(College of Letters & Science)

Alexander Aue, Ph.D., Chairperson of the Department

**Department Office.** 4118 Mathematical Sciences Building; https://statistics.ucdavis.edu

(College of Letters & Science)

Alexander Aue, Ph.D., Chairperson of the Department

**Department Office.** 4118 Mathematical Sciences Building; https://statistics.ucdavis.edu

(College of Letters & Science)

Alexander Aue, Ph.D., Chairperson of the Department

**Department Office.** 4118 Mathematical Sciences Building; https://statistics.ucdavis.edu

**The Major Program**

Statistics enables us to make inferences about entire populations, based on samples extracted from those populations. Statistical methods can be applied to problems from almost every discipline and they are vitally important to researchers in agricultural, biological, environmental, social, engineering, and medical sciences.

**The Program. **Statistics majors may receive either a Bachelor of Arts (A.B.) or a Bachelor of Science (B.S.) degree. Both the A.B. and the B.S. programs require theoretical and applied course work and underscore the strong interdependence of statistical theory and the applications and computational aspects of statistics. The B.S. degree program has five tracks: Applied Statistics Track, Computational Statistics Track, General Track, Machine Learning Track, and the Statistical Data Science Track. The A.B. degree program has one track.

**A.B. in Statistics-Applied Statistics Track **emphasizes statistical applications. This track is recommended for students who are interested in applications of statistical techniques to various disciplines, especially the social sciences.

**Major Advisors. **For a current list of faculty and staff advisors, see https://statistics.ucdavis.edu/undergrad/advising.

Students are encouraged to meet with an advisor to plan a program as early as possible.

**Career Alternatives.** Probability models, statistical methods, and computational techniques are used in a great many fields, including the biological, physical, social, and health sciences, business, and engineering. The wide applicability of statistics is reflected in the strong demand for graduates with statistical training in both the public and private sectors. Employment opportunities include careers in data & policy analysis in government & industry, financial management, quality control, insurance & healthcare industry, actuarial science, engineering, public health, biological and pharmaceutical research, law, and education. Students with an undergraduate degree in statistics have entered advanced studies in statistics, economics, finance, psychology, medicine, business management & analytics, and other professional school programs.

Preparatory Subject Matter

Units: 20-23

Mathematics

12-15

Choose a series:

9-12

MAT 016A

Short Calculus (Active)

3

MAT 016B

Short Calculus (Active)

3

MAT 016C

Short Calculus (Active)

3

MAT 017A

Calculus for Biology & Medicine (Active)

4

MAT 017B

Calculus for Biology & Medicine (Active)

4

MAT 017C

Calculus for Biology & Medicine (Active)

4

MAT 021A

Calculus (Active)

4

MAT 021B

Calculus (Active)

4

MAT 021C

Calculus (Active)

4

MAT 022A

Linear Algebra (Active)

3

Computer Science Engineering; choose one:

4

ECS 032A

Introduction to Programming (Active)

4

ECS 036A

Programming & Problem Solving (Active)

4

Statistics; choose one:

4

STA 013

Elementary Statistics (Active)

4

STA 032

Gateway to Statistical Data Science (Active)

4

STA 100

Applied Statistics for Biological Sciences (Active)

4

Depth Subject Matter

Units: 45-48

STA 106

Applied Statistical Methods: Analysis of Variance (Active)

4

STA 108

Applied Statistical Methods: Regression Analysis (Active)

4

STA 130A

Mathematical Statistics: Brief Course (Active)

4

STA 130B

Mathematical Statistics: Brief Course (Active)

4

STA 138

Analysis of Categorical Data (Active)

4

Choose one:

4

STA 137

Applied Time Series Analysis (Active)

4

STA 141A

Fundamentals of Statistical Data Science (Active)

4

Choose three:

12

STA 104

Applied Statistical Methods: Nonparametric Statistics (Active)

4

STA 135

Multivariate Data Analysis (Active)

4

STA 137

Applied Time Series Analysis (Active)

4

STA 141A

Fundamentals of Statistical Data Science (Active)

4

Choose one:

4

STA 141B

Data & Web Technologies for Data Analysis (Active)

4

STA 141C

Big Data & High Performance Statistical Computing (Active)

4

STA 144

Sampling Theory of Surveys (Active)

4

STA 145

Bayesian Statistical Inference (Active)

4

STA 160

Practice in Statistical Data Science (Active)

4

MAT 168

Optimization (Active)

4

Choose one approved 4 unit course:

4

STA 194HA

Special Studies for Honors Students (Active)

4

STA 194HB

Special Studies for Honors Students (Active)

4

STA 199

Special Study for Advanced Undergraduates (Active)

1-5

Three upper division elective courses outside of Statistics.

9-12

Electives are chosen with and must be approved by the major advisor. Electives should follow a coherent sequence in one single disciple where statistical methods and models are applied and should cover the quantitative aspects of the discipline. A list of pre-approved electives can be found on the Statistics Department website.

Total: 65-71

(College of Letters & Science)

Alexander Aue, Ph.D., Chairperson of the Department

**Department Office.** 4118 Mathematical Sciences Building; https://statistics.ucdavis.edu

**The Major Program**

Statistics enables us to make inferences about entire populations, based on samples extracted from those populations. Statistical methods can be applied to problems from almost every discipline and they are vitally important to researchers in agricultural, biological, environmental, social, engineering, and medical sciences.

**The Program.** Statistics majors may receive either a Bachelor of Arts or a Bachelor of Science degree. Both the A.B. and the B.S. programs require theoretical and applied course work and underscore the strong interdependence of statistical theory and the applications and computational aspects of statistics. The B.S. degree program has five tracks: Applied Statistics Track, Computational Statistics Track, General Track, Machine Learning Track, and the Statistical Data Science Track.

**B.S. in Statistics-Applied Statistics Track** emphasizes statistical applications. This track is recommended for students who are interested in applications of statistical techniques to various disciplines including the biological, physical and social sciences.

**B.S. in Statistic-Computational Statistics Track** emphasizes computing. This track is recommended for students interested in the computational and data management aspects of statistical analysis.

**B.S. in Statistics-General Track** emphasizes statistical theory and is especially recommended as preparation for graduate study in statistics.

**B.S in Statistics-Machine Learning Track** emphasizes algorithmic and theoretical aspects of statistical learning methodologies that are geared towards building predictive and explanatory models for large and complex data. It is recommended for students interested in pursuing graduate programs in statistics, machine learning, or data science, as well as for students interested in learning statistical techniques for industry.

**B.S. in Statistic-Statistical Data Science Track** emphasizes data handling skills and statistical computation. This track is recommended for students interested in statistical learning methodology, advanced data handling techniques and computational aspects of statistical analysis.

**Major Advisors.** For a current list of faculty and staff advisors, see https://statistics.ucdavis.edu/undergrad/advising.

Students are encouraged to meet with an advisor to plan a program as early as possible.

**Career Alternatives. **Probability models, statistical methods, and computational techniques are used in a great many fields, including the biological, physical, social, and health sciences, business, and engineering. The wide applicability of statistics is reflected in the strong demand for graduates with statistical training in both the public and private sectors. Employment opportunities include careers in data & policy analysis in government & industry, financial management, quality control, insurance & healthcare industry, actuarial science, engineering, public health, biological & pharmaceutical research, law, and education. Students with an undergraduate degree in statistics have entered advanced studies in statistics, economics, finance, psychology, medicine, business management & analytics, and other professional school programs.

