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1StatisticsSTATISTICSCourses offered by the Department of Statistics are listed underthe subject code STATS on the Stanford Bulletin's CourseSearch/search/?view catalog&catalog &page 0&q STATS&filter-catalognumberSTATS on) web site.The department's goals are to acquaint students with the role playedin science and technology by probabilistic and statistical ideas andmethods, to provide instruction in the theory and application oftechniques that have been found to be commonly useful, and to trainresearch workers in probability and statistics. There are courses forgeneral students as well as those who plan careers in statistics inbusiness, government, industry, and teaching.The department has long recognized the relation of statisticaltheory to applications. It has fostered this by encouraging a liaisonwith other departments in the form of joint and courtesy facultyappointments, as well as membership in various interdisciplinaryprograms: Biomedical Data Science, Bio-X, Center for Computational,Evolutionary and Human Genomics, Computer Science, Economics,Education, Electrical Engineering, Environmental Earth System Science,Genetics, Mathematics, Mathematical and Computational Finance, andMedicine. The research activities of the department reflect an interest inapplied and theoretical statistics and probability. There are workshops inbiology/medicine and in environmental factors in health.In addition to courses for Statistics students, the department offers anumber of service courses designed for students in other departments.These tend to emphasize the application of statistical techniques ratherthan their theoretical development.The department has always drawn visitors from other countries anduniversities, and as a result there are a wide range of seminars offered byboth the visitors and the department's own faculty.Undergraduate Programs in StatisticsThe department offers a minor in Statistics and in Data Science aduate-programs/). Programdetails can be found under the Minor section.Undergraduates Interested in StatisticsStudents wishing to build a concentration in probability and statisticsare encouraged to consider declaring a major in Mathematical andComputational Science ( This interdisciplinaryprogram is administered in the Department of Statistics and providescore training in computing, mathematics, operations research, andstatistics, with opportunities for further elective work and specialization.See the "Mathematical and Computational Science" section of thisbulletin.Graduate Programs in StatisticsUniversity requirements for the M.S. and Ph.D. degrees are discussedin the "Graduate Degrees s/)" section of this bulletin.Learning Outcomes (Graduate)The purpose of the master's program is to further develop knowledge andskills in Statistics and to prepare students for a professional career ordoctoral studies. This is achieved through completion of courses, in theprimary field as well as related areas, and experience with independentwork and specialization.and analysis in Statistics. Through completion of advanced course workand rigorous skills training, the doctoral program prepares studentsto make original contributions to the knowledge of Statistics and tointerpret and present the results of such research.The Department of Statistics offers two minor programs forundergraduates, a minor in Data Science (p. 1) and a minor inStatistics (p. 2). To declare either minor for a degree program,visit the Statistics website undergraduate-programs/) and submit the appropriate form tothe department.Minor in Data ScienceThe undergraduate Data Science minor has been designed for majors inthe humanities and social sciences who want to gain practical knowledgeof statistical data analytic methods as it relates to their field of interest.The minor: provides students with the knowledge of exploratory andconfirmatory data analyses of diverse data types such as text,numbers, images, graphs, trees, and binary input) strengthens social research by teaching students how to correctlyapply data analysis tools and the techniques of data visualization toconvey their conclusions.No previous programming or statistical background is assumed.Learning OutcomesStudents are expected to:1. be able to connect data to underlying phenomena and to thinkcritically about conclusions drawn from data analysis.2. be knowledgeable about programming abstractions so that they canlater design their own computational inferential proceduresAll courses for the minor must be taken for a letter grade, with theexception of the Data Mining requirement.Seven courses are required, 22 units minimum. An overall 2.75 gradepoint average (GPA) is required for courses fulfilling the minor.RequirementsLinear AlgebraUnitsOne of the following:MATH 51Linear Algebra, Multivariable Calculus, andModern ApplicationsCME 100Vector Calculus for EngineersProgrammingCS 106AProgramming MethodologyProgramming in R55Units3-5UnitsOne of the following:THINK 3STATS 32Introduction to R for UndergraduatesSTATS 48NRiding the Data WaveSTATS 195Introduction to ROr other course that teaches proficiency in R programming.4131The Ph.D. is conferred upon candidates who have demonstratedsubstantial scholarship and the ability to conduct independent researchStanford Bulletin 2020-21

