Why George Mason University?
The online Master of Science in Data Analytics Engineering offered by George Mason University will attract aspiring data scientists. They want to leverage their quantitative skills into a rewarding career in data science. George Mason, a large public research university in Fairfax County, Virginia, enjoys a location amid the booming tech scene of Northern Virginia and the DC suburbs. Applicants to the MSDAE program should hold an undergraduate degree in engineering, business, computer science, statistics, mathematics, or information technology. They should also demonstrate foundational competence in calculus, statistics, and computer programming.
Students without the necessary academic background will be considered on a case-by-case basis. Standardized test scores are not required, and a minimum undergraduate GPA of 3.0 is expected. Applicants should be aware that each concentration within the degree may have additional prerequisite courses, which are listed in the GMU graduate catalog.
|George Mason University
|Name of Degree
|Master of Science in Data Analytics Engineering
|School or Department Administering Program
|Cost per credit
|Tuition for Entire Program
|Prerequisite Courses and Skills
|Degree in engineering, business, computer science, statistics, mathematics, or information technology, with demonstrated foundational competence in calculus, statistics, and computer programming. Students without this background will be considered on a case-by-case basis
|Campus Visit Required?
|Typical Time to Complete
|When Can Students Start the Program?
|Does the Program Include a Capstone?
|Does the program include a practicum or internship?
|Other Features that Make the Program Unique
|Program Objectives (quoted directly from the program)
|The degree is designed to provide students with a foundation in the technologies and methods of data-driven problem-solving and decision-making. Students study data mining, information technology, modeling, predictive analytics, optimization, risk analysis, and data visualization.
|This 30-credit program prepares students to make decisions driven by data analytics. Topics covered include data mining, information technology, statistical modeling, predictive analytics, optimization, risk analysis, and data visualization. Ten different concentrations are offered: Applied Analytics, Bioengineering, Business Analytics, Cyber Analytics, Data Mining, Financial Engineering, Health Data Analytics, The Internet of Things, Predictive Analytics, and Statistical Analytics.
What will I Learn?
The emphasis throughout the GMU program is preparing students with the techniques and methods they need to participate in data-driven decision making in a variety of organizations. Topics covered include data mining, information technology, statistical modeling, predictive analytics, optimization, risk analysis, and data visualization. Four courses are required of all students. Two of the required courses, Analytics: Big Data to Information and Analytics and Decision Analysis, focus on analytics theory and methods. Students then have a choice between a course in Principles of Data Management and Mining or a course in Theory and Applications of Data Mining. All students must complete a required Data Analytics Project in which they apply the skills and knowledge they have learned to explore a real-world organizational problem.
Setting the GMU program apart from other similar programs is its selection of ten different concentrations to provide focus to the curriculum. The concentration in applied analytics requires a background in programming, calculus, and statistics, and offers in-depth training on using significant data approaches for organizations and businesses. The bioengineering concentration requires a significant mathematical background and a prerequisite course in bioengineering. The concentration hones in on the data collection and analysis processes presented by data collected from biomedical sensors, which are sensors that track physical and chemical processes.
The Business Analytics concentration offers courses in marketing analytics and data mining for business to enable students to build modeling and decision-making skills in data-rich business environments. The Concentration in Cyber Analytics, otherwise known as digital forensics, prepares students to acquire, transform and model data that is suitable for presentation in a court of law, covering areas such as digital media, intercepted network data, and mobile media. Cyber Analytics students should have prior coursework in computer networking systems.
The concentration in Data Mining teaches students interested in advanced pattern-recognition, decision-guidance systems, and Bayesian inference, as well as advanced database. Prior computer science courses in several subjects are required. The concentration in Financial Engineering teaches students the techniques to analyze extensive financial and economic data to make effective business decisions, to prepare students for careers in investment and risk mitigation. Prior coursework in statistics and computer science is required. The concentration in Health Data Analytics focuses on data for health care and has no additional prerequisites.
Data collected from sensors embedded in everyday objects is the focus of the concentration in the Internet of Things, which requires students to have prior coursework in electronics, math, and programming. The concentration in Predictive Analytics teaches students to analyze unstructured and structured data to derive meaningful knowledge that suggests practical actions for the future in fields such as financial engineering, health care, transportation, and intelligence. The concentration in Statistical Analytics trains students to use statistical modeling and data visualization to derive insight from data, with a focus on deepening understanding of statistical concepts and techniques.
How much will the program cost?
Students finish their degree within two years and pay $930 per credit for a total of 30 credits, or just under $28,000 for the entire degree.
How does the program fit into my life?
In comparison with other data science Master’s programs, the George Mason program is somewhat less flexible because at least some courses will have scheduled meeting times for lectures and other activities. Some other classes will be asynchronous, meaning that instructors create learning materials such as lectures, activities, quizzes, assignments, and group discussions in weekly modules, and students are responsible for completing the module on their schedule within the given week. The program can be completed 100% online, and no campus visits are required. The program is usually completed in 2 years. Students can enter the program in the Fall or Spring semesters. The option to follow an individualized plan of study could be used as a way to make the program more relevant to your particular interests or the needs of your workplace, making your educational experience more efficient and motivating.
The George Mason University Master of Science in Data Analytics Engineering offers a vast array of fascinating courses that draw on the research wealth of this large university. GMU’s proximity to Washington, DC, is reflected in the program’s exciting offerings in the areas of intelligence, digital forensics, and application of data science to government problems. Applicants interested in careers in finance will also be well served by the curriculum, as will those who have an enthusiasm for innovative technologies. In comparison to similar programs at large high-level research universities, the GMU program is competitively priced. It would not be the first choice for students without significant prior quantitative and engineering coursework or professional experience.