Department of Statistics
Daniel L. Gillen, Department Chair
2038 Donald Bren Hall
9498249862
Fax: 9498249863
http://www.stat.uci.edu/
Overview
Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting, and presenting empirical data. Statistical principles and methods are important for addressing questions in public policy, medicine, industry, and virtually every branch of science. Interest in statistical methods has increased dramatically with the abundance of large databases in fields like computer science (Internet and Web traffic), business and marketing (transaction records), and biology (the human genome and related data). It is the substantive questions in such areas of application that drive the development of new statistical methods and motivate the mathematical study of the properties of these methods.
Undergraduate Major in Data Science
The Data Science Major prepares students for a career in data analysis, combining foundational statistical concepts with computational principles from computer science. In the first two years of the program students will take core courses in both the Statistics and Computer Science Departments, providing a strong foundation in the principles of each field. In the 3rd and 4th years of the program, students will take more specialized courses, on topics such as design of algorithms, machine learning, information visualization, and Bayesian statistics. A major component of this degree is the final year capstone project course, a 2quarter course that teaches students how to apply statistical and computational principles to solve largescale realworld data analysis problems.
Admissions
Freshman Applicants: See the Undergraduate Admissions section.
Transfer Applicants: Juniorlevel applicants who satisfactorily complete course requirements will be given preference for admission. Applicants must satisfy the following requirements:
1. Completion of one year of college level mathematics (calculus or discrete math) and one semester of college level statistics.
2. Completion of one year of transferable Computer Science courses*; at least one of these should involve concepts such as those found in the Python and C++ programming languages, or another highlevel programming language.
*NOTE: Additional Computer Science and Statistics courses beyond those above are strongly recommended, particularly those that align with the major(s) of interest. Python, C++ and R are used extensively in the curriculum; therefore, transfer students should plan to learn these by studying on their own or by completing Python, C++, and Rrelated programming courses prior to their first quarter at UCI. Additional courses beyond those required for admission must be taken to fulfill the lowerdivision degree requirements, as many are prerequisites for upperdivision courses. For some transfer students, this may mean that it will take longer than two years to complete their degree.
Major and Minor Restrictions
Bren School of ICS majors (including shared majors, BIM and CSE) pursuing minors within the Bren School of ICS may not count more than five courses toward both the major and minor. Some ICS majors and minors outside of the School are not permitted due to significant overlap. Visit the ICS Student Affairs Office website for Majors and Minors restrictions. All students should check the Double Major Restrictions Chart and view our information page on double majoring to see what degree programs are eligible for double majoring.
Requirements for the B.S. in Data Science
All students must meet the University Requirements.
Data Science Major Requirements
Lowerdivision:  
A. Select one of the following series:  
Introduction to Programming and Programming with Software Libraries and Intermediate Programming 

or


Python Programming and Libraries (Accelerated) and Intermediate Programming 

B. Complete:  
I&C SCI 45C  Programming in C/C++ as a Second Language 
I&C SCI 46  Data Structure Implementation and Analysis 
I&C SCI 51  Introductory Computer Organization 
IN4MATX 43  Introduction to Software Engineering 
C. Complete:  
MATH 2A  SingleVariable Calculus 
MATH 2B  SingleVariable Calculus 
MATH 2D  Multivariable Calculus 
MATH 3A  Introduction to Linear Algebra 
or I&C SCI 6N  Computational Linear Algebra 
I&C SCI 6B  Boolean Logic and Discrete Structures 
I&C SCI 6D  Discrete Mathematics for Computer Science 
STATS 5  Seminar in Data Science 
STATS 7  Basic Statistics 
STATS 68  Statistical Computing and Exploratory Data Analysis 
Upperdivision:  
