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ASA Guidelines for Undergraduate Programs in Statistical Science: TOPICSSTATISTICAL METHODS AND THEORYCourse(s) in which topic is covered:CurrentNewDesign studiesUse graphical and other means to explore dataBuild and assess statistical modelsEmploy a variety of formal inference procedures (including resampling methods)Draw appropriate conclusions Foundation in theoretical statistics principles for sound analyses (modern)Statistical TheoryDistributions of random variablesLikelihood theoryPoint and interval estimationHypothesis testingDecision theoryBayesian methodsResampling methods (bootstrapping and permutation tests)Exploratory Data AnalysisVisualization (including advanced)Visualization early for errors and anomaliesSmoothing/kernel estimationSpatial methodsMappingDesign of StudiesData collectionRandom assignmentBlocking and stratificationAdaptive designsEfficiency (power?)Issues of biasRandom selectionSurvey samplingCausalityConfounding and coincidenceStatistical ModelsSimple linear regressionMultiple regressionGeneralized linear modelsModel selectionDiagnosticsCross-validationMixed modelsTime SeriesSurvival analysisGeneralized additive modelsRegression treesStatistical and machine learning techniquesSpatial analysisMultivariate methodsRegularizationDATA SCIENCECourse(s) in which topic is covered:CurrentNewAble to program in a higher level language (write functions, utilize control flow in a variety of languages and tools such as Python, R, SAS, or Stata)Think algorithmicallyUse simulation-based statistical techniquesUndertake simulation studiesManage and manipulate data, including joining data from different sources and formats and restructure data into a form suitable for analysisWell-documented and reproducible waySoftware and toolsUse of professional statistical softwareUse of multiple data toolsAccessing and Manipulating DataAccess data and manipulate data in various waysJudge data qualityMethods for addressing missing dataWork with csvWork with JSON (javascript object notation)Work with XMLWork with databases, database systemsWork with text dataWell-documented and reproducibleBasic Programming ConceptsBreaking down a problem into modular piecesAlgorithmic thinkingStructured programmingDebuggingEfficiencyComputationally intensive statistical methodsIterative methodsOptimizationResamplingSimulation/monte carlo methodsMATHEMATICAL FOUNDATIONSCourse(s) in which topic is covered:CurrentNewCalculus (integration and differentiation)Linear algebraProbabilityEmphasis on connections between these concepts and their applications in statisticsSTATISTICAL PRACTICECourse(s) in which topic is covered:CurrentNewWrite clearly, construct compelling written summariesSpeak fluentlyConstruct effective visual displaysCollaborate in teams, organize and manage projectsCommunicate complex statistical methods in basic terms and show results in an accessible mannerCommunicationEffective technical writingEffective presentation skillsEffective visualizationsCollaborationTeamwork and collaborationAbility to interact and communicate with a variety of clients and collaboratorsOpportunities for PracticeInternshipsSenior-level capstone courseResearch experiencesConsulting experiencesPROBLEM SOLVINGCourse(s) in which topic is covered:CurrentNewComplex, open-ended problemsTackle real research questionsAbility to solve complex problems out of contextAbility to deal with messy or unstructured dataScientific method and statistical problem-solving cycleFormulating good questionsAssessing appropriateness of dataChoosing from a set of different toolsUndertaking the analyses in a reproducible mannerAssessing the analytic methodsDrawing appropriate conclusionsCommunicating resultsDISCIPLINE-SPECIFIC KNOWLEDGECourse(s) in which topic is covered:CurrentNewApply statistical reasoning to domain specific questionsTranslate research questions into statistical questionsCommunicate results appropriate to different disciplinary audiencesEncourage study in a substantive area of application ................
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