and doctoral degrees.
How much does it cost to apply?
While there is a $75 application fee, there are multiple times throughout the year that it may be waived.
What are the admissions recommendations?
- Completed online application
- $75 application fee
- 2 Letters of Recommendation
- Statement of Purpose essay
- Undergrad GPA-minimum 3.0 (submit all undergraduate and graduate transcripts)
- GPA Addendum essay if undergrad GPA below 3.0
- Required courses Calculus and Statistics; coursework or work experience in programming languages also highly recommended