Applied Statistics Track

Units: 75-83

Preparatory Subject Matter

27-31

Mathematics

12-15

Choose a series:

9-12

MAT 016A

Short Calculus (Active)

3

MAT 016B

Short Calculus (Active)

3

MAT 016C

Short Calculus (Active)

3

MAT 017A

Calculus for Biology & Medicine (Active)

4

MAT 017B

Calculus for Biology & Medicine (Active)

4

MAT 017C

Calculus for Biology & Medicine (Active)

4

MAT 021A

Calculus (Active)

4

MAT 021B

Calculus (Active)

4

MAT 021C

Calculus (Active)

4

MAT 022A

Linear Algebra (Active)

3

Computer Science Engineering; choose one:

4

ECS 032A

Introduction to Programming (Active)

4

ECS 036A

Programming & Problem Solving (Active)

4

Statistics; choose one:

4

STA 013

Elementary Statistics (Active)

4

STA 032

Gateway to Statistical Data Science (Active)

4

STA 100

Applied Statistics for Biological Sciences (Active)

4

Two introductory courses serving as the prerequisites to upper division courses in a chosen discipline to which statistics is applied.

7-8

Depth Subject Matter

48-52

STA 106

Applied Statistical Methods: Analysis of Variance (Active)

4

STA 108

Applied Statistical Methods: Regression Analysis (Active)

4

STA 130A

Mathematical Statistics: Brief Course (Active)

4

STA 130B

Mathematical Statistics: Brief Course (Active)

4

STA 138

Analysis of Categorical Data (Active)

4

STA 141A

Fundamentals of Statistical Data Science (Active)

4

Choose three:

12

STA 104

Applied Statistical Methods: Nonparametric Statistics (Active)

4

STA 135

Multivariate Data Analysis (Active)

4

STA 137

Applied Time Series Analysis (Active)

4

Choose one:

4

STA 141B

Data & Web Technologies for Data Analysis (Active)

4

STA 141C

Big Data & High Performance Statistical Computing (Active)

4

STA 144

Sampling Theory of Surveys (Active)

4

STA 145

Bayesian Statistical Inference (Active)

4

STA 160

Practice in Statistical Data Science (Active)

4

MAT 168

Optimization (Active)

4

Choose one approved 4 unit course:

4

STA 194HA

Special Studies for Honors Students (Active)

4

STA 194HB

Special Studies for Honors Students (Active)

4

STA 199

Special Study for Advanced Undergraduates (Active)

1-5

Four upper division elective courses outside of Statistics.

12-16

Electives are chosen with and must be approved by the major advisor. Electives should follow a coherent sequence in one single disciple where statistical methods and models are applied: at least three of them should cover the quantitative aspects of the discipline. A list of pre-approved electives can be found on the Statistics Department website.

Computational Statistics Track

Units: 79-80

Preparatory Subject Matter

27-28

Mathematics

19

MAT 021A

Calculus (Active)

4

MAT 021B

Calculus (Active)

4

MAT 021C

Calculus (Active)

4

MAT 021D

Vector Analysis (Active)

4

MAT 022A

Linear Algebra (Active)

3

Computer Science Engineering; choose one:

4-5

ECS 034

Software Development in UNIX & C++ (Active)

4

ECS 036C

Data Structures, Algorithms, & Programming (Active)

4

Or the equivalent.

Statistics; choose one:

4

STA 013

Elementary Statistics (Active)

4

STA 032

Gateway to Statistical Data Science (Active)

4

STA 100

Applied Statistics for Biological Sciences (Active)

4

Depth Subject Matter

52

Statistics

28

STA 106

Applied Statistical Methods: Analysis of Variance (Active)

4

STA 108

Applied Statistical Methods: Regression Analysis (Active)

4

STA 131A

Introduction to Probability Theory (Active)

4

STA 131B

Introduction to Mathematical Statistics (Active)

4

STA 141A

Fundamentals of Statistical Data Science (Active)

4

Choose two:

8

STA 104

Applied Statistical Methods: Nonparametric Statistics (Active)

4

STA 135

Multivariate Data Analysis (Active)

4

STA 137

Applied Time Series Analysis (Active)

4

STA 138

Analysis of Categorical Data (Active)

4

STA 142A

Statistical Learning I (Active)

4

STA 142B

Statistical Learning II (Active)

4

STA 144

Sampling Theory of Surveys (Active)

4

STA 145

Bayesian Statistical Inference (Active)

4

STA 160

Practice in Statistical Data Science (Active)

4

Choose one approved 4 unit course:

4

STA 194HA

Special Studies for Honors Students (Active)

4

STA 194HB

Special Studies for Honors Students (Active)

4

STA 199

Special Study for Advanced Undergraduates (Active)

1-5

Programming, Data Management & Data Technologies:

8

Choose one:

4

ECS 130

Scientific Computation (Active)

4

ECS 145

Scripting Languages & Their Applications (Active)

4

ECS 165A

Database Systems (Active)

4

Scientific Computational Algorithm & Visualization; choose two:

8

ECS 122A

Algorithm Design & Analysis (Active)

4

ECS 129

Computational Structural Bioinformatics (Active)

4

ECS 140A

Programming Languages (Active)

4

ECS 158

Programming on Parallel Architectures (Active)

4

ECS 163

Information Interfaces (Active)

4

STA 141B

Data & Web Technologies for Data Analysis (Active)

4

STA 141C

Big Data & High Performance Statistical Computing (Active)

4

Mathematics; choose two:

8

MAT 124

Mathematical Biology (Active)

4

MAT 128A

Numerical Analysis (Active)

4

MAT 128B

Numerical Analysis in Solution of Equations (Active)

4

MAT 129

Fourier Analysis (Active)

4

MAT 145

Combinatorics (Active)

4

MAT 148

Discrete Mathematics (Active)

4

MAT 160

Mathematics for Data Analytics & Decision Making (Active)

4

MAT 165

Mathematics & Computers (Active)

4

MAT 167

Applied Linear Algebra (Active)

4

MAT 168

Optimization (Active)

4

General Statistics Track

Units: 82-84

Preparatory Subject Matter

27-28

Mathematics

19-20

MAT 021A

Calculus (Active)

4

MAT 021B

Calculus (Active)

4

MAT 021C

Calculus (Active)

4

MAT 021D

Vector Analysis (Active)

4

Choose one:

3-4

MAT 022A

Linear Algebra (Active)

3

MAT 067

Modern Linear Algebra (Active)

4

Computer Science Engineering; choose one:

4

ECS 032A

Introduction to Programming (Active)

4

ECS 036A

Programming & Problem Solving (Active)

4

Statistics; choose one:

4

STA 013

Elementary Statistics (Active)

4

STA 032

Gateway to Statistical Data Science (Active)

4

STA 100

Applied Statistics for Biological Sciences (Active)

4

Depth Subject Matter

55-56

STA 106

Applied Statistical Methods: Analysis of Variance (Active)

4

STA 108

Applied Statistical Methods: Regression Analysis (Active)

4

STA 131A

Introduction to Probability Theory (Active)

4

STA 131B

Introduction to Mathematical Statistics (Active)

4

STA 131C

Introduction to Mathematical Statistics (Active)

4

STA 138

Analysis of Categorical Data (Active)

4

Choose three:

12

STA 104

Applied Statistical Methods: Nonparametric Statistics (Active)

4

STA 135

Multivariate Data Analysis (Active)

4

STA 137

Applied Time Series Analysis (Active)

4

STA 141A

Fundamentals of Statistical Data Science (Active)

4

Choose one:

4

STA 141B

Data & Web Technologies for Data Analysis (Active)

4

STA 141C

Big Data & High Performance Statistical Computing (Active)

4

STA 142A

Statistical Learning I (Active)

4

STA 142B

Statistical Learning II (Active)

4

STA 144

Sampling Theory of Surveys (Active)

4

STA 145

Bayesian Statistical Inference (Active)

4

STA 160

Practice in Statistical Data Science (Active)

4

MAT 168

Optimization (Active)

4

Choose one approved 4 unit course:

4

STA 194HA

Special Studies for Honors Students (Active)

4

STA 194HB

Special Studies for Honors Students (Active)

4

STA 199

Special Study for Advanced Undergraduates (Active)

1-5

Mathematics

16

MAT 127A

Real Analysis (Active)

4

MAT 127B

Real Analysis (Active)

4

Choose one:

4

MAT 108

Introduction to Abstract Mathematics (Active)

4

MAT 127C

Real Analysis (Active)

4

MAT 167

Applied Linear Algebra (Active)

4

Related Elective Courses

3-4

One upper division course approved by major advisor; it should be in mathematics, computer science or cover quantitative aspects of a substantive discipline.

