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  • Faculty with Research Interest: Bio-X | Department of Statistics
    About Welcome History Awards Joint Research Contact Us Faculty with Research Interest Bio X Chiara Sabatti Phone 650 723 5082 Email sabatti stanford edu Faculty Courtesy Associate Professor Bayesian statistics computational biology statistical genetics Richard A Olshen Phone 650 725 2241 Email olshen stat stanford edu Faculty Courtesy Professor biostatistics tree structured methods Susan Holmes Phone 650 725 1925 Faculty Professor phylogenetic trees computational statistics bootstrap nonparametric computer intensive methods

    Original URL path: https://statistics.stanford.edu/joint-appointments/bio-x (2014-10-04)
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  • Faculty with Research Interest: Economics | Department of Statistics
    Academics Academic Programs Admissions Current Courses TA Resources Resources Technical Reports Consulting Services Computing Guide Room Requests Emergency Plan About Welcome History Awards Joint Research Contact Us Faculty with Research Interest Economics Joseph P Romano Phone 650 723 6326 Email romano stat stanford edu Faculty Professor bootstrap subsampling and resampling methods multiple hypothesis testing large sample theory econometrics Theodore W Anderson Phone 650 723 4732 Email twa stanford edu Faculty

    Original URL path: https://statistics.stanford.edu/joint-appointments/economics (2014-10-04)
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  • Faculty with Research Interest: Biology | Department of Statistics
    Associates Postdocs Students Staff Industrial Affiliates Members Conference Become a Member Academics Academic Programs Admissions Current Courses TA Resources Resources Technical Reports Consulting Services Computing Guide Room Requests Emergency Plan About Welcome History Awards Joint Research Contact Us Faculty with Research Interest Biology Wing Hung Wong Phone 650 725 2915 Email whwong stanford edu Faculty Professor computational biology multivariate analysis statistical inference machine learning FOR STUDENTS Computing Guide Explore Courses

    Original URL path: https://statistics.stanford.edu/joint-appointments/biology (2014-10-04)
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  • Faculty with Research Interest: Environmental Earth System Science | Department of Statistics
    Academics Academic Programs Admissions Current Courses TA Resources Resources Technical Reports Consulting Services Computing Guide Room Requests Emergency Plan About Welcome History Awards Joint Research Contact Us Faculty with Research Interest Environmental Earth System Science Bala Rajaratnam Phone 650 721 6404 Email brajarat stanford edu Faculty Assistant Professor multivariate analysis graphical models machine learning random matrix theory Paul Switzer Phone 650 723 2879 Email switzer stanfxxx edu Faculty Emeritus space

    Original URL path: https://statistics.stanford.edu/joint-appointments/environmental-earth-system-science (2014-10-04)
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  • Financial Support | Department of Statistics
    to apply for outside scholarships fellowships and other forms of financial support Students with outside support enable the department to stretch its own resources The department will supplement outside awards to the level set for departmental support New Financial Support Available Interdisciplinary training Due to a generous grant from the National Science Foundation the department has funds available to support students interested in acquiring training in statistics with an orientation towards interdisciplinary work The many joint appointments and co operative research activities of our faculty provide students with ample opportunity to become involved in such interdisciplinary work Stanford Graduate Fellowships A prestigious program begun in 1996 these fellowships are awarded by Stanford to selected applicants nominated by departments and are roughly comparable in benefits to National Science Foundation Graduate Fellowships The Stanford Fellowships are open both to foreign and U S applicants Biostatistics Training Program Jointly administered by the Department of Statistics and the medical school s Department of Health Research and Policy this program will address the growing shortage of biostatisticians proficient in skills for analyzing the vast amounts of health data being generated by sequencers microarrays and electronic medical records Teaching Assistantships As part of the doctoral program students are expected to act as teaching assistants Duties typically involve assisting a professor with grading of homework problems and or conducting weekly tutorial section meetings for elementary and intermediate statistics courses In later years students may be assistants for advanced courses Each quarter students are asked to list preferences for TA assignments which are accommodated where possible Tutorial section meetings provide students an opportunity to develop teaching skills Our Teaching Assistants Resources section contains advice about many aspects of TA work Documented teaching experience is an important factor in many post PhD job searches Through course evaluations and the

