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

MS in Data Analytics Engineering - Concentration in Statistics for 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 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.

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 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.

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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