Statistics

University of California, Berkeley

About the Program

Bachelor of Arts (BA)

The undergraduate major at Berkeley provides a systematic and thorough grounding in applied and theoretical statistics and in probability. The quality and dedication of the teaching staff and faculty are extremely high. A major in Statistics from Berkeley is an excellent preparation for a career in science or industry, or for further academic study in a wide variety of fields. The department has particular strength in Machine Learning, a key ingredient of the emerging field of Data Science. It is also very useful to combine studies of statistics and probability with other subjects. Our department excels at interdisciplinary science, and more than half of the department's undergraduate students are double or triple majors.

Students interested in teaching statistics and mathematics in middle or high school should pursue the teaching option within the major. Students interested in teaching should also consider the Cal Teach Program.

Declaring the Major

Students should apply in the semester they will complete their prerequisites. For applicants with prerequisites in progress, applications will be reviewed after the grades for all prerequisites are available, 2-3 weeks after finals. For applicants who have completed all prerequisites in a previous term, applications will be reviewed and processed within a week.

For detailed information regarding the process of declaring the major, please see the Statistics Department website.

Minor Program

The minor is for students who want to study a significant amount of statistics and probability at the upper division level. For information regarding the requirements, please see the Minor Requirements tab on this page.

Students may obtain the minor once they have completed both the lower division prerequisites and the five upper division requirements. Students will need to meet with the undergraduate faculty adviser and bring the following items with them:

After meeting with the faculty adviser, students should bring their forms to the undergraduate student services adviser.

Visit Department Website

Major Requirements

In addition to the University, campus, and college requirements, listed on the College Requirements tab, students must fulfill the below requirements specific to their major program.

General Guidelines

  1. All courses taken to fulfill the major requirements below must be taken for graded credit, other than courses listed which are offered on a Pass/No Pass basis only. Other exceptions to this requirement are noted as applicable.
  2. No more than one upper division course may be used to simultaneously fulfill requirements for a student's major and minor programs, with the exception of minors offered outside of the College of Letters & Science.
  3. A minimum grade point average (GPA) of 2.0 must be maintained in both upper and lower division courses used to fulfill the major requirements.

For information regarding residence requirements and unit requirements, please see the College Requirements tab.


Lower Division Prerequisites (Four Courses)

Students must earn a minimum 3.2 UC grade point average in the lower division math prerequisites with no lower than a C in each. 1
MATH 1ACalculus4
MATH 1BCalculus4
MATH 53Multivariable Calculus4
MATH 54Linear Algebra and Differential Equations4

Upper Division Requirements (Nine Courses)

Core Statistics Courses (3)
STAT 133Concepts in Computing with Data3
STAT 134Concepts of Probability 2, 33
STAT 135Concepts of Statistics 34
Statistics Electives (3)
Select three statistics electives from the following; at least one of the selections must have a lab:10-12
Stochastic Processes
Linear Modelling: Theory and Applications (LAB COURSE)
Linear Modelling: Theory and Applications
Sampling Surveys (LAB COURSE)
Introduction to Time Series (LAB COURSE)
Modern Statistical Prediction and Machine Learning (LAB COURSE)
Game Theory
Seminar on Topics in Probability and Statistics
The Design and Analysis of Experiments (LAB COURSE)
Reproducible and Collaborative Statistical Data Science (LAB COURSE)
Applied Cluster Courses (3)
Select three applied cluster courses. See Cluster Course Information and Approved Cluster Courses below the Teaching Option requirements.9-12

Upper Division Requirements: Teaching Option (Nine Courses)

Core Statistics Courses (3)
STAT 133Concepts in Computing with Data3
STAT 134Concepts of Probability 2, 33
STAT 135Concepts of Statistics 34
Statistics Electives (2)
Select two of the following; at least one course must include a lab:7-8
Stochastic Processes
Linear Modelling: Theory and Applications (LAB COURSE)
Linear Modelling: Theory and Applications (LAB COURSE)
Sampling Surveys (LAB COURSE)
Introduction to Time Series (LAB COURSE)
Modern Statistical Prediction and Machine Learning (LAB COURSE)
Game Theory
Seminar on Topics in Probability and Statistics
The Design and Analysis of Experiments (LAB COURSE)
Reproducible and Collaborative Statistical Data Science (LAB COURSE)
Teaching Track Cluster (4)
MATH 110Linear Algebra4
MATH 113Introduction to Abstract Algebra4
MATH 151Mathematics of the Secondary School Curriculum I4
MATH 152Mathematics of the Secondary School Curriculum II4
or MATH 153 Mathematics of the Secondary School Curriculum III
1

Students who have completed any of the math prerequisites at a non-UC institution should look at the Statistics Major Frequently Asked Questions on the Statistics Department website.

2

Other non-statistics UC Berkeley courses, such as IND ENG 172, cannot be used to fulfill this requirement

3

At least a B- in either STAT 134 or STAT 135 is a prerequisite to declare the major, with no more than one course repeated between STAT 134 and STAT 135.


Cluster Course Information

The applied cluster is a chance to learn about areas in which statistics can be applied and to learn specialized techniques not taught in the Statistics Department. Students need to design their own applied cluster. The courses should have a unifying theme. Picking their own applied cluster is a valuable exercise that gives students a chance to explore and refine their interests and to develop a coherent course of study. A preapproved list has been provided below. However, it is not exhaustive. If students would like to use a course that is not on the list, the undergraduate major faculty adviser must approve it. Clusters may consist of courses from more than one department, but at least two must be approved courses from the same department. Students' choices should reflect a theme so that students study some area of application in breadth and depth. Cluster courses should meet the following criteria:

  1. Courses must be upper division courses and at least 3 units.
  2. Courses in the biological and physical sciences, chemistry, and engineering are often acceptable.
  3. Courses in social sciences must be quantitative.
  4. Courses with statistics prerequisites are often acceptable.
  5. Courses that are similar to courses offered in the Statistics Department are not acceptable.
  6. Courses that primarily teach how to use a particular software package are not acceptable.
  7. Courses that focus on the use of spreadsheet software (e.g., UGBA 104) are not acceptable.
  8. Courses should be taken in the home department. For instance, economics classes should be taken in the economics or business department.
  9. Seminars and special topics courses require approval by the undergraduate faculty adviser.

