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The Data Analytics major is one of the first of its kind for a major research institution in the nation. Data analytics applies fundamental scientific principles to analyze large, complex data sets. This rapidly growing field needs practitioners with expertise that cuts across core disciplines of computer science, mathematics and statistics, and highly developed critical thinking, problem-solving and communication skills. It is a uniquely interdisciplinary major with academic partnerships rarely found in other majors. The major is co-directed by the Department of Statistics and the College of Engineering's Department of Computer Science and Engineering.
Data analytics majors receive a BS from the Arts and Sciences, but specialize in specific areas through curricular partnerships with the College of Engineering, the College of Medicine, the College of Public Health, and the Fisher College of Business. Current specializations include Biomedical and Public Health Analytics, Business Analytics, Computational Analytics, Data Vizualization, and Social Science Analytics.
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• Applying to graduate
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127 Pomerene Hall
Data analytics students share how they plan on applying thier education to their careers.
Through courses and a capstone experience, students investigate the principles of data representation and management, software design, statistical modeling and analysis, and the application of these concepts in areas such as business analytics, computational analytics and biomedical informatics.
Career opportunities are growing in fields unimagined a few years ago, in fields such as: Clinical Research, Sports Analytics, Cyber Security, Market Research, Genomic Sequencing, Health Information Systems, Machine Intelligence, Search Engine Development, Financial Fund Management, Multimedia artists and animators, Pharmaceutical Research, Aviation Management, and Insurance and Risk Management.
Introduction to concepts and methods for making decisions in the presence of uncertainty. Topics include: formulation of decision problems and quantification of their components; learning about unknown features of a decision problem based on data via Bayesian analysis; characterizing and finding optimal decisions. Techniques and computational methods for practical implementation are presented.
Knowledge discovery, data mining, data preprocessing, data transformations; clustering, classification, frequent pattern mining, anomaly detection, graph and network analysis; applications.
Statistical models for data analysis and discovery in big-data settings, with primary focus on linear regression models. The challenges of building meaningful models from vast data are explored, and emphasis is placed on model building and the use of numerical and graphical diagnostics for assessing model fit. Interpretation and communication of the results of analyses is emphasized.
Principles and methods for visualizing data from measurements and calculations in physical and life sciences, and transactional and social disciplines; information visualization; scientific visualization.