Machine Learning Track

Units: 79

Preparatory Subject Matter

27

Mathematics

19

MAT 021A

Calculus (Active)

4

MAT 021B

Calculus (Active)

4

MAT 021C

Calculus (Active)

4

MAT 021D

Vector Analysis (Active)

4

MAT 022A

Linear Algebra (Active)

3

Computer Science Engineering; choose one:

4

ECS 032A

Introduction to Programming (Active)

4

ECS 036A

Programming & Problem Solving (Active)

4

Statistics; choose one:

4

STA 013

Elementary Statistics (Active)

4

STA 032

Gateway to Statistical Data Science (Active)

4

STA 100

Applied Statistics for Biological Sciences (Active)

4

Depth Subject Matter

52

Statistics

36

STA 106

Applied Statistical Methods: Analysis of Variance (Active)

4

STA 108

Applied Statistical Methods: Regression Analysis (Active)

4

STA 131A

Introduction to Probability Theory (Active)

4

STA 131B

Introduction to Mathematical Statistics (Active)

4

STA 131C

Introduction to Mathematical Statistics (Active)

4

STA 141A

Fundamentals of Statistical Data Science (Active)

4

STA 142B

Statistical Learning II (Active)

4

STA 142B

Statistical Learning II (Active)

4

Choose one:

4

STA 144

Sampling Theory of Surveys (Active)

4

STA 145

Bayesian Statistical Inference (Active)

4

Mathematics; choose one:

4

MAT 167

Applied Linear Algebra (Active)

4

MAT 168

Optimization (Active)

4

Choose three:

12

STA 104

Applied Statistical Methods: Nonparametric Statistics (Active)

4

STA 135

Multivariate Data Analysis (Active)

4

STA 137

Applied Time Series Analysis (Active)

4

STA 138

Analysis of Categorical Data (Active)

4

STA 141B

Data & Web Technologies for Data Analysis (Active)

4

STA 141C

Big Data & High Performance Statistical Computing (Active)

4

STA 144

Sampling Theory of Surveys (Active)

4

STA 145

Bayesian Statistical Inference (Active)

4

MAT 127A

Real Analysis (Active)

4

MAT 128A

Numerical Analysis (Active)

4

MAT 160

Mathematics for Data Analytics & Decision Making (Active)

4

ECS 122A

Algorithm Design & Analysis (Active)

4

ECS 158

Programming on Parallel Architectures (Active)

4

ECS 163

Information Interfaces (Active)

4

ECS 160

Software Engineering (Active)

4

ECS 170

Introduction to Artificial Intelligence (Active)

4

ECS 174

Computer Vision (Active)

4

Choose one approved 4 unit course:

4

STA 194HA

Special Studies for Honors Students (Active)

4

STA 194HB

Special Studies for Honors Students (Active)

4

STA 199

Special Study for Advanced Undergraduates (Active)

1-5

NOTE: A course used to fulfill the core requirement cannot be used as an elective.

Statistical Data Science Track

Units: 79

Preparatory Subject Matter

27

Mathematics

19

MAT 021A

Calculus (Active)

4

MAT 021B

Calculus (Active)

4

MAT 021C

Calculus (Active)

4

MAT 021D

Vector Analysis (Active)

4

MAT 022A

Linear Algebra (Active)

3

Computer Science Engineering; choose one:

4

ECS 032A

Introduction to Programming (Active)

4

ECS 036A

Programming & Problem Solving (Active)

4

Statistics; choose one:

4

STA 013

Elementary Statistics (Active)

4

STA 032

Gateway to Statistical Data Science (Active)

4

STA 100

Applied Statistics for Biological Sciences (Active)

4

Depth Subject Matter

52

Statistics

36

STA 106

Applied Statistical Methods: Analysis of Variance (Active)

4

STA 108

Applied Statistical Methods: Regression Analysis (Active)

4

Choose one:

4

STA 131A

Introduction to Probability Theory (Active)

4

STA 130A

Mathematical Statistics: Brief Course (Active)

4

Choose one:

4

STA 131B

Introduction to Mathematical Statistics (Active)

4

STA 130B

Mathematical Statistics: Brief Course (Active)

4

STA 135

Multivariate Data Analysis (Active)

4

STA 141A

Fundamentals of Statistical Data Science (Active)

4

STA 141B

Data & Web Technologies for Data Analysis (Active)

4

STA 141C

Big Data & High Performance Statistical Computing (Active)

4

STA 160

Practice in Statistical Data Science (Active)

4

Machine Learning; choose one:

4

ECS 171

Machine Learning (Active)

4

STA 142A

Statistical Learning I (Active)

4

Mathematics; choose one:

4

MAT 167

Applied Linear Algebra (Active)

4

MAT 168

Optimization (Active)

4

Choose two:

8

STA 104

Applied Statistical Methods: Nonparametric Statistics (Active)

4

STA 137

Applied Time Series Analysis (Active)

4

STA 138

Analysis of Categorical Data (Active)

4

STA 142A

Statistical Learning I (Active)

4

STA 142B

Statistical Learning II (Active)

4

STA 144

Sampling Theory of Surveys (Active)

4

STA 145

Bayesian Statistical Inference (Active)

4

MAT 128A

Numerical Analysis (Active)

4

MAT 160

Mathematics for Data Analytics & Decision Making (Active)

4

ECS 122A

Algorithm Design & Analysis (Active)

4

ECS 158

Programming on Parallel Architectures (Active)

4

ECS 163

Information Interfaces (Active)

4

ECS 165A

Database Systems (Active)

4

Choose one approved 4 unit course:

4

STA 194HA

Special Studies for Honors Students (Active)

4

STA 194HB

Special Studies for Honors Students (Active)

4

STA 199

Special Study for Advanced Undergraduates (Active)

1-5

NOTE: A course used to fulfill the core requirement cannot be used as an elective.

Total: 75-84

**The BS/MS Integrated Degree Program is no longer accepting students.**

Alexander Aue, Ph.D., Chairperson of the Program

Jie Peng, Ph.D., Vice Chairperson for Graduate Affairs

**Program Office. **4118 Mathematical Sciences Building; 530-752-2361; https://statistics.ucdavis.edu

**Faculty. **https://statistics.ucdavis.edu/people

The Department offers undergraduate majors a path into the Statistics M.S. program through the Integrated Degree Program (I.D.P.). This program is intended for students who seek to be employed as statisticians in government or industry. The minimum major GPA requirement is 3.200 at the end of the junior year, although students with demonstrated excellence in academic work (with a major GPA of 3.500 or above) are most likely to be admitted. Before moving into the graduate phase, I.D.P. students must satisfy all requirements of the B.S. degree.

