Quantitative graduate course offerings at UNR
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DepartmentCourse NumberCourse TitlePrimary quantitiative skillSecondary quantitative skill(s)Target audienceWhen offeredContact personWhat is coveredPrerequisitesWebpage/linksNotes/commentsCourse Description
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STATSTAT 459/659StatisticsPaul Hurtado
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STATSTAT 757Applied Regression AnalysisStatisticsprogramminggrads outside math/stat deptspringPaul Hurtadomultiple regression, poss. GLM, Logistic regressionvariable, depends on instructor. usually uses R
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STATSTAT 461/661ProbabilityStatisticsbasic foundational theoryfirst course in probPaul Hurtadostandard 1st course in prob, calc based.
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STATSTAT 467/667Intro to Stat theoryStatistics
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STATSTAT 452/652Intro to regression and linear modelsStatistics
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STATSTAT 420Mathematical ModelingMath Modeling programmingundergrad majors and minorsPaul Hurtado
intro to stochastic and ODE modeling basics; mechanistic modeling process
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BIOLBIOL 792Introduction to computational tools for (genomic) biologyProgrammingData management/ big dataGrad students interested in big data, especially genomicsTom ParchmanCovers unix and perl programmingcalc and some prob, stat basics helps
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PSYPSY 706/707Intro to stats 1 and 2Statistics
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NRESNRES 701CGraduate Environmental StatisticsStatisticsScripting in REvery fall semesterBen Sullivan
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NRESNRES 746Advanced Analysis Methods for Natural Resources and Environmental ScienceStatisticsProgrammingGraduate students in NRES, BIOL and EECBEvery other fall semester (even years)Kevin ShoemakerCovers model-based inference using maximum likelihood and Bayesian methodsIntroductory statistics, introductory computer programminghttps://kevintshoemaker.github.io/NRES-746/
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NRESNRES 485/685GIS ProgrammingProgrammingGISAdvanced undergraduates and graduate students with little programming experienceSpringJonathan Greenberg
Environmental Science are excited to announce a new course starting Spring 2018 that will focus on teaching basic and advanced programming skills using the R statistical computing language, and using these skills to expand geospatial analyses programmatically, including advanced techniques to import, manipulate, analyze, and export geospatial data. This course is well suited for students with no previous programming skills, and will cover such topics as Basic Programming, Debugging, Code Performance, Parallel Processing, Raster and Vector Processing, Spatial Modeling, Geostatistics, and Remote Sensing. This class will teach critical programming skills that will allow students to dramatically expand the types and scale of geospatial analyses
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GEOGGEOG 416/616Spatial AnalysisStatisticsSpatial Data management
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EECBEECB 750Research DesignStatisticsScripting in RIncoming EECB studentsEvery fall semester?Lee Dyer
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BUSINESSData management?
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Public HealthCovers SAS programming
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Comp SciCS 491/691Intro to BioinformaticsStatisticsScripting in Rundergraduate and graduate Every fall semesterTin Nguyenhypothesis testing (e.g., t-test, ANOVA, p-value adjustment), pathway analysis, gene expression, experiment designCS 365 (Mathematics of Computer Science) or STAT 352 (Probability and Statistics)
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Physics?
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Network modeling
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GEOGGEOG 407/607Advanced GIS and AnalysisGISStatistics, Data management
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CHS 780Biostatistics in Public Health
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CHS 7xxBiostatistics Theory
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CHS 753Health Informatics
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CHS 703RHealth Data Analysis
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CHS 7xx
Data Science and Statistical Computation in Public Health
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CHS 782Analysis of Categorical Data
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CHS 717Applied Bayesian Statistics
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CHS 7XXSurvival Analysis for Public Health
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CHS 786Biostatistical Analysis for Cohort Studies
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Stat 755Multivariate Data Analysis
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Stat 753Stochastic Models and Simulation (aka Applied Stochastic Processes)
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Stat 756Survival Analysis
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Stat 758Time Series
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STAT 645TBA (new course)Introduction to Statistical Computing
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STAT 745TBA (new course)Computational Methods in Statistics
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STAT 760TBA (new course)Statistical Learning
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BIOL 607Genomics and Bioinformatics
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