Course overview
The School of Engineering and Computing offers a master's degree in data science (MSDS). Advances in technology have contributed to a deluge of data in virtually any domain. Computational techniques are being developed to store, process, and interpret data. New insights into data contribute to increased productivity, correlations among previously distinctive domains, and improved decision-making. Data science is an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured.
It employs techniques and theories drawn from many fields within the broad areas of statistics and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, uncertainty quantification, computational science, data mining, databases, and visualization. At the same time, it requires domain-specific knowledge to apply the techniques and theories effectively. Bob Hayes, Chief Research Officer at AnalyticsWeek thinks of 'data science' as "a flag that was planted at the intersection of several different disciplines that have not always existed in the same place." Current Data Science programs are either too technical or too specialized. As an interdisciplinary program, the Master of Science in Data Science (MSDS) decouples the computational techniques from the domains of interest, thus allowing all students to learn data science techniques, while concentrating on learning about data in one or more domains of interest.