To apply for the I.D.P., undergraduate students must submit the Statistics I.D.P. form along with supporting documents during the last quarter of their junior year, to enter the I.D.P. in the first quarter of their senior year. In addition, applicants must submit an application to the M.S. program during the senior year, prior to the deadline of the MS application. Students with a major GPA of 3.500 or above may waive the GRE requirement in the M.S. application. Before applying to the I.D.P., students are strongly advised to consult with both the undergraduate and graduate advisors.

Once a student enters the graduate phase of the I.D.P., they follow the course requirements for the Master's degree. A maximum of 6 units taken in the undergraduate phase can be transferred to the M.S. provided they have not been used to satisfy any requirements of the B.S. degree.

**Major Advisors. **For a current list of faculty and staff advisors, see https://statistics.ucdavis.edu/undergrad/advising

**Graduate Advisor.** Jie Peng (Statistics)

Alexander Aue, Ph.D., Chairperson of the Program

Jie Peng, Ph.D., Vice Chairperson for Graduate Affairs

**Program Office. **4118 Mathematical Sciences Building; https://statistics.ucdavis.edu/

**Faculty. **https://statistics.ucdavis.edu/people

**Graduate Study.** The Graduate Program in Statistics offers programs of study and research leading to the M.S. and Ph.D. degrees. The M.S. gives students a strong foundation in the theory of statistics as well as substantial familiarity with the most widely used statistical methods. Facility in computer programming is essential for some of the course work. The supervised statistical consulting required of all M.S. students has proven to be a valuable educational experience. The Ph.D. program combines advanced course work in statistics and probability with the opportunity for in-depth concurrent study in an applied field. For detailed information contact the Chairperson of the Program or the Graduate Advisor.

**Standard Track: **32 units of core coursework and 12 units of electives are required for a total of 44 units.

**Emphasis in Data Science Track: **36 units of core coursework and 12 units of electives are required for a total of 48 units.

**Preparation. **Preparation for the graduate program requires a year of calculus, a course in linear algebra, facility with a programming language and upper division coursework in mathematics and/or statistics. For admission to the Ph.D. program, course work requirements for the master's degree, and at least one semester/two quarters of advanced calculus must be completed.

**Graduate Advisor. **Jie Peng (Statistics)

Alexander Aue, Ph.D., Chairperson of the Program

Jie Peng, Ph.D., Vice Chairperson for Graduate Affairs

**Program Office. **4118 Mathematical Sciences Building; https://statistics.ucdavis.edu/

**Faculty. **https://statistics.ucdavis.edu/people

**Graduate
Study. **The Graduate
Program in Statistics offers programs of study and research
leading to the M.S. and Ph.D. degrees.
The M.S. gives students a strong foundation in the theory
of statistics as well as substantial familiarity with the most widely
used statistical methods.
Facility in computer programming is essential for some of the course
work. The supervised statistical
consulting required of all M.S. students has proven to be a valuable educational experience. The
Ph.D. program combines advanced course
work in statistics and probability with the opportunity for in-depth concurrent study in an applied field.
For detailed information contact the Chairperson of the Program or the Graduate
Advisor.

**Preparation. **Preparation for the graduate program
requires a year of calculus,
a course in linear
algebra, facility with a programming language and upper division coursework in mathematics and/or statistics. For admission to the Ph.D.
program, course work requirements for the master's degree, and at least one semester/two quarters
of advanced calculus
must be completed.

**Graduate
Advisor. **Jie Peng (Statistics)

(College of Letters & Science)

Alexander Aue, Ph.D., Chairperson of the Department

**Department Office.** 4118 Mathematical Sciences Building; https://statistics.ucdavis.edu

**The Program.** The Department offers a minor program in Statistics that consists of five upper division level courses focusing on the fundamentals of mathematical statistics and of the most widely used applied statistical methods.

**Minor Advisors.** For a current list of faculty and staff advisors, see https://statistics.ucdavis.edu/undergrad/advising.

Statistics

Units: 20

Preparation; not counted toward total units:

0

STA 013

Elementary Statistics (Active)

4

STA 032

Gateway to Statistical Data Science (Active)

4

STA 100

Applied Statistics for Biological Sciences (Active)

4

Additional preparatory courses will be needed based on the course prerequisites listed in the catalog.

STA 106

Applied Statistical Methods: Analysis of Variance (Active)

4

STA 108

Applied Statistical Methods: Regression Analysis (Active)

4

Choose a series:

8

STA 130A

Mathematical Statistics: Brief Course (Active)

4

STA 130B

Mathematical Statistics: Brief Course (Active)

4

STA 131A

Introduction to Probability Theory (Active)

4

STA 131B

Introduction to Mathematical Statistics (Active)

4

Choose one:

4

STA 101

Advanced Applied Statistics for the Biological Sciences (Active)

4

STA 104

Applied Statistical Methods: Nonparametric Statistics (Active)

4

STA 135

Multivariate Data Analysis (Active)

4

STA 137

Applied Time Series Analysis (Active)

4

STA 138

Analysis of Categorical Data (Active)

4

STA 141A

Fundamentals of Statistical Data Science (Active)

4

STA 141B

Data & Web Technologies for Data Analysis (Active)

4

STA 141C

Big Data & High Performance Statistical Computing (Active)

4

STA 142A

Statistical Learning I (Active)

4

STA 142B

Statistical Learning II (Active)

4

STA 144

Sampling Theory of Surveys (Active)

4

STA 145

Bayesian Statistical Inference (Active)

4

STA 160

Practice in Statistical Data Science (Active)

4

Total: 20

STA 010—Statistical Thinking (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): Two years of high school algebra. Statistics and probability in daily life. Examines principles of collecting, presenting and interpreting data in order to critically assess results reported in the media; emphasis is on understanding polls, unemployment rates, health studies; understanding probability,
risk and odds. (Letter.) GE credit: QL, SE. Effective: 2000 Spring Quarter.

STA 012—Introduction to Discrete Probability (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): Two years of high school algebra. Random experiments; countable sample spaces; elementary probability
axioms; counting formulas; conditional probability; independence; Bayes theorem; expectation; gambling problems; binomial, hypergeometric, Poisson, geometric, negative binomial and multinomial models; limiting distributions; Markov chains. Applications in the social, biological, and engineering sciences. (Letter.) GE credit: QL, SE. Effective: 1999 Fall Quarter.

STA 013—Elementary Statistics (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): Two years of high school algebra or Mathematics D. Descriptive statistics; basic probability concepts; binomial, normal, Student's t, and chi-square distributions. Hypothesis testing and confidence intervals for one and two means and proportions. Regression. Not open for credit for students who have completed STA 013V, or higher. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.

STA 013Y—Elementary Statistics (4) Active

Lecture—1.5 hour(s); Web Virtual Lecture—5 hour(s). Prerequisite(s): Two years of high school algebra or Mathematics D. Descriptive statistics; basic probability concepts; binomial, normal, Student's t, and chi-square distributions. Hypothesis testing and confidence intervals for one and two means and proportions. Regression. Not open for credit for students who have completed STA 013, or higher. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.

STA 015A—Introduction to Statistical Data Science I (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): Two years of high school algebra or Mathematics D. Principles of descriptive statistics. Concepts of randomness, probability models, sampling variability, hypothesis tests and confidence interval. Not open for credit to students who have taken STA 013 or STA 032 or STA 100. (Letter.) GE credit: QL, SE. Effective: 2020 Fall Quarter.