2StatisticsData ScienceSTATS 101STATS 191CS 102MS&E 226Data Science 101Introduction to Applied StatisticsUnits53Fundamentals of Data Science: Prediction,Inference, CausalityStatistics3UnitsOne of the following:ECON 102AIntroduction to Statistical Methods(Postcalculus) for Social ScientistsPHIL 166Probability: Ten Great Ideas About ChanceSTATS 48NRiding the Data WaveSTATS 141BiostatisticsSTATS 191Introduction to Applied StatisticsSTATS 211Meta-research: Appraising ResearchFindings, Bias, and Meta-analysis543533Data Mining and AnalysisSTATS 202STATS 216Data Mining and Analysis (may be takenCR/NC)Introduction to Statistical LearningElective CourseOne course fulfilling Data Science methodology from cognatefield of interest. Suggested courses:CS 224WMachine Learning with GraphsECON 291Social and Economic NetworksENGLISH 184ELiterary Text MiningLINGUIST 275Probability and Statistics for linguistsMS&E 135NetworksPHIL 166Probability: Ten Great Ideas About ChancePOLISCI 150BMachine Learning for Social ScientistsPOLISCI 450APolitical Methodology I: RegressionPSYCH 109An introduction to computation andcognitionPUBLPOL 105Empirical Methods in Public PolicySOC 126Introduction to Social NetworksSOC 180AFoundations of Social Researchor SOC 180BIntroduction to Data Analysis33Units3-43-552-4345544-544*STATS 191 cannot count for both requirements.return to top (p. 1)Minor in StatisticsThe undergraduate minor in Statistics is designed to complementmajor degree programs primarily in the social and natural sciences.Students with an undergraduate Statistics minor should find broadenedpossibilities for employment. The Statistics minor provides valuablepreparation for professional degree studies in postgraduate academicprograms.The minor consists of a minimum of six courses with a total of at least19 units. There are two required courses (8 units) and four qualifyingor elective courses (12 or more units). All courses for the minor mustStanford Bulletin 2020-21be taken for a letter grade. An overall 2.75 grade point average (GPA) isrequired for courses fulfilling the minor.Required CoursesBoth:STATS 116STATS 200UnitsTheory of ProbabilityIntroduction to Statistical Inference44Qualifying CoursesAt most, one of these two courses may be counted toward the six courserequirement for the minor:UnitsChoose one from the following:MATH 52Integral Calculus of Several VariablesSTATS 191Introduction to Applied Statistics53Three Elective CoursesAt least one of the elective courses should be a STATS 200-levelcourse. The remaining two elective courses may also be 200-levelcourses. Alternatively, one or two elective courses may be approvedcourses in other departments. Special topics courses and seminars forundergraduates are offered from time to time by the department, andthese may be counted toward the course requirement. Students may notcount any Statistics courses below the 100 level toward the minor.Examples of elective course sequences are:Data Analysis and Applied StatisticsSTATS 202Data Mining and AnalysisSTATS 203Introduction to Regression Models andAnalysis of VarianceStatistical MethodologySTATS 205Introduction to Nonparametric StatisticsSTATS 206Applied Multivariate AnalysisSTATS 207Introduction to Time Series AnalysisEconomic OptimizationSTATS 206Applied Multivariate AnalysisECON 160Game Theory and Economic ApplicationsPsychology Modeling and ExperimentsSTATS 206Applied Multivariate AnalysisSignal ProcessingSTATS 207Introduction to Time Series AnalysisEE 264Digital Signal ProcessingEE 279Introduction to Digital CommunicationGenetic and Ecologic ModelingSTATS 217Introduction to Stochastic Processes IBIO 283Theoretical Population GeneticsProbability and ApplicationsSTATS 217Introduction to Stochastic Processes ISTATS 218Introduction to Stochastic Processes IIMathematical FinancesSTATS 240Statistical Methods in FinanceSTATS 243Risk Analytics and Management in Financeand InsuranceSTATS 250Mathematical Financereturn to top (p. 1)Units333333533333333333

