statistics

For admission for an Option A Minor in statistics, the candidate must have had at least one year of calculus, and an introductory knowledge of statistics that is satisfactory to the department. Any of the following (or an equivalent course) is sufficient for this purpose:

STAT 240 Introduction to Data Modeling I4
STAT 301 Introduction to Statistical Methods3
STAT 302 Accelerated Introduction to Statistical Methods3
STAT 324 Introductory Applied Statistics for Engineers3
STAT 371 Introductory Applied Statistics for the Life Sciences3
STAT/​B M I  541 Introduction to Biostatistics3
STAT/​F&W ECOL/​HORT  571 Statistical Methods for Bioscience I4

GENERAL REQUIREMENTS FOR AN OPTION-A MINOR IN STATISTICS FOR GRADUATES:

Please carefully read the requirements below. Requests for further information should be addressed to the PhD Minor Advisor in the Department of Statistics.  Note: Candidates for an Option A Minor in Statistics must be aware of the Graduate School "Minors" policy.  For further information please visit this link: https://stat.wisc.edu/statistics-doctoral-minor/

The student should have a program of study approved by the PhD Minor Advisor in the Department of Statistics and the student's major advisor, early in the student's graduate work. The proposed program should be submitted to and approved by the minor program advisor in statistics upon, or before, the completion of 6 credits. 

Students must take at least four courses totaling 12 or more credits from the following lists:
List 1 (at least one course):
STAT 303 R for Statistics I1
STAT 304 R for Statistics II1
STAT 305 R for Statistics III1
STAT 327 Learning a Statistical Language1
STAT 333 Applied Regression Analysis3
STAT 340 Introduction to Data Modeling II4
STAT 349 Introduction to Time Series3
STAT 351 Introductory Nonparametric Statistics3
STAT 411 An Introduction to Sample Survey Theory and Methods3
STAT 421 Applied Categorical Data Analysis3
STAT/​M E  424 Statistical Experimental Design3
STAT 433 Data Science with R3
STAT 443 Classification and Regression Trees3
STAT 451 Introduction to Machine Learning and Statistical Pattern Classification3
STAT 453 Introduction to Deep Learning and Generative Models3
STAT 456 Applied Multivariate Analysis3
STAT 461 Financial Statistics3
STAT/​COMP SCI  471 Introduction to Computational Statistics3
STAT 479 Special Topics in Statistics1-3
STAT/​B M I  542 Introduction to Clinical Trials I3
STAT/​F&W ECOL/​HORT  572 Statistical Methods for Bioscience II4
STAT 575 Statistical Methods for Spatial Data3
STAT/​B M I  641 Statistical Methods for Clinical Trials3
STAT/​B M I  642 Statistical Methods for Epidemiology3
STAT 679 Special Topics in Statistics1-3
STAT 732 Large Sample Theory of Statistical Inference3
STAT/​B M I  741 Survival Analysis Theory and Methods3
STAT 760 Multivariate Analysis I3
STAT 761 Decision Trees for Multivariate Analysis3
STAT/​B M I  768 Statistical Methods for Medical Image Analysis3
STAT 771 Statistical Computing3
STAT/​ECON/​GEN BUS  775 Introduction to Bayesian Decision and Control I3
STAT 801 Advanced Financial Statistics3
STAT/​MATH  803 Experimental Design I3
STAT 809 Non Parametric Statistics3
STAT 811 Sample Survey Theory and Method3
STAT 834 Empirical Processes and Semiparametric Inference1-3
STAT 840 Statistical Model Building and Learning3
STAT 841 Nonparametric Statistics and Machine Learning Methods3
STAT 849 Theory and Application of Regression and Analysis of Variance I3
STAT 850 Theory and Application of Regression and Analysis of Variance II3
STAT 860 Estimation of Functions from Data3
STAT/​COMP SCI/​E C E  861 Theoretical Foundations of Machine Learning3
STAT/​B M I  877 Statistical Methods for Molecular Biology3
STAT 992 Seminar1-3
List 2 (at most one course) :
STAT/​MATH  309 Introduction to Probability and Mathematical Statistics I3
STAT 311 Introduction to Theory and Methods of Mathematical Statistics I3
STAT 609 Mathematical Statistics I3
STAT/​MATH  709 Mathematical Statistics4
List 3 (at most one course):
STAT/​MATH  310 Introduction to Probability and Mathematical Statistics II3
STAT 312 Introduction to Theory and Methods of Mathematical Statistics II3
STAT 610 Introduction to Statistical Inference4
STAT/​MATH  710 Mathematical Statistics4
List 4 (at most one course):
STAT/​MATH  431 Introduction to the Theory of Probability3
STAT/​COMP SCI/​MATH  475 Introduction to Combinatorics3
STAT/​COMP SCI/​I SY E/​MATH  525 Linear Optimization3
STAT/​I SY E/​MATH/​OTM  632 Introduction to Stochastic Processes3
STAT/​COMP SCI/​I SY E/​MATH  726 Nonlinear Optimization I3
STAT/​MATH  733 Theory of Probability I3
STAT/​MATH  833 Topics in the Theory of Probability3
OR another course approved by the Ph.D. minor advisor.

The student must achieve a 3.00 GPA in courses used to satisfy the minor requirement.

Faculty: 

Cecile Ane, Professor 

Richard Chappell, Professor 

Peter Chien, Professor

Jessi Cisewski-Kehe, Assistant Professor

Nicolas Garcia Trillos, Assistant Professor

Hyunseung Kang, Assistant Professor

Sunduz Keles, Professor 

Bret Larget, Professor

Keith Levin, Assistant Professor

Po-Ling Loh, Associate Professor

Wei-Yin Loh, Professor 

Michael Newton, Professor 

Vivak Patel, Assistant Professor

Sebastian Raschka, Assistant Professor

Garvesh Raskutti, Associate Professor

Karl Rohe, Associate Professor

Kris Sankaran, Assistant Professor

Jun Shao, Professor 

Miaoyan Wang, Assistant Professor

Yahzen Wang, Professor

Brian Yandell, Professor 

Anru Zhang, Assistant Professor

Chunming Zhang, Professor 

Zhengjun Zhang, Professor 

Jun Zhu (chair), Professor