STA 015B—Introduction to Statistical Data Science II (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 015A C- or better or STA 013 C- or better or STA 032 C- or better or STA 100 C- or better. Programming in R; Summarization and visualization of different data types; Concepts of correlation, regression, classification and clustering. (Letter.) GE credit: QL, SE, VL. Effective: 2020 Fall Quarter.

STA 015C—Introduction to Statistical Data Science III (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 015B C- or better. Classical and Bayesian inference procedures in parametric statistical models. Nonparametric methods; resampling techniques; missing data. Use of statistical software. (Letter.) GE credit: QL, SE. Effective: 2020 Fall Quarter.

STA 032—Gateway to Statistical Data Science (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): MAT 016B C- or better or MAT 021B C- or better or MAT 017B C- or better. Probability concepts; programming in R; exploratory data analysis; sampling distribution; estimation and inference; linear regression; simulations; resampling methods. Alternative to STA 013 for students with a background in calculus and programming. Only 2 units of credit allowed to students who have taken STA 013; not open for credit to students who have taken STA 100. (Letter.) GE credit: QL, SE. Effective: 2019 Fall Quarter.

STA 035A—Statistical Data Science I (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): MAT 016A (can be concurrent) or MAT 017A (can be concurrent) or MAT 021A (can be concurrent). Principles of descriptive statistics; basic R programming; probability models; sampling variability; hypothesis tests; confidence intervals; statistical simulation. Not open for credit to students who have taken STA 032 or STA 100. Only 2 units credit for students who have taken STA 013. (Letter.) GE credit: QL, SE. Effective: 2020 Spring Quarter.

STA 035B—Statistical Data Science II (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): (STA 035A C- or better or STA 032 C- or better or STA 100 C- or better); (MAT 016A (can be concurrent) or MAT 017A (can be concurrent) or MAT 021A (can be concurrent)). Advanced programming and data manipulation in R. Principles of data visualization. Concepts of correlation, regression, analysis of variance, nonparametrics. (Letter.) GE credit: QL, SE, VL. Effective: 2020 Spring Quarter.

STA 035C—Statistical Data Science III (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 035B C- or better; (MAT 016B C- or better or MAT 017B C- or better or MAT 021B C- or better). Introduction to statistical learning; Bayesian paradigm; model selection; simultaneous inference; bootstrap and cross validation; classification and clustering methods; PCA; nonparametric smoothing techniques.
(Letter.) GE credit: QL, SE, SL. Effective: 2020 Spring Quarter.

STA 090X—Seminar (1-2) Active

Seminar—1-2 hour(s). Prerequisite(s): Consent of Instructor. High school algebra. Examination of a special topic in a small group setting. (Letter.) Effective: 1997 Winter Quarter.

STA 098—Directed Group Study (1-5) Active

Variable. Prerequisite(s): Consent of Instructor. (P/NP grading only.) Effective: 1997 Winter Quarter.

STA 099—Special Study for Undergraduates (1-5) Active

Variable. Prerequisite(s): Consent of Instructor. (P/NP grading only.) Effective: 2000 Spring Quarter.

STA 100—Applied Statistics for Biological Sciences (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): MAT 016B C- or better or MAT 017B C- or better or MAT 021B C- or better. Descriptive statistics, probability, sampling distributions, estimation, hypothesis testing, contingency tables, ANOVA, regression; implementation of statistical methods using computer package. Only 2 units credit allowed to students who have taken STA 013, STA 032 or 103; not open for credit to students who have taken STA 102. (Letter.) GE credit: QL, SE. Effective: 2019 Fall Quarter.

STA 101—Advanced Applied Statistics for the Biological Sciences (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 100 C- or better. Basic experimental designs, two-factor ANOVA without interactions, repeated measures ANOVA, ANCOVA, random effects vs. fixed effects, multiple regression, basic model building, resampling methods, multiple comparisons, multivariate methods, generalized linear models, Monte Carlo simulations. (Letter.) GE credit: QL, SE. Effective: 2019 Fall Quarter.

STA 103—Applied Statistics for Business & Economics (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): (STA 013 C- or better or STA 013Y C- or better or STA 032 C- or better or STA 100 C- or better); (MAT 016B C- or better or MAT 017B C- or better or MAT 021B C- or better). Descriptive statistics; probability; random variables; expectation; binomial, normal, Poisson, other univariate distributions; joint distributions; sampling distributions, central limit theorem; properties of estimators; linear combinations of random variables; testing and estimation; Minitab computing package. May be taught abroad. Only 2 units credit to students who have completed STA 100. (Letter.) GE credit: QL, SE. Effective: 2019 Fall Quarter.

STA 104—Applied Statistical Methods: Nonparametric Statistics (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 013 C- or better or STA 013Y C- or better or STA 032 C- or better or STA 100 C- or better. Sign and Wilcoxon tests, Walsh averages. Two-sample procedures. Inferences concerning scale. Kruskal-Wallis test. Measures of association. Chi square and Kolmogorov-Smirnov tests. (Letter.) GE credit: QL, SE. Effective: 2019 Fall Quarter.

STA 106—Applied Statistical Methods: Analysis of Variance (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 013 C- or better or STA 013Y C- or better or STA 032 C- or better or STA 100 C- or better. Basics of experimental design. One-way and two-way fixed effects analysis of variance models. Randomized complete and incomplete block design. Multiple comparisons procedures. One-way random effects model. (Letter.) GE credit: SE. Effective: 2019 Fall Quarter.

STA 108—Applied Statistical Methods: Regression Analysis (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 013 C- or better or STA 013Y C- or better or STA 032 C- or better or STA 100 C- or better. Simple linear regression, variable selection techniques, stepwise regression, analysis of covariance, influence measures, computing packages. (Letter.) GE credit: QL, SE, SL. Effective: 2019 Fall Quarter.

STA 109—Fundamentals of Statistical Learning (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 015C C- or better or STA 106 C- or better or STA 108 C- or better. Principles of supervised and unsupervised statistical learning. Regularization and cross validation; classification, clustering and dimension reduction techniques; nonparametric smoothing methods. Not open for credit to students who have taken STA 142A or ECS 171; only 2 units credit for students who have taken STA 035C. (Letter.) GE credit: SE. Effective: 2020 Fall Quarter.

STA 130A—Mathematical Statistics: Brief Course (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): (MAT 016C C- or better or MAT 017C C- or better or MAT 021C C- or better); (STA 013 C- or better or STA 013Y C- or better or STA 032 C- or better or STA 100 C- or better). Basic probability, densities and distributions, mean, variance, covariance, Chebyshev's inequality, some special distributions, sampling distributions, central limit theorem and law of large numbers, point estimation, some methods of estimation, interval estimation, confidence intervals for certain quantities, computing sample sizes. Only 2 units of credit allowed to students who have taken STA 131A. (Letter.) GE credit: QL, SE. Effective: 2019 Fall Quarter.

STA 130B—Mathematical Statistics: Brief Course (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. Transformed random variables, large sample properties of estimates. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of-fit tests. General linear model, least squares estimates, Gauss-Markov theorem. Analysis of variance, F-test. Regression and correlation, multiple regression. Selected topics. (Letter.) GE credit: QL, SE. Effective: 2019 Fall Quarter.

STA 131A—Introduction to Probability Theory (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): MAT 021C C- or better; (MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better); MAT 021D strongly recommended. Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem. Not open for credit to students who have completed MAT 135A. (Letter.) GE credit: QL, SE. Effective: 2019 Fall Quarter.