​Approved Cluster Courses

Of the three applied cluster courses required for the major, at least two must be approved courses from the same department. This is not an exhaustive list.

ANTHRO C103Introduction to Human Osteology6
ANTHRO 115Introduction to Medical Anthropology4
ANTHRO 121CHistorical Archaeology: Historical Artifact Identification and Analysis4
ANTHRO C124C/INTEGBI C187Human Biogeography of the Pacific3
ANTHRO 127ABioarchaeology: Introduction to Skeletal Biology and Bioarchaeology4
ANTHRO 127BBioarchaeology: Reconstruction of Life in Bioarchaeology4
ANTHRO C129D/INTEGBI C155Holocene Paleoecology: How Humans Changed the Earth3
ANTHRO C129FThe Archaeology of Health and Disease4
ANTHRO C131/EPS C171Course Not Available4
ANTHRO 132AAnalysis of Archaeological Materials: Analysis of Archaeological Ceramics4
ANTHRO 135Paleoethnobotany: Archaeological Methods and Laboratory Techniques4
ANTHRO 135BEnvironmental Archaeology4
ANTHRO 169BResearch Theory and Methods in Socio-Cultural Anthropology5
ARCH 140Energy and Environment4
ARCH 150Introduction to Structures4
ARCH 154Design and Computer Analysis of Structure3
ASTRON: All courses that meet the above criteria
BIO ENG: All courses that meet the above criteria
CHM ENG: All courses that are at least 3 units
CHEM: All courses that meet the above criteria
CY PLAN 101Introduction to Urban Data Analytics4
CY PLAN 118ACThe Urban Community4
CY PLAN 119Planning for Sustainability3
CIV ENG: All courses that meet the above criteria
COG SCI C100Basic Issues in Cognition3
COG SCI C101The Mind and Language4
COG SCI C102Scientific Approaches to Consciousness3
COG SCI C110Course Not Available4
COG SCI C124Course Not Available3
COG SCI C126Perception3
COG SCI C131Course Not Available
COG SCI C140Quantitative Methods in Linguistics4
COG SCI C147Language Disorders3
COMP SCI: All courses that meet the above criteria, except COMPSCI 174
DEMOG 110Introduction to Population Analysis3
DEMOG C175Economic Demography4
EPS: All courses that meet the above criteria, except EPS C100, EPS C120
ECON 101AEconomic Theory--Micro4
ECON 101BEconomic Theory--Macro4
ECON C102Natural Resource Economics4
ECON C103Introduction to Mathematical Economics4
ECON 104Advanced Microeconomic Theory4
ECON 119Psychology and Economics4
ECON 121Industrial Organization and Public Policy4
ECON C125Environmental Economics4
ECON 126Course Not Available4
ECON 131Public Economics4
Only one from the following may be used in an applied cluster for the Statistics major:
Financial Economics
Principles of Engineering Economics
Introduction to Finance
ECON 138Financial and Behavioral Economics4
ECON 141Econometric Analysis4
ECON 174Global Poverty and Impact Evaluation4
ECON C175Economic Demography3
or ECON N175 Economic Demography
ECON C181International Trade4
ECON 182International Monetary Economics4
EL ENG: All courses that meet the above criteria
ENE,RES C100Energy and Society4
ENE,RES 102Quantitative Aspects of Global Environmental Problems4
ENE,RES C130Course Not Available4
ENE,RES 175Water and Development4
ENGIN 115Engineering Thermodynamics4
ENGIN 117Methods of Engineering Analysis3
ENVECON C101Environmental Economics4
ENVECON C102Natural Resource Economics4
ENVECON C115Modeling and Management of Biological Resources4
ENVECON 131Globalization and the Natural Environment3
ENVECON 140ACEconomics of Race, Agriculture, and the Environment3
ENVECON 141Agricultural and Environmental Policy4
ENVECON 142Industrial Organization with Applications to Agriculture and Natural Resources4
ENVECON 143Economics of Innovation and Intellectual Property3
ENVECON 145Health and Environmental Economic Policy4
ENVECON 147Regulation of Energy and the Environment4
ENVECON C151Economic Development4
ENVECON 152Advanced Topics in Development and International Trade3
ENVECON 153Population, Environment, and Development3
ENVECON 154Economics of Poverty and Technology3
ENVECON 161Advanced Topics in Environmental and Resource Economics4
ENVECON 162Economics of Water Resources3
ENVECON C175The Economics of Climate Change4
ENVECON C181International Trade4
ENVECON C183Forest Ecosystem Management4
ENV SCI 100Introduction to the Methods of Environmental Science4
ENV SCI 125Environments of the San Francisco Bay Area3
ESPM 173Introduction to Ecological Data Analysis3
GEOG C139Atmospheric Physics and Dynamics3
GEOG 140APhysical Landscapes: Process and Form4
GEOG 142Climate Dynamics4
GEOG 143Global Change Biogeochemistry3
GEOG 144Principles of Meteorology3
GEOG C145Geological Oceanography4
GEOG 148Biogeography4
GEOG C188Geographic Information Systems4
IND ENG 115