{"@id":"https://credentialengineregistry.org/resources/ce-c846f054-3ee3-4422-a392-d7d6d1506b31","@type":"ceterms:MasterDegree","@context":"https://credreg.net/ctdl/schema/context/json","ceterms:ctid":"ce-c846f054-3ee3-4422-a392-d7d6d1506b31","ceterms:name":{"en-US":"M.S. in Data Science in Finance"},"ceterms:ownedBy":["https://credentialengineregistry.org/resources/ce-5b250f35-7527-4115-9e5c-c49dd1dbed4e"],"ceterms:identifier":[{"@type":"ceterms:IdentifierValue","ceterms:identifierTypeName":{"en-US":"CHEDSS Program ID"},"ceterms:identifierValueCode":"61041"}],"ceterms:inLanguage":["en"],"ceterms:description":{"en-US":"The goal of the program is to equip students with the tools necessary to pursue a career in a quantitative financial field. The 2-year course work provides students with comprehensive and practical knowledge of the mathematical, statistical, and computational skills. The following courses are offered as guides. They should be adapted to suit a student's needs and an advisory committee's recommendation. This degree requires 33 credit hours plus an oral final exam. Students in this program are also required to take at least one Data Science in Finance seminar course."},"ceterms:subjectWebpage":"https://catalog.purdue.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=27.0502","ceterms:codedNotation":"27.0502","ceterms:frameworkName":{"en-US":"Classification of Instructional Programs"},"ceterms:targetNodeName":{"en-US":"Mathematical Statistics and Probability"},"ceterms:targetNodeDescription":{"en-US":"A program that focuses on the mathematical theory underlying statistical methods and their use.  Includes instruction in probability theory parametric and non-parametric inference, sequential analysis, multivariate analysis, Bayesian analysis, experimental design, time series analysis, resampling, robust statistics, limit theory, infinite particle systems, stochastic processes, martingales, Markov processes, and Banach spaces."}}]}