STA 131B—Introduction to Mathematical Statistics (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 131A C- or better or MAT 135A C- or better; Consent of Instructor. Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. (Letter.) GE credit: SE. Effective: 2019 Fall Quarter.

STA 131C—Introduction to Mathematical Statistics (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 131B C- or better. Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. (Letter.) GE credit: SE. Effective: 2019 Fall Quarter.

STA 135—Multivariate Data Analysis (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): (STA 130B C- or better or STA 131B C- or better); (MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better). Multivariate normal distribution; Mahalanobis distance; sampling distributions of the mean vector and covariance matrix; Hotellings T2; simultaneous inference; one-way MANOVA; discriminant analysis; principal components; canonical correlation; factor analysis. Intensive use of computer analyses and real data sets. (Letter.) GE credit: QL, SE. Effective: 2019 Fall Quarter.

STA 137—Applied Time Series Analysis (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 108 C- or better. Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. (Letter.) GE credit: QL, SE. Effective: 2020 Winter Quarter.

STA 138—Analysis of Categorical Data (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): (STA 130B or STA 131B) or (STA 106, STA 108). Varieties of categorical data, cross-classifications, contingency tables, tests for independence. Multidimensional tables and log-linear models, maximum likelihood estimation; tests of goodness-of-fit. Logit
models, linear logistic models. Analysis of incomplete tables. Packaged computer programs, analysis of real data. (Letter.) GE credit: QL, SE. Effective: 1997 Winter Quarter.

STA 141A—Fundamentals of Statistical Data Science (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 108 C- or better or STA 106 C- or better. Pass One & Pass Two: open to Statistics Majors, Biostatistics & Statistics graduate students; registration open to all students during schedule adjustment. Introduction to computing for data analysis & visualization, and simulation, using a high-level language (e.g., R). Computational reasoning, computationally intensive statistical methods, reading tabular & non-standard data. Not open for credit to students who have taken STA 141 or STA 242. (Letter.) Effective: 2020 Spring Quarter.

STA 141B—Data & Web Technologies for Data Analysis (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 141A C- or better. Pass One and Pass Two restricted to Statistics majors and graduate students in Statistics and Biostatistics. Open to all students during Open Registration. Essentials of using relational databases and SQL. Processing data in blocks. Scraping Web pages and using Web services/APIs. Basics of text mining. Interactive data visualization with Web technologies. Computational data workflow and best practices. Statistical methods. (Letter.) Effective: 2019 Fall Quarter.

STA 141C—Big Data & High Performance Statistical Computing (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 141B C- or better or (STA 141A C- or better, (ECS 010 C- or better or ECS 032A C- or better)). Pass One and Pass Two restricted to Statistics majors and graduate students in Statistics and Biostatistics; open to all students during Open registration. High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning. (Letter.) Effective: 2019 Fall Quarter.

STA 142A—Statistical Learning I (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 141A C- or better; (STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better); STA 131A or MAT 135A preferred. Pass One restricted to Statistics majors. Fundamental concepts and methods in statistical learning with emphasis on supervised learning. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. Only 2 units of credit for students who have previously taken ECS 171. (Letter.) Effective: 2020 Winter Quarter.

STA 142B—Statistical Learning II (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 142A C- or better; (STA 130B C- or better or STA 131B C- or better); STA 131B preferred. Pass One restricted to Statistics majors. Fundamental concepts and methods in statistical learning with emphasis on unsupervised learning. Principles, methodologies and applications of clustering methods, dimension reduction and manifold learning techniques, graphical models and latent variables modeling. (Letter.) Effective: 2020 Winter Quarter.

STA 144—Sampling Theory of Surveys (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): (STA 130B or STA 131B) or (STA 106, STA 108). Simple random, stratified random, cluster, and systematic sampling plans; mean, proportion, total, ratio, and regression estimators for these plans; sample survey design, absolute and relative error, sample size selection, strata construction; sampling and nonsampling sources of error. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.

STA 145—Bayesian Statistical Inference (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 130B C- or better or STA 131B C- or better. Subjective probability, Bayes Theorem, conjugate priors, non-informative priors, estimation, testing, prediction, empirical Bayes methods, properties of Bayesian procedures, comparisons with classical procedures, approximation techniques, Gibbs sampling, hierarchical Bayesian analysis, applications, computer implemented data analysis. (Letter.) GE credit: QL, SE. Effective: 2019 Fall Quarter.

STA 160—Practice in Statistical Data Science (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 106 C- or better; STA 108 C- or better; (STA 130B C- or better or STA 131B C- or better); STA 141A C- or better. Open to undergraduate Statistics majors. Principles and practice of interdisciplinary, collaborative data analysis; complete case study review and team data analysis project. (Letter.) Effective: 2020 Spring Quarter.

STA 190X—Seminar (1-2) Active

Seminar—1-2 hour(s). Prerequisite(s): STA 013 or STA 013Y or STA 032 or STA 100 or STA 103. In-depth examination of a special topic in a small group setting. (Letter.) Effective: 2018 Spring Quarter.

STA 192—Internship in Statistics (1-12) Active

Internship—3-36 hour(s); Term Paper. Prerequisite(s): Consent of Instructor. Upper division standing. Work experience in statistics. (P/NP grading only.) Effective: 1997 Winter Quarter.

STA 194HA—Special Studies for Honors Students (4) Active

Independent Study—12 hour(s). Prerequisite(s): Senior qualifying for honors. Directed reading, research and writing, culminating in the completion of a senior honors thesis or project under direction of a faculty advisor. (Letter.) GE credit: SE. Effective: 1997 Winter Quarter.

STA 194HB—Special Studies for Honors Students (4) Active

Independent Study—12 hour(s). Prerequisite(s): Senior qualifying for honors. Directed reading, research and writing, culminating in the completion of a senior honors thesis or project under direction of a faculty advisor. (Letter.) GE credit: SE. Effective: 1997 Winter Quarter.

STA 198—Directed Group Study (1-5) Active

Variable. Prerequisite(s): Consent of Instructor. Directed group study. May be taught abroad. (P/NP grading only.) Effective: 1997 Winter Quarter.

STA 199—Special Study for Advanced Undergraduates (1-5) Active

Variable. Prerequisite(s): Consent of Instructor. (P/NP grading only.) Effective: 1997 Winter Quarter.

STA 200A—Introduction to Probability Theory (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): MAT 021A; MAT 021B; MAT 021C; MAT 022A; Consent of Instructor. Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem. (Letter.) Effective: 2018 Winter Quarter.

STA 200B—Introduction to Mathematical Statistics I (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 200A; or Consent of Instructor. Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. (Letter.) Effective: 2018 Winter Quarter.

STA 200C—Introduction to Mathematical Statistics II (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 200B; or Consent of Instructor. Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. (Letter.) Effective: 2019 Summer Session 1.

STA 201—SAS Programming for Statistical Analysis (3) Active

Lecture—2 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): Introductory, upper division statistics course; some knowledge of vectors and matrices; STA 106 or STA 108 or the equivalent suggested. Introductory SAS language, data management, statistical applications, methods. Includes basics, graphics, summary statistics, data sets, variables and functions, linear models, repetitive code, simple macros, GLIM and GAM, formatting output, correspondence analysis, bootstrap. Prepare SAS base programmer certification exam. (Letter.) Effective: 2013 Fall Quarter.

STA 205—Statistical Methods for Research with SAS (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): An introductory upper division statistics course and some knowledge of vectors and matrices; STA 100, or STA 102, or STA 103 suggested or the equivalent. Focus on linear statistical models widely used in scientific research. Emphasis on concepts, methods and data analysis using SAS. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA. (Letter.) Effective: 2008 Fall Quarter.