3StatisticsMaster of Science in StatisticsThe University’s basic requirements for the M.S. degree are discussedin the “Graduate Degrees” s/) section of this bulletin. The following are specificdepartmental requirements.The M.S. in Statistics and the M.S. in Statistics, Data Science track,are intended as terminal degree programs and do not lead to the Ph.D.program in Statistics. Students interested in pursuing doctoral study inStatistics should apply directly to the Ph.D. program.AdmissionProspective applicants should consult the Graduate Admissions( and the Statistics Departmentadmissions webpages te-application-information-and-instructions/) for completeinformation on admission requirements and deadlines.Recommended preparatory courses include advanced undergraduatelevel courses in linear algebra, statistics/probability and proficiency inprogramming.Stanford students interested in the Data Science track (subplan) inStatistics must apply as external candidates. Visit Graduate Admissions( to start an application.Coterminal Master's Programmay still be open. The University also requires that the Master’s DegreeProgram Proposal be completed by the student and approved by thedepartment by the end of the student’s first graduate quarter.Master of Science in StatisticsCurriculum and Degree RequirementsThe department requires that a master's student take 45 unitsof work from offerings in the Department of Statistics ( catalog&filter-coursestatusActive on&page 0&catalog &academicYear &q STATS&collapse ) orfrom authorized courses in other departments. With the advice of themaster's program advisors, each student selects his or her own set ofelectives.All requirements for a master's degree, including the coterminal master'sdegree, must be completed within three years after the student's firstterm of enrollment in the master's program. Ordinarily, four or fivequarters are needed to complete all requirements. Honors Cooperativestudents must finish within five years.Units for a given course may not be counted to meet the requirements ofmore than one degree, with the exception that up to 45 units of a StanfordM.A. or M.S. degree may be applied to the residency requirement for thePh.D., D.M.A. or Engineer degrees. See the "Residency Policy for GraduateStudents s/#residencytext)" section of this Bulletin for University rules.Stanford undergraduates who want to apply for the coterminalmaster's degree in Statistics must submit a complete application to thedepartment by the deadline published on the department's coterminaladmissions webpage. tistics-coterm-eligibility/)As defined in the general graduate student requirements, students mustmaintain a grade point average (GPA) of 3.0 (or better) for courses usedto fulfill degree requirements and classes must be taken at the 200 levelor higher.Applications are accepted twice a year in autumn and winter quartersfor winter and spring quarter start, respectively. The general GRE is notrequired of coterminal applicants.The Statistics Master's Degree Program Proposal form /graduate-programs/msprogram-forms/) must be signed and approved by the department'sstudent services administrator before submission to the student'sprogram advisor. This form is due no later than the end of the first quarterof enrollment in the program.Students pursuing the Statistics coterminal master's degree must followthe same curriculum requirements stated in the Requirements for theMaster of Science in Statistics section.University Coterminal RequirementsCoterminal master’s degree candidates are expected to complete allmaster’s degree requirements as described in this bulletin. Universityrequirements for the coterminal master’s degree are described in the“Coterminal Master’s Program )” section. University requirements for themaster’s degree are described in the "Graduate Degrees s/#masterstext)" section ofthis bulletin.After accepting admission to this coterminal master’s degree program,students may request transfer of courses from the undergraduate to thegraduate career to satisfy requirements for the master’s degree. Transferof courses to the graduate career requires review and approval of boththe undergraduate and graduate programs on a case by case basis.In this master’s program, courses taken during or after the first quarterof the sophomore year are eligible for consideration for transfer to thegraduate career; the timing of the first graduate quarter is not a factor.No courses taken prior to the first quarter of the sophomore year may beused to meet master’s degree requirements.Course transfers are not possible after the bachelor’s degree has beenconferred.The University requires that the graduate advisor be assigned in thestudent’s first graduate quarter even though the undergraduate careerMaster's Degree Program ProposalA revised program proposal must be submitted if degree plans change.There is no thesis requirement.For further information about the Statistics master's degreeprogram requirements, see the program's webpage istics/).1. Statistics Core Courses (must complete all fourcourses):UnitsProbabilitySTATS 116Applied StatisticsSTATS 203Theory of Probability14Introduction to Regression Models andAnalysis of VarianceApplied Statistics IIntroduction to Applied Statisticsor STATS 305Aor STATS 191Theoretical StatisticsSTATS 200Introduction to Statistical Inferenceor STATS 300ATheory of Statistics Ior STATS 370A Course in Bayesian StatisticsStochastic Processes1,2STATS 217Introduction to Stochastic Processes I33-43Stanford Bulletin 2020-21