A. Data Science core requirements:  
STATS 110  Statistical Methods for Data Analysis I 
STATS 111  Statistical Methods for Data Analysis II 
STATS 112  Statistical Methods for Data Analysis III 
STATS 115  Introduction to Bayesian Data Analysis 
STATS 120A  Introduction to Probability and Statistics I 
STATS 120B  Introduction to Probability and Statistics II 
STATS 120C  Introduction to Probability and Statistics III 
I&C SCI 139W  Critical Writing on Information Technology 
COMPSCI 122A  Introduction to Data Management 
COMPSCI 161  Design and Analysis of Algorithms 
COMPSCI 178  Machine Learning and DataMining 
IN4MATX 143  Information Visualization 
B. Three elective courses from the list below:  
Probability and Stochastic Processes  
Probability and Stochastic Processes  
Multivariate Statistical Methods  
Principles in System Design  
Digital Image Processing  
Computer Simulation  
Information Retrieval  
Project in Databases and Web Applications  
Principles of Data Management  
Next Generation Search Systems  
Parallel and Distributed Computing  
Computer and Network Security  
Graph Algorithms  
Project In Algorithms And Data Structures  
Introduction to Optimization  
Introduction to Artificial Intelligence  
Neural Networks and Deep Learning  
Human Computer Interaction  
Information Retrieval  
Social Analysis of Computing  
C. Data Science capstone teambased project courses: STATS 170A and STATS 170B 
Sample Program of Study — Data Science
Freshman  

Fall  Winter  Spring 
I&C SCI 31  I&C SCI 32  I&C SCI 33 
MATH 2A  MATH 2B  MATH 2D 
WRITING 39A  STATS 5  STATS 7 
WRITING 39B  WRITING 39C  
Sophomore  
Fall  Winter  Spring 
I&C SCI 6B  I&C SCI 45C  I&C SCI 46 
STATS 120A  I&C SCI 51  I&C SCI 6D 
MATH 3A  STATS 120B  STATS 68 
General Education III  STATS 120C  
Junior  
Fall  Winter  Spring 
STATS 110  STATS 111  STATS 112 
IN4MATX 43  COMPSCI 178  COMPSCI 122A 
COMPSCI 161  I&C SCI 139W  IN4MATX 143 
General Education IV/VIII  General Education III/VII  General Education VI 
Senior  
Fall  Winter  Spring 
STATS 115  STATS 170A  STATS 170B 
General Education III  General Educaiton IV  Data Science Major Elective 
General Education IV  Data Science Major Elective  Data Science Major Elective 
Additional Information
Career Opportunities
A wide variety of careers and graduate programs are open to graduates of the Data Science major. Demand for graduates with skills in both statistics and computer science currently outpaces supply  thus, students with these skills typically find employment quickly, across a wide variety of sectors, including internet companies, finance, engineering, business, medicine, and more. Data Science graduates are wellqualified for job titles such as “data scientist,” “data analyst,” or “statistician,” both in the public and private sectors. Graduate school in area such as Computer Science or Statistics is also a possible career path.
Undergraduate Program in Statistics
The Department of Statistics offers lowerdivision undergraduate courses designed to introduce students to the field of statistics (STATS 7, STATS 8, STATS 67) and upperdivision undergraduate courses on the theoretical foundations of probability and statistics (STATS 120ASTATS 120BSTATS 120C) and statistical methodology (STATS 110STATS 111STATS 112). The Department is in the process of planning an undergraduate degree program in Statistics. In the interim, students interested in focusing on statistics are encouraged to consider a minor in Statistics along with a major in a field of interest.
Minor in Statistics
The minor in Statistics is designed to provide students with exposure to both statistical theory and practice. The minor requires a total of seven courses. These include a mathematics course, five core statistics courses, and an elective that may be taken from among several departments. Some of the courses used to complete the minor may include prerequisites that may or may not be part of a student’s course requirements for their major. Because of this, the minor is somewhat intensive, but it is a useful complement to a variety of undergraduate fields for mathematically inclined students. The minor, supplemented with a few additional courses (mathematics and computing), would provide sufficient background for graduate study in statistics. Students considering a minor in Statistics should meet with the academic counselor of their major as early as possible to plan their course work and incorporate the required courses into their fouryear academic plan.