    Original URL path: https://statistics.stanford.edu/academics/phd-ext-finance (2014-10-04)
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  • MS in Statistics: Required Courses | Department of Statistics
    M S degree 1 Four Statistics core courses Probability Stats 116 Stochastic Processes Stats 217 Applied Statistics Stats 191 Theoretical Statistics Stats 200 All must be taken for a letter grade Note Students who have not had Probability may consider starting their program in the prior summer and taking it at that time Otherwise students should take Stats 116 in the autumn quarter followed by Stats 200 in the winter Stats 116 is also the prerequisite for Stats 217 Students with prior background may replace each course with a more advanced course from the same area Replace With Stats 116 Stats 217 Intro to Stochastic Processes Stats 218 Intro to Stochastic Processes Stats 219 Stochastic Processes or Stats 310A B or C Theory of Probability Stats 217 Stats 218 Intro to Stochastic Processes Stats 219 Stochastic Processes or Stats 310A B or C Theory of Probability Stats 191 Stats 203 Intro to Regression Models and Analysis of Variance or Stats 305 Intro to Statistical Modeling Stats 200 Stats 300A and 300B Theory of Statistics Students who replace 116 with 217 must take a second course in Stochastic Processes or Probability 2 Four additional Statistics courses Four additional courses from the

    Original URL path: https://statistics.stanford.edu/academics/ms-statistics-required-courses (2014-10-04)
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  • MS in Statistics: Elective Courses | Department of Statistics
    All courses from the list below are also approved electives Descriptions may be found in the Stanford Bulletin Other graduate courses 200 or above may be authorized by the program adviser if they provide skills relevant to statistics or deal primarily with an application of statistics or probability and do not overlap with courses in the student s program There is sufficient flexibility to accommodate students with interests in applications to business computing economics engineering health operations research and social sciences Courses below 200 level are generally not acceptable with the following exceptions Stats 116 191 Math 104 113 115 171 180 CS 106A 106B 106X 140 142 143 144 145 147 148 149 154 155 157 161 164 170 178 181 At most one of these courses may be counted Math 104 113 151 Stats 116 http exploredegrees stanford edu schoolofhumanitiesandsciences statistics masterstext Aeronautics 225 Biology 244 283 Biomedical Informatics 214 228 233 Civil Engineering 203 204 267 289 Computer Science 205 221 228 229 237 261 273A 274 Economics 202 203 210 271 272 273 274 275 276 281 284 286 287 288 291 Education 351 353A Electrical Engineering 248 261 278 364 368 372 373A 373B 379A

    Original URL path: https://statistics.stanford.edu/academics/ms-statistics-elective-courses (2014-10-04)
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  • MS in Statistics: Data Science | Department of Statistics
    higher Students satisfying the course requirement of the Data Science track do not have to satisfy the other course requirements for the MS in Statistics The total number of units in the degree is 45 36 of which must be taken for a letter grade Submission of approved Master s Program Proposal signed by the master s adviser to the student services specialist by the end of the first quarter of the master s degree program A revised program proposal is required to be filed whenever there are changes to a student s previously approved program proposal Data Science Program Proposal Form PDF DOCX Students must demonstrate breadth of knowledge in the field by completing five core areas as required by ICME Requirement 1 Mathematical Core 12 units These 12 units must be taken for a letter grade The recommended Mathematics core courses for the Data Science track are CME302 Linear Algebra CME304 Numerical Optimization or CME364A Convex Optimization CME305 Discrete Mathematics In addition to these three core courses the students are required to take a course in stochastics They can take either CME308 or an equivalent course approved by the steering committee Requirement 2 Advanced Scientific Programming and High Performance Computing Core 6 units To ensure that students have a strong foundation in programming all students will be required to take 6 units of advanced programming with at least 3 units in parallel computing These 6 units must be taken for a letter grade Approved courses include CME212 Advanced Programming for Scientists and Engineering CME214 Software Design in Modern Fortran for Scientists and Engineering CS107 Computer Organization and Systems CS249B Large Scale Software Development And for parallel HPC at least 3 units required CME213 Introduction to Parallel Computing using MPI openMP and CUDA CME342 Parallel Methods in Numerical Analysis CS149 Parallel Computing CS315A Parallel Computer Architecture and Programming CS315B Parallel Computing Research Project Also CS316 or CS344C Similar courses may be approved by the steering committee Students who do not start the program with a strong computational and or programming background will take an extra 3 units to prepare themselves by for example taking CME211 Programming in C C for Scientists and Engineers or an equivalent course such as CS106A B or CS106X For Data Science track students the 1 unit course in MapReduce offered by ICME annually is also highly recommended Requirement 3 Statistics Core 12 units The following 12 units must be taken for a letter grade The curriculum for the Data Science track requires 12 units of focused coursework in Statistics consisting of the following courses Stats200 Introduction to Statistical Inference Stats203 305 Regression Models Statistical Modeling Stats315A Modern Applied Statistics Learning Stats315B Modern Applied Statistics Data Mining or equivalent courses as approved by the steering committee Requirement 4 Domain Specialization or preparatory courses 9 units Three courses in specialized areas One or two of these courses may be used by the students that enter the program with insufficient linear algebra or programming experience to prepare

    Original URL path: https://statistics.stanford.edu/academics/ms-statistics-data-science (2014-10-04)
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