4Statisticsor STATS 218Introduction to Stochastic Processes IIor STATS 219Stochastic Processesor STATS 318Modern Markov ChainsStudents with prior background may replace each course with amore advanced course from the same area, or a more advancedcourse offered by the department, with consent of the adviser. Allmust be taken for a letter grade.or STATS 300CSTATS 305Aor STATS 305B2. Statistics Depth:Five additional Statistics courses must be taken from graduate offeringsin the department (at or above the 200-level). During the 2020-21academic year, three of five courses must be taken for a letter grade (withthe exception of courses that may only be offered satisfactory(S)/credit(CR) only).3The following courses that may only be used to fulfill elective credit :STATS 260A Workshop in Biostatistics series, STATS 299 IndependentStudy, STATS 298 Industrial Research for Statisticians, and STATS 390Consulting Workshop (see list of electives below).UnitsCourses which may be offered by the department:STATS 202Data Mining and AnalysisSTATS 203Introduction to Regression Models andAnalysis of Variance (STATS 203V)STATS 204SamplingSTATS 205Introduction to Nonparametric StatisticsSTATS 206Applied Multivariate AnalysisSTATS 207Introduction to Time Series AnalysisSTATS 208Bootstrap, Cross-Validation, and SampleRe-useSTATS 209ATopics in Causal InferenceSTATS 211Meta-research: Appraising ResearchFindings, Bias, and Meta-analysisSTATS 215Statistical Models in BiologySTATS 216Introduction to Statistical LearningSTATS 222Statistical Methods for LongitudinalResearchSTATS 229Machine Learningor CS 229Machine LearningSTATS 237Investment Portfolios, Derivative Securities,and Risk MeasuresSTATS 240Statistical Methods in FinanceSTATS 241Data-driven Financial EconometricsSTATS 244Quantitative Trading: Algorithms, Data, andOptimizationSTATS 245Data, Models and Applications toHealthcare AnalyticsSTATS 250Mathematical FinanceSTATS 263Design of ExperimentsSTATS 266Advanced Statistical Methods forObservational StudiesSTATS 270A Course in Bayesian Statisticsor STATS 370A Course in Bayesian StatisticsSTATS 271Applied Bayesian Statisticsor STATS 371Applied Bayesian StatisticsSTATS 285Massive Computational Experiments,PainlesslySTATS 290Computing for Data ScienceSTATS 300ATheory of Statistics Ior STATS 300BTheory of Statistics IIStanford Bulletin 2020-21333333333332-33-43332-4or STATS 305CSTATS 310Aor STATS 310Bor STATS 310CSTATS 311or EE 377STATS 314ASTATS 315ASTATS 315BSTATS 317STATS 318STATS 319STATS 322STATS 325STATS 334STATS 359or MATH 273STATS 361STATS 364STATS 363STATS 366STATS 374or MATH 234STATS 368STATS 369STATS 376ASTATS 376Bor EE 376BSTATS 385Theory of Statistics IIIApplied Statistics I3Applied Statistics II: Generalized Linear Models,Survival Analysis, and Exponential FamiliesApplied Statistics IIITheory of Probability I3Theory of Probability IITheory of Probability IIIInformation Theory and Statistics3Information Theory and StatisticsAdvanced Statistical Theory3Modern Applied Statistics: Learning3Modern Applied Statistics: Data Mining3Stochastic Processes3Modern Markov Chains3Literature of Statistics1Function Estimation in White Noise3Multivariate Analysis and Random Matrices3in StatisticsMathematics and Statistics of Gambling3Topics in Mathematical Physics3Topics in Mathematical PhysicsCausal Inference ((NEW))3Theory and Applications of Selective3Inference ((NEW))Design of Experiments3Modern Statistics for Modern Biology3Large Deviations Theory3Large Deviations TheoryEmpirical Process Theory and its3ApplicationsMethods from Statistical Physics3Information Theory3Topics in Information Theory and Its3ApplicationsTopics in Information Theory and Its ApplicationsAnalyses of Deep Learning13. Linear Algebra Requirement:Units3Must be taken for a letter grade, with the exception of coursesoffered satisfactory/no credit only.Select one of the following:MATH 104Applied Matrix TheoryMATH 113Linear Algebra and Matrix TheoryMATH 115Functions of a Real VariableMATH 171Fundamental Concepts of AnalysisCME 302Numerical Linear AlgebraCME 364AConvex Optimization Ior CME 364BConvex Optimization IISubstitution of more advanced courses in Mathematics, thatprovide similar skills, may be made with consent of the adviser.24. Programming Requirement:3332-3333333333Units2020-21: May be taken for a letter grade or CR.Select one of the following:CS 106AProgramming Methodology3

CS 106BProgramming

THINK 3 4 STATS 32 Introduction to R for Undergraduates 1 . STATS 116 Theory of Probability 4 STATS 200 Introduction to Statistical Inference 4 Qualifying Courses At most, one of these two courses may be counted toward the six course . level courses in linear algebra, statistics/probability and proficiency in programming.

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