NOTE: Students may not receive both a minor in Statistics and a specialization in Statistics within the Mathematics major.
Requirements for the Minor
Required Courses  
MATH 3A  Introduction to Linear Algebra 
or I&C SCI 6N  Computational Linear Algebra 
STATS 110 111  Statistical Methods for Data Analysis I and Statistical Methods for Data Analysis II 
STATS 120A 120B 120C  Introduction to Probability and Statistics I and Introduction to Probability and Statistics II and Introduction to Probability and Statistics III 
Select one elective from the following: ^{1}  
Introduction to Programming  
Probability and Stochastic Processes  
or MATH 130C 
Probability and Stochastic Processes 
Basic Statistics (or equivalent course) ^{2}  
Statistical Methods for Data Analysis III  
Introduction to Bayesian Data Analysis  
Multivariate Statistical Methods 
^{1}  Or can substitute another course with approval of the Director of Undergraduate Studies. 
^{2}  Only if taken prior to STATS 110 
NOTE: A maximum of two courses can be taken Pass/Not Pass toward a minor. Visit the ICS Student Affairs Office website for Majors and Minors restrictions.
Graduate Programs in Statistics
Research in statistics can range from mathematical studies of the theoretical underpinnings of a statistical model or method to the development of novel statistical models and methods and a thorough study of their properties. Frequently, statistics research is motivated and informed by collaborations with experts in a particular substantive field. Their scientific studies and data collection efforts may yield complex data that cannot be adequately handled using standard statistical methodology. Statisticians aim to develop methods that address the scientific or policy questions of the researcher. In doing so, statisticians must consider how efficiently and effectively the proposed methodology can be implemented and what guarantees can be provided as to the performance of the proposed methods. Such questions can often be answered using a combination of mathematical, analytical, and computational techniques.
Background: Individuals from a variety of backgrounds can make significant contributions to the field of statistics as long as they have sufficient background in statistics, mathematics, and computing. Undergraduate preparation in statistics, mathematics, and computing should include multivariate calculus (the equivalent of UCI courses MATH 2AMATH 2B, MATH 2DMATH 2E), linear algebra (MATH 121A), elementary analysis (MATH 140AMATH 140B), introductory probability and statistics (STATS 120ASTATS 120BSTATS 120C), and basic computing (I&C SCI 31). For students with undergraduate majors outside of mathematics and statistics, it is possible to make up one or two missing courses during the first year in the program.
Students may be admitted to either the master’s program or the doctoral program. For additional information about the Bren School of ICS's graduate programs and admissions information, click here.
Master of Science in Statistics
Course Requirements
A. Complete:  
STATS 200A 200B 200C  Intermediate Probability and Statistical Theory and Intermediate Probability and Statistical Theory and Intermediate Probability and Statistical Theory 
STATS 210  Statistical Methods I: Linear Models 
STATS 210B  Statistical Methods II: Categorical Data ^{1} 
STATS 210C  Statistical Methods III: Longitudinal Data ^{1} 
STATS 205  Introduction to Bayesian Data Analysis 
B. Complete three quarters of seminar in STATS 280.  
C. Select five additional graduate courses in or related to statistics, at least two of which are offered by the Department of Statistics. ^{2} 
^{1}  STATS 211 and STATS 212 may be substituted for STATS 210B and STATS 210C. 
^{2}  At most one of the five elective courses may be an Individual Study (STATS 299), and only with prior approval of the Department Graduate Committee. STATS 281ASTATS 281BSTATS 281C may not be taken as an elective. 
