MS in Data Analytics Engineering - Concentration in Predictive Analytics Postgraduate Programme By George Mason University |TopUniversities

MS in Data Analytics Engineering - Concentration in Predictive Analytics

Main Subject Area

Engineering - GeneralMain Subject Area

Programme overview

Main Subject

Engineering - General

Study Level

Masters

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. 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.

Programme overview

Main Subject

Engineering - General

Study Level

Masters

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. 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.

Admission Requirements

6.5+
Other English Language requirements: Students are required to have paper-based TOEFL of 570 and 230 on the computer-based TOEFL ; overall band score of 59 on the Pearson Test of English.

Jan-2000

Domestic
0 USD
International
0 USD

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