STA 206—Statistical Methods for Research–I (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): Introductory statistics course; some knowledge of vectors and matrices. Focus on linear statistical models. Emphasis on concepts, method and data analysis; formal mathematics kept to minimum. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. Use of professional level software. (Letter.) Effective: 2013 Fall Quarter.

STA 207—Statistical Methods for Research–II (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 206; Knowledge of vectors and matrices. Linear and nonlinear statistical models emphasis on concepts, methods/data analysis using professional level software; formal mathematics kept to minimum. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures. (Letter.) Effective: 2013 Fall Quarter.

STA 208—Statistical Methods in Machine Learning (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 206; STA 207; STA 135; Or their equivalents. Focus on linear and nonlinear statistical models. Emphasis on concepts, methods, and data analysis; formal mathematics kept to minimum. Topics include resampling methods, regularization techniques in regression and modern classification, cluster analysis and dimension reduction techniques. Use professional level software. (Letter.) Effective: 2013 Fall Quarter.

STA 209—Optimization for Big Data Analytics (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 200A; STA 208. Optimization algorithms for solving problems in statistics, machine learning, data analytics. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newton’s method.)
(Letter.) Effective: 2018 Spring Quarter.

STA 220—Data & Web Technologies for Data Analysis (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Essentials of using relational databases and SQL. Processing data in blocks. Scraping Web pages and using Web services/APIs. Basics of text mining. Interactive data visualization with Web technologies. Computational data workflow and best practices. Statistical Methods. (Letter.) Effective: 2020 Winter Quarter.

STA 221—Big Data & High Performance Statistical Computing (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 220. High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning. (Letter.) Effective: 2020 Spring Quarter.

STA 222—Biostatistics: Survival Analysis (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 131C. Incomplete data; life tables; nonparametric methods; parametric methods; accelerated failure time models; proportional hazards models; partial likelihood; advanced topics.
(Same course as BST 222.) (Letter.) Effective: 2002 Fall Quarter.

STA 223—Biostatistics: Generalized Linear Models (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 131C. Likelihood and linear regression; generalized linear model; Binomial regression; case-control studies; dose-response and bioassay; Poisson regression; Gamma regression; quasi-likelihood models; estimating equations; multivariate GLMs. (Same course as BST 223.) (Letter.) Effective: 2002 Fall Quarter.

STA 224—Analysis of Longitudinal Data (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): ((STA 222, STA 223) or (BST 222, BST 223)); STA 232B; or Consent of Instructor. Standard and advanced methodology, theory, algorithms, and applications relevant for analysis of repeated measurements and longitudinal data in biostatistical and statistical settings. (Same course as BST 224.) (Letter.) Effective: 2005 Spring Quarter.

STA 225—Clinical Trials (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 223 or BST 223; or Consent of Instructor. Basic statistical principles of clinical designs, including bias, randomization, blocking, and masking. Practical applications of widely-used designs, including dose-finding, comparative and cluster randomization designs. Advanced statistical procedures for analysis of data collected in clinical trials. (Same course as BST 225.) (Letter.) Effective: 2005 Spring Quarter.

STA 226—Statistical Methods for Bioinformatics (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 131C; or Consent of Instructor. Data analysis experience recommended. Standard and advanced statistical methodology, theory, algorithms, and applications relevant to the analysis of -omics data. (Same course as BST 226.) (Letter.) Effective: 2007 Fall Quarter.

STA 231A—Mathematical Statistics I (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 131A; STA 131B; STA 131C; MAT 025; MAT 125A; Or equivalent of MAT 025 and MAT 125A. First part of three-quarter sequence on mathematical statistics. Emphasizes foundations. Topics include basic concepts in asymptotic theory, decision theory, and an overview of methods of point estimation.
(Letter.) Effective: 2008 Summer Session 1.

STA 231B—Mathematical Statistics II (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 231A. Second part of a three-quarter sequence on mathematical statistics. Emphasizes: hyposthesis testing (including multiple testing) as well as theory for linear models.
(Letter.) Effective: 2008 Summer Session 1.

STA 231C—Mathematical Statistics III (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 231A; STA 231B. Third part of three-quarter sequence on mathematical statistics. Emphasizes large sample theory and their applications. Topics include statistical functionals, smoothing methods and optimization techniques relevant for statistics. (Letter.) Effective: 2008 Summer Session 1.

STA 232A—Applied Statistics I (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 106; STA 108; STA 131A; STA 131B; STA 131C; MAT 167. Estimation and testing for the general linear model, regression, analysis of designed experiments, and missing data techniques. (Letter.) Effective: 2011 Fall Quarter.

STA 232B—Applied Statistics II (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 106; STA 108; STA 131A; STA 131B; STA 131C; STA 232A; MAT 167. Alternative approaches to regression, model selection, nonparametric methods amenable to linear model framework and their applications. (Letter.) Effective: 2011 Fall Quarter.

STA 232C—Applied Statistics III (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 106; STA 108; STA 131C; STA 232B; MAT 167. Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis. (Letter.) Effective: 2011 Fall Quarter.

STA 233—Design Experiments (3) Active

Lecture—3 hour(s). Prerequisite(s): STA 131C. Topics from balanced and partially balanced incomplete block designs, fractional factorials, and response surfaces. (Letter.) Effective: 1997 Winter Quarter.

STA 235A—Probability Theory (4) Active

Lecture—3 hour(s); Term Paper/Discussion—1 hour(s). Prerequisite(s): (MAT 125B, MAT 135A) or STA 131A; or Consent of Instructor. Measure-theoretic foundations, abstract integration, independence, laws of large numbers, characteristic functions, central limit theorems. Weak convergence in metric spaces, Brownian motion, invariance principle. Conditional expectation. Topics selected from: martingales, Markov chains, ergodic theory. (Same course as MAT 235A.) (Letter.) Effective: 2007 Spring Quarter.

STA 235B—Probability Theory (4) Active

Lecture—3 hour(s); Term Paper/Discussion—1 hour(s). Prerequisite(s): STA 235A or MAT 235A; or Consent of Instructor. Measure-theoretic foundations, abstract integration, independence, laws of large numbers, characteristic functions, central limit theorems. Weak convergence in metric spaces, Brownian motion, invariance principle. Conditional expectation. Topics selected from: martingales, Markov chains, ergodic theory. (Same course as MAt 235B.) (Letter.) Effective: 2008 Spring Quarter.

STA 235C—Probability Theory (4) Active

Lecture—3 hour(s); Term Paper/Discussion—1 hour(s). Prerequisite(s): STA 235B or MAT 235B; or Consent of Instructor. Measure-theoretic foundations, abstract integration, independence, laws of large numbers, characteristic functions, central limit theorems. Weak convergence in metric spaces, Brownian motion, invariance principle. Conditional expectation. Topics selected from: martingales, Markov chains, ergodic theory. (Same course as MAT 235C.) (Letter.) Effective: 2008 Spring Quarter.

STA 237A—Time Series Analysis (4) Active

Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 131B; Or the equivalent of STA 131B. Advanced topics in time series analysis and applications. Models for experimental data, measures of dependence, large-sample theory, statistical estimation and inference. Univariate and multivariate spectral analysis, regression, ARIMA models, state-space models, Kalman filtering. (Letter.) Effective: 1999 Fall Quarter.

STA 237B—Time Series Analysis (4) Active

Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 131B; STA 237A; Or the equivalent of STA 131B. Advanced topics in time series analysis and applications. Models for experimental data, measures of dependence, large-sample theory, statistical estimation and inference. Univariate and multivariate spectral analysis, regression, ARIMA models, state-space models, Kalman filtering. (Letter.) Effective: 1999 Fall Quarter.