The entire program of courses must be approved by the Statistics Department Graduate Committee. Students with previous graduate training in statistics may petition the Committee to substitute other courses for a subset of the required courses. Students are required to pass a written comprehensive examination ordinarily at the end of the first year, covering the material from STATS 200ASTATS 200BSTATS 200C, and either STATS 210, STATS 210B, and STATS 210C, or STATS 210, STATS 211, and STATS 212.
Doctor of Philosophy in Statistics
Statistics Course Requirements
A. Complete:  
STATS 200A 200B 200C  Intermediate Probability and Statistical Theory and Intermediate Probability and Statistical Theory and Intermediate Probability and Statistical Theory 
STATS 210  Statistical Methods I: Linear Models 
STATS 211  Statistical Methods II: Generalized Linear Models 
STATS 212  Statistical Methods III: Methods for Correlated Data 
STATS 220A 220B  Advanced Probability and Statistics Topics and Advanced Probability and Statistics Topics 
STATS 225  Bayesian Statistical Analysis 
STATS 230  Statistical Computing Methods 
STATS 275  Statistical Consulting 
B. Select four additional graduate courses in or related to statistics, at least two of which are offered by the Department of Statistics. ^{1}  
C. In addition, continual enrollment in STATS 280 is required in all quarters. 
^{1}  STATS 202, STATS 203, and STATS 281ASTATS 281BSTATS 281C may not be taken as electives. These courses must be completed prior to candidacy. 
Additional Ph.D. requirements
Each Ph.D. student is required to take a written comprehensive examination, ordinarily at the end of the first year, covering the material from STATS 200ASTATS 200BSTATS 200C, STATS 210, STATS 211, and STATS 212. In addition, each student is required to take a written comprehensive examination after completion of the second year course work, covering material from STATS 220ASTATS 220B.
Ph.D. students who have passed the written comprehensive examinations are required to give a postcomprehensive research presentation each year.
Ph.D. students are required to serve as teaching assistants for at least two quarters.
Ph.D. students are required to demonstrate substantive knowledge of an application area outside of statistics (e.g., computer science, economics, cognitive sciences, biology, or medicine). Such knowledge can be demonstrated by course work in the application area (three quarter courses), coauthorship of publishable research in the application area, or other evidence of supervised collaborative work that is substantiated by an expert in the field. In the case of a theoretically oriented student, the outside application area may be mathematics.
The normative time for advancement to candidacy is three years. The normative time for completion of the Ph.D. is five years, and the maximum time permitted is seven years.
Courses
STATS 5. Seminar in Data Science. 1 Unit.
An introduction to the field of Data Science; intended for entering freshman and transfers.
Grading Option: Pass/no pass only.
Restriction: Information Computer Science Majors only.
STATS 7. Basic Statistics. 4 Units.
Introduces basic inferential statistics including confidence intervals and hypothesis testing on means and proportions, tdistribution, Chi Square, regression and correlation. Fdistribution and nonparametric statistics included if time permits.
Overlaps with STATS 8, MGMT 7, SOCECOL 13.
Restriction: STATS 7 may not be taken for credit concurrently with or after STATS 110, STATS 111, STATS 112.
(Va)
STATS 8. Introduction to Biological Statistics . 4 Units.
Introductory statistical techniques used to collect and analyze experimental and observational data from health sciences and biology. Includes exploration of data, probability and sampling distributions, basic statistical inference for means and proportions, linear regression, and analysis of variance.
Overlaps with SOCECOL 13, MGMT 7, STATS 7.
Restriction: STATS 8 may not be taken for credit concurrently with or after STATS 110, STATS 111, STATS 112.
(Va)
STATS 67. Introduction to Probability and Statistics for Computer Science. 4 Units.
Introduction to the basic concepts of probability and statistics with discussion of applications to computer science.
Prerequisite: MATH 2B
Restriction: School of Info & Computer Sci students have first consideration for enrollment. Computer Science Engineering Majors have first consideration for enrollment. STATS 67 may not be taken for credit concurrently with or after STATS 120B.
(Va)
STATS 68. Statistical Computing and Exploratory Data Analysis. 4 Units.
