{"@id":"https://credentialengineregistry.org/resources/ce-a8891a6a-4ee7-4ce9-a062-8eebce83c9c3","@type":"ceterms:MasterDegree","@context":"https://credreg.net/ctdl/schema/context/json","ceterms:ctid":"ce-a8891a6a-4ee7-4ce9-a062-8eebce83c9c3","ceterms:name":{"en-US":"Master of Science in Interdisciplinary Data Science (Data Science - Scientific Computing - MS)"},"ceterms:ownedBy":["https://credentialengineregistry.org/resources/ce-5e249535-2207-4fa3-a883-22005113371f"],"ceterms:requires":[{"@type":"ceterms:ConditionProfile","ceterms:condition":{"en-US":["https://registrar.fsu.edu/bulletin/graduate-departments"]},"ceterms:creditValue":[{"@type":"ceterms:ValueProfile","schema:value":30.0,"ceterms:creditUnitType":[{"@type":"ceterms:CredentialAlignmentObject","ceterms:framework":"https://credreg.net/ctdl/terms/CreditUnit","ceterms:targetNode":"creditUnit:DegreeCredit","ceterms:frameworkName":{"en-US":"Credit Unit"},"ceterms:targetNodeName":{"en-US":"Degree Credit"},"ceterms:targetNodeDescription":{"en-US":"Credit that is issued or is accepted as credit for earning a college-level degree."}}]}],"ceterms:description":{"en-US":"Program Requirements"}}],"ceterms:offeredBy":["https://credentialengineregistry.org/resources/ce-5e249535-2207-4fa3-a883-22005113371f"],"ceterms:identifier":[{"@type":"ceterms:IdentifierValue","ceterms:identifierTypeName":{"en-US":"FLVC Public Status"},"ceterms:identifierValueCode":"Public"}],"ceterms:inLanguage":["en-US"],"ceterms:availableAt":[{"@type":"ceterms:Place","ceterms:latitude":30.4409616,"ceterms:longitude":-84.2906658,"ceterms:postalCode":"32306","ceterms:addressRegion":{"en-US":"FL"},"ceterms:streetAddress":{"en-US":"600 W College Ave"},"ceterms:addressLocality":{"en-US":"Tallahassee"}}],"ceterms:description":{"en-US":"The Florida State University College of Arts and Sciences and the Departments of Computer Science, Mathematics, Scientific Computing, and Statistics offer a Master's of Science Degree in Interdisciplinary Data Science (MS-IDS) that provides students a unique and broad educational experience across the four foundational areas of Data Science. The program consists of 1) a common core of 18-credit course work, and 2) at least four additional three- or four-credit electives that define a major in one of the participating areas. The program requires a minimum of 30 credits and can be completed in three academic semesters. Additional information can be found at https://datascience.fsu.edu/ and on the individual departmental websites."},"ceterms:subjectWebpage":"https://datascience.fsu.edu/","ceterms:credentialStatusType":{"@type":"ceterms:CredentialAlignmentObject","ceterms:framework":"https://credreg.net/ctdl/terms/CredentialStatus","ceterms:targetNode":"credentialStat:Active","ceterms:frameworkName":{"en-US":"Credential Status"},"ceterms:targetNodeName":{"en-US":"Active"},"ceterms:targetNodeDescription":{"en-US":"Awards of the credential are ongoing."}},"ceterms:learningDeliveryType":[{"@type":"ceterms:CredentialAlignmentObject","ceterms:framework":"https://credreg.net/ctdl/terms/Delivery","ceterms:targetNode":"deliveryType:InPerson","ceterms:frameworkName":{"en-US":"Delivery Type"},"ceterms:targetNodeName":{"en-US":"In-Person Only"},"ceterms:targetNodeDescription":{"en-US":"Delivery is only face-to-face."}}],"ceterms:instructionalProgramType":[{"@type":"ceterms:CredentialAlignmentObject","ceterms:framework":"https://nces.ed.gov/ipeds/cipcode/Default.aspx?y=56","ceterms:targetNode":"https://nces.ed.gov/ipeds/cipcode/cipdetail.aspx?y=56\u0026cip=30.7001","ceterms:codedNotation":"30.7001","ceterms:frameworkName":{"en-US":"Classification of Instructional Programs"},"ceterms:targetNodeName":{"en-US":"Data Science, General."},"ceterms:targetNodeDescription":{"en-US":"A program that focuses on the analysis of large scale data sources from the interdisciplinary perspectives of applied statistics, computer science, data storage, data representation, data modeling, mathematics, and statistics. Includes instruction in computer algorithms, computer programming, data management, data mining, information policy, information retrieval, mathematical modeling, quantitative analysis, statistics, trend spotting, and visual analytics."}}]}