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Course Criteria
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3.00 Credits
This course covers the role of the scientific method in applied data science with a focus on topics such as incomplete data, temporal cadence, systematic biases, rejection of outlier data, constraints on the mathematical modeling systems arising from underlying scientific considerations, experimental design, signal to noise, time-dependent modeling, ensemble modeling, statistical image analysis, and scientific numerical methods. NOTE: Please refer to the appropriate academic catalog for additional course information concerning prerequisites, co-requisites and course restrictions.
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3.00 Credits
This course will cover the systems and strategies for cleaning, wrangling, organizing, querying, and visualization of data streams and big data. NOTE: Please refer to the appropriate academic catalog for additional course information concerning prerequisites, co-requisites and course restrictions.
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3.00 Credits
A course that introduces the student to the basic concepts, organization and implementation models of databases, with an emphasis on the relational model. Among the topics covered are data models, query languages, relational database design using normal forms and database programming, and information assurance and security. Problems will be assigned using a relational DBMS and SQL.
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3.00 Credits
This course will cover the concepts and methods of machine learning, analytics, and data mining. Students will implement and use state-of-the-art machine learning algorithms for knowledge discovery. NOTE: Please refer to the appropriate academic catalog for additional course information concerning prerequisites, co-requisites and course restrictions.
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3.00 Credits
An intensive investigation of an area of current interest in data science and analytics.
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3.00 Credits
This course consists of individual study of an agreed-upon topic under the direction of a faculty member and following a course of reading and other requirements proposed by the faculty member or student and established by negotiation with the director. This course is intended to provide graduate students with an opportunity to study in an area of data science that is not generally offered.
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3.00 Credits
Students will demonstrate their knowledge, skills, and dispositions for performing applied data science assignments in a professional setting. Students already employed in a data science field must perform additional data science tasks outside of their preexisting responsibilities. It is expected that 120 contact/work hours will be completed for this 3 credit hours. The course will be graded on a pass-fail basis.
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3.00 Credits
Thesis in Data Science and Analytics is a three credit hour course for the completion of a formal master's degree thesis under faculty direction. A research thesis is a traditional research project characterized by a comprehensive paper on a research topic. The course will be graded on a pass-fail basis.
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3.00 Credits
An exploration of various roles of culture and the arts in American democracy. Topics addressed may include arts law, public art, free expression, cultural policy, arts advocacy,art and protest, cultural expression, monuments and memorials, public funding of the arts, art-based placemaking, the arts and community outreach.
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6.00 Credits
An internship designed to provide the student with the opportunity to explore issues about the role of the arts in a democracy through experiential learning, by working in an arts, cultural or political organization in Washington DC.
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