Introduces key concepts in statistical computing. Techniques such as exploratory data analysis, data visualization, simulation, and optimization methods, will be presented in the context of data analysis within a statistical computing environment.
Prerequisite: STATS 7 and I&C SCI 31
STATS 110. Statistical Methods for Data Analysis I. 4 Units.
Introduction to statistical methods for analyzing data from experiments and surveys. Methods covered include twosample procedures, analysis of variance, simple and multiple linear regression.
Prerequisite: STATS 7 or STATS 8 or AP90 or (STATS 120A and STATS 120B and STATS 120C). AP90 with a minimum score of 3
Restriction: School of Info & Computer Sci students only.
STATS 111. Statistical Methods for Data Analysis II. 4 Units.
STATS 112. Statistical Methods for Data Analysis III. 4 Units.
Introduction to statistical methods for analyzing longitudinal data from experiments and cohort studies. Topics covered include survival methods for censored timetoevent data, linear mixed models, nonlinear mixed effects models, and generalized estimating equations.
Prerequisite: STATS 111
Concurrent with STATS 203.
STATS 115. Introduction to Bayesian Data Analysis. 4 Units.
Basic Bayesian concepts and methods with emphasis on data analysis. Special emphasis on specification of prior distributions. Development for onetwo samples and on to binary, Poisson, and linear regression. Analyses performed using free OpenBugs software.
Prerequisite: STATS 120C. Recommended: STATS 110.
Concurrent with STATS 205.
STATS 120A. Introduction to Probability and Statistics I. 4 Units.
Introduction to basic principles of probability and statistical inference. Axiomatic definition of probability, random variables, probability distributions, expectation.
Prerequisite: MATH 2A and MATH 2B and (MATH 2D or MATH 4)
Overlaps with MATH 130A.
Restriction: Data Science Majors have first consideration for enrollment. Quantitative Economics majors have second consideration.
Concurrent with STATS 281A.
STATS 120B. Introduction to Probability and Statistics II. 4 Units.
Introduction to basic principles of probability and statistical inference. Point estimation, interval estimating, and testing hypotheses, Bayesian approaches to inference.
Prerequisite: STATS 120A
Restriction: Data Science Majors have first consideration for enrollment. Quantitative Economics majors have second consideration.
Concurrent with STATS 281B.
STATS 120C. Introduction to Probability and Statistics III. 4 Units.
Introduction to basic principles of probability and statistical inference. Linear regression, analysis or variance, model checking.
Prerequisite: STATS 120B and (MATH 3A or MATH 6G or I&C SCI 6N)
Restriction: Data Science Majors have first consideration for enrollment. Quantitative Economics majors have second consideration.
Concurrent with STATS 281C.
STATS 140. Multivariate Statistical Methods. 4 Units.
Theory and application of multivariate statistical methods. Topics include statistical inference for the multivariate normal model and its extensions to multiple samples and regression, use of statistical packages for data visualization and reduction, discriminant analysis, cluster analysis, and factor analysis.
Prerequisite: STATS 120C and (MATH 3A or I&C SCI 6N)
Concurrent with STATS 240.
STATS 170A. Project in Data Science I. 4 Units.
Problem definition and analysis, data representation, algorithm selection, solution validation, and results presentation. Students do team projects and lectures cover analysis alternatives, project planning, and data analysis issues. First quarter emphasizes approach selection, project planning, and experimental design.
Prerequisite: STATS 68 and STATS 112 and IN4MATX 43 and COMPSCI 122A and COMPSCI 161 and COMPSCI 178
Grading Option: In Progress (Letter Grade with P/NP).
Restriction: Seniors only. Data Science Majors have first consideration for enrollment.
STATS 170B. Project in Data Science II. 4 Units.
Problem definition and analysis, data representation, algorithm selection, solution validation, and results presentation. Students do team projects and lectures cover analysis alternatives, project planning, and data analysis issues. Second quarter emphasizes project execution and analysis, and presentation of results.