STA 238—Theory of Multivariate Analysis (4) Active

Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 131B; STA 135. Multivariate normal and Wishart distributions, Hotellings T-Squared, simultaneous inference, likelihood ratio and union intersection tests, Bayesian methods, discriminant analysis, principal component and factor analysis, multivariate clustering, multivariate regression and analysis of variance, application to data. (Letter.) Effective: 1999 Fall Quarter.

STA 240A—Nonparametric Inference (4) Active

Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 231C; STA 235A, STA 235B, STA 235C recommended. Comprehensive treatment of nonparametric statistical inference, including the most basic materials from classical nonparametrics, robustness, nonparametric estimation of a distribution function from
incomplete data, curve estimation, and theory of resampling methodology. (Letter.) Effective: 2000 Winter Quarter.

STA 240B—Nonparametric Inference (4) Active

Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 231C; STA 235A, STA 235B, STA 235C recommended. Comprehensive treatment of nonparametric statistical inference, including the most basic materials from classical nonparametrics, robustness, nonparametric estimation of a distribution function from
incomplete data, curve estimation, and theory of re-sampling methodology. (Letter.) Effective: 2000 Winter Quarter.

STA 241—Asymptotic Theory of Statistics (4) Active

Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 231C; STA 235A, STA 235B, STA 235C desirable. Topics in asymptotic theory of statistics chosen from weak convergence, contiguity, empirical processes, Edgeworth expansion, and semiparametric inference. (Letter.) Effective: 2000 Spring Quarter.

STA 242—Introduction to Statistical Programming (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 130A; STA 130B; or equivalent of STA 130A and STA 130B. Essentials of statistical computing using a general-purpose statistical language. Topics include algorithms; design; debugging and efficiency; object-oriented concepts; model specification and fitting; statistical visualization; data and text processing; databases; computer systems and platforms; comparison of scientific programming languages. (Letter.) Effective: 2009 Winter Quarter.

STA 243—Computational Statistics (4) Active

Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): (STA 130A, STA 130B); (MAT 067 or MAT 167); Or equivalent of STA 130A and 130B, or equivalent of MAT 167 or MAT 067. Numerical analysis; random number generation; computer experiments and resampling techniques (bootstrap, cross validation); numerical optimization; matrix decompositions and linear algebra computations; algorithms (markov chain monte carlo, expectation-maximization); algorithm design and efficiency; parallel and distributed computing. (Letter.) Effective: 2009 Winter Quarter.

STA 250—Topics in Applied & Computational Statistics (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 131A; STA 232A recommended, not required. Resampling, nonparametric and semiparametric methods, incomplete data analysis, diagnostics, multivariate and time series analysis, applied Bayesian methods, sequential analysis and quality control, categorical data analysis, spatial and image analysis, computational biology, functional data analysis, models for correlated data, learning theory. May be repeated for credit with consent of graduate advisor. (Letter.) Effective: 2006 Spring Quarter.

STA 251—Topics in Statistical Methods & Models (4) Active

Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 231B; Or the equivalent of STA 231B. Topics may include Bayesian analysis, nonparametric and semiparametric regression, sequential analysis, bootstrap, statistical methods in high dimensions, reliability, spatial processes, inference for stochastic process, stochastic methods in finance, empirical processes, change-point problems, asymptotics for parametric, nonparametric and semiparametric models, nonlinear time series, robustness. May be repeated for credit when topic differs and only with consent of graduate advisor. (Letter.) Effective: 2002 Fall Quarter.

STA 252—Advanced Topics in Biostatistics (4) Active

Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): (STA 222 or BST 222); (STA 223 or BST 223). Biostatistical methods and models selected from the following: genetics, bioinformatics and genomics; longitudinal or functional data; clinical trials and experimental design; analysis of environmental data; dose-response, nutrition and toxicology; survival analysis; observational studies and epidemiology; computer-intensive or Bayesian methods in biostatistics. May be repeated for credit when topic differs and only with consent of graduate advisor. (Same course as BST 252.) (Letter.) Effective: 2002 Fall Quarter.

STA 260—Statistical Practice & Data Analysis (3) Active

Lecture/Discussion—3 hour(s). Prerequisite(s): STA 207 or STA 232B; Working knowledge of advanced statistical software and the equivalent of STA 207 or STA 232B. Open to students enrolled in the graduate program in Statistics or Biostatistics, as the class also serves to provide professional service to clients and collaborators who work with the students. Principles and practice of interdisciplinary collaboration in statistics, statistical consulting, ethical aspects, and basics of data analysis and study design. Emphasis on practical consulting and collaboration of statisticians with clients and scientists under instructor supervision. May be repeated up to 1 Time(s). (Letter.) Effective: 2014 Fall Quarter.

STA 280—Orientation to Statistical Research (2) Active

Seminar—2 hour(s). Prerequisite(s): Consent of Instructor. Guided orientation to original statistical research papers, and oral presentations in class of such papers by students under the supervision of a faculty member. May be repeated up to 1 Time(s). (S/U grading only.) Effective: 1999 Spring Quarter.

STA 290—Seminar in Statistics (1-6) Active

Variable. Prerequisite(s): Consent of Instructor. Seminar on advanced topics in probability and statistics. (S/U grading only.) Effective: 1997 Winter Quarter.

STA 292—Graduate Group in Statistics Seminar (1-2) Active

Seminar—1-2 hour(s). Prerequisite(s): Consent of Instructor. Graduate standing. Advanced study in various fields of statistics with emphasis in applied topics, presented by members of the Graduate Group in Statistics and other guest speakers. (S/U grading only.) Effective: 1997 Fall Quarter.

STA 298—Directed Group Study (1-5) Active

Variable—3-15 hour(s). Prerequisite(s): Consent of Instructor. Graduate standing. Special topics in Statistics appropriate for study at the graduate level. May be repeated for credit. (Letter.) Effective: 2004 Spring Quarter.

STA 299—Individual Study (1-12) Active

Variable. Prerequisite(s): Consent of Instructor. (S/U grading only.) Effective: 1997 Winter Quarter.

STA 299D—Dissertation Research (1-12) Active

Variable—3-36 hour(s). Prerequisite(s): Consent of Instructor. Advancement to candidacy for Ph.D. Research in Statistics under the supervision of major professor. May be repeated for credit. (S/U grading only.) Effective: 2004 Spring Quarter.

STA 390—Methods of Teaching Statistics (2) Active

Lecture/Discussion—1 hour(s); Laboratory—1 hour(s). Prerequisite(s): Graduate standing. Practical experience in methods/problems of teaching statistics at university undergraduate level. Lecturing techniques, analysis of tests and supporting material, preparation and grading of examinations, and use of statistical software. Emphasis on practical training. May be repeated for credit. (S/U grading only.) Effective: 2004 Spring Quarter.

STA 396—Teaching Assistant Training Practicum (1-4) Active

Variable. Prerequisite(s): Consent of Instructor. Graduate standing. May be repeated for credit. (P/NP grading only.) Effective: 1997 Winter Quarter.

STA 401—Methods in Statistical Consulting (3) Active

Lecture—3 hour(s); Discussion—1 hour(s). Students must be enrolled in the graduate program in Statistics or Biostatistics. Introduction to consulting, in-class consulting as a group, statistical consulting with clients, and in-class discussion of consulting problems. Clients are drawn from a pool of University clients. May be repeated for credit with consent of graduate advisor. (S/U grading only.) Effective: 2006 Spring Quarter.