Prerequisite: STATS 170A
Restriction: Seniors only. Data Science Majors have first consideration for enrollment.
STATS 199. Individual Study. 25 Units.
Individual research or investigations under the direction of an individual faculty member.
Repeatability: May be repeated for credit unlimited times.
STATS 200A. Intermediate Probability and Statistical Theory. 4 Units.
Basics of probability theory, random variables and basic transformations, univariate distributions—discrete and continuous, multivariate distributions.
Prerequisite: STATS 120C
STATS 200B. Intermediate Probability and Statistical Theory. 4 Units.
Random samples, transformations, limit laws, normal distribution theory, introduction to stochastic processes, data reduction, point estimation (maximum likelihood).
Prerequisite: STATS 200A
STATS 200C. Intermediate Probability and Statistical Theory. 4 Units.
Interval estimation, hypothesis testing, decision theory and Bayesian inference, basic linear model theory.
Prerequisite: STATS 200B
STATS 201. Statistical Methods for Data Analysis I. 4 Units.
STATS 202. Statistical Methods for Data Analysis II. 4 Units.
Introduction to statistical methods for analyzing data from surveys or experiments. Emphasizes application and understanding of methods for categorical data including contingency tables, logistic and Poisson regression, loglinear models.
Prerequisite: STATS 201 or STATS 210
Concurrent with STATS 111.
STATS 203. Statistical Methods for Data Analysis III. 4 Units.
Introduction to statistical methods for analyzing longitudinal data from experiments and cohort studies. Topics covered include survival methods for censored timetoevent data, linear mixed models, nonlinear mixed effects models, and generalized estimating equations.
Prerequisite: STATS 202
Concurrent with STATS 112.
STATS 205. Introduction to Bayesian Data Analysis. 4 Units.
Basic Bayesian concepts and methods with emphasis on data analysis. Special emphasis on specification of prior distributions. Development for onetwo samples and on to binary, Poisson and linear regression. Analyses performed using free OpenBugs software.
Prerequisite: STATS 120C. Recommended: STATS 201 or STATS 210.
Concurrent with STATS 115.
STATS 210. Statistical Methods I: Linear Models. 4 Units.
Statistical methods for analyzing data from surveys and experiments. Topics include randomization and modelbased inference, twosample methods, analysis of variance, linear regression and model diagnostics.
Prerequisite: Knowledge of basic statistics, calculus, linear algebra.
STATS 210A. Statistical Methods I: Linear Models. 4 Units.
Statistical methods for analyzing data from surveys and experiments. Topics include randomization and modelbased inference, twosample methods, analysis of variance, linear regression, and model diagnostics.
Prerequisite: Knowledge of basic statistics (at the level of STATS 7), calculus, and linear algebra.
Restriction: Graduate students only.
STATS 210B. Statistical Methods II: Categorical Data. 4 Units.
Introduction to statistical methods for analyzing discrete and nonnormal outcomes. Emphasizes the development and application of methods for categorical data, including contingency tables, logistic and Poisson regression, loglinear models.
Prerequisite: STATS 210. May not be taken for graduate credit by Ph.D. students in Statistics.
Restriction: Graduate students only.
STATS 210C. Statistical Methods III: Longitudinal Data. 4 Units.
Introduction to statistical methods for analyzing longitudinal outcomes. Emphasizes the development and application of regression methods for correlated and censored outcomes. Methods for continuous and discrete correlated outcomes, as well as censored outcomes, are covered.
Prerequisite: STATS 210B. May not be taken for graduate credit by Ph.D. students in Statistics.
Restriction: Graduate students only.
STATS 211. Statistical Methods II: Generalized Linear Models. 4 Units.
Development of the theory and application of generalized linear models. Topics include likelihood estimation and asymptotic distributional theory for exponential families, quasilikelihood and mixed model development. Emphasizes methodological development and application to real scientific problems.
Prerequisite or corequisite: STATS 210
STATS 212. Statistical Methods III: Methods for Correlated Data. 4 Units.
Development and application of statistical methods for analyzing corrected data. Topics covered include repeated measures ANOVA, linear mixed models, nonlinear mixed effects models, and generalized estimating equations. Emphasizes both theoretical development and application of the presented methodology.
Prerequisite: STATS 211
STATS 220A. Advanced Probability and Statistics Topics. 4 Units.
Advanced topics in probability and statistical inference including measure theoretic probability, large sample theory, decision theory, resampling and Monte Carlo methods, nonparametric methods.
Prerequisite: STATS 200C
STATS 220B. Advanced Probability and Statistics Topics. 4 Units.
Advanced topics in probability and statistical inference, including measure theoretic probability, large sample theory, decision theory, resampling and Monte Carlo methods, nonparametric methods.
Prerequisite: STATS 220A and MATH 140B
STATS 225. Bayesian Statistical Analysis. 4 Units.
STATS 226. Advanced Topics in Modern Bayesian Statistical Inference. 4 Units.
Modern Bayesian Statistics: selected topics from theory of Markov chains, application of theory to modern methods of Markov chain Monte Carlo sampling; Bayesian nonparametric and semiparametric modeling, including Dirichlet Process Mixtures; Mixtures of Polya Trees.
Prerequisite: STATS 200C and STATS 225
STATS 230. Statistical Computing Methods. 4 Units.
Numerical computations and algorithms with applications in statistics. Topics include optimization methods including the EM algorithm, random number generation and simulation, Markov chain simulation tools, and numerical integration.
Prerequisite: Two quarters of upperdivision or graduate training in probability and statistics.
Overlaps with COMPSCI 206.
STATS 235. Modern Data Analysis Methods . 4 Units.
Introduces selected modern tools for data analysis. Emphasizes use of computational and resampling techniques for data analyses when the data do not conform to standard toolbox of regression models and/or complexity of modeling problem threatens validity of standard methods.
Prerequisite: STATS 120C and STATS 205 and (STATS 201 or STATS 210)
Restriction: Graduate students only.
STATS 240. Multivariate Statistical Methods. 4 Units.
Theory and application of multivariate statistical methods. Topics include statistical inference for the multivariate normal model and its extensions to multiple samples and regression, use of statistical packages for data visualization and reduction, discriminant analysis, cluster analysis, and factor analysis.
Prerequisite: STATS 120C and (MATH 3A or I&C SCI 6N)
Concurrent with STATS 140.
STATS 245. Time Series Analysis. 4 Units.
Statistical models for analysis of time series from time and frequency domain perspectives. Emphasizes theory and application of time series data analysis methods. Topics include ARMA/ARIMA models, model identification/estimation, linear operators, Fourier analysis, spectral estimation, state space models, Kalman filter.
Corequisite: STATS 200C
Prerequisite or corequisite: STATS 201 or STATS 210
STATS 246. Spectral Analysis . 4 Units.
Spectral methods that are most commonly utilized for analyzing univariate and multivariate time series and signals. These methods include spectral and coherence estimation, transfer function modeling, classification and discrimination of time series, nonstationary time series, timefrequency analysis, and wavelets analysis.
Prerequisite: STATS 200B and (STATS 201 or STATS 210)
STATS 250. Biostatistics. 4 Units.
Statistical methods commonly used to analyze data arising from clinical studies. Topics include analysis of observational studies and randomized clinical trials, techniques in the analysis of survival and longitudinal data, approaches to handling missing data, metaanalysis, nonparametric methods.
Prerequisite: STATS 210
STATS 255. Statistical Methods for Survival Data. 4 Units.
Statistical methods for analyzing survival data from cohort studies. Topics include parametric and nonparametric methods, the KaplanMeier estimator, logrank tests, regression models, the Cox proportional hazards model and accelerated failure time models, efficient sampling designs, discrete survival models.
Corequisite: STATS 202 or STATS 211.
Prerequisite: STATS 210
STATS 257. Introduction to Statistical Genetics. 4 Units.
Provides students with knowledge of the basic principles, concepts, and methods used in statistical genetic research. Topics include principles of population genetics, and statistical methods for family and populationbased studies.
Prerequisite: Two quarters of upperdivision or graduate training in statistical methods.
Same as EPIDEM 215.
STATS 260. Inference with Missing Data. 4 Units.
Statistical methods and theory useful for analysis of multivariate data with partially observed variables. Bayesian and likelihoodbased methods developed. Topics include EMtype algorithms, MCMC samplers, multiple imputation, and general location model. Applications from economics, education, and medicine are discussed.
Prerequisite or corequisite: STATS 210 or STATS 200C. STATS 230.
STATS 262. Theory and Practice of Sample Surveys. 4 Units.
Basic techniques and statistical methods used in designing surveys and analyzing collected survey data. Topics include simple random sampling, ratio and regression estimates, stratified sampling, cluster sampling, sampling with unequal probabilities, multistage sampling, and methods to handle nonresponse.
Prerequisite: STATS 120C
STATS 265. Causal Inference. 4 Units.
Various approaches to causal inference focusing on the Rubin causal model and propensityscore methods. Topics include randomized experiments, observational studies, noncompliance, ignorable and nonignorable treatment assignment, instrumental variables, and sensitivity analysis. Applications from economics, politics, education, and medicine.
Prerequisite: STATS 200C and STATS 210
STATS 270. Stochastic Processes. 4 Units.
Introduction to the theory and application of stochastic processes. Topics include Markov chains, continuoustime Markov processes, Poisson processes, and Brownian motion. Applications include Markov chain Monte Carlo methods and financial modeling (for example, option pricing).
Prerequisite: STATS 120C
STATS 275. Statistical Consulting. 4 Units.
Training in collaborative research and practical application of statistics. Emphasis on effective communication as it relates to identifying scientific objectives, formulating a statistical analysis plan, choice of statistical methods, and interpretation of results and their limitations to nonstatisticians.
Prerequisite: STATS 203 or STATS 212
Repeatability: May be taken for credit 2 times.
STATS 280. Seminar in Statistics. 0.5 Units.
Periodic seminar series covering topics of current research in statistics and its application.
Grading Option: Satisfactory/unsatisfactory only.
Repeatability: May be repeated for credit unlimited times.
Restriction: Graduate students only.
STATS 281A. Introduction to Probability and Statistics I. 4 Units.
Introduction to basic principles of probability and statistical inference. Axiomatic definition of probability, random variables, probability distributions, expectation.
Restriction: Graduate students only.
Concurrent with STATS 120A.
STATS 281B. Introduction to Probability and Statistics II. 4 Units.
Introduction to basic principles of probability and statistical inference. Point estimation, interval estimating, and testing hypotheses, Bayesian approaches to inference.
Restriction: Graduate students only.
Concurrent with STATS 120B.
STATS 281C. Introduction to Probability and Statistics III. 4 Units.
Introduction to basic principles of probability and statistical inference. Contingency table analysis, linear regression, analysis of variance, model checking.
Restriction: Graduate students only.
Concurrent with STATS 120C.
STATS 295. Special Topics in Statistics. 4 Units.
Studies in selected areas of statistics. Topics addressed vary each quarter.
Repeatability: Unlimited as topics vary.
STATS 298. Thesis Supervision. 212 Units.
Individual research or investigation conducted in preparation for the M.S. thesis option or the dissertation requirements for the Ph.D. program.
Repeatability: May be repeated for credit unlimited times.
STATS 299. Individual Study. 212 Units.
Individual research or investigation under the direction of an individual faculty member.
Repeatability: May be repeated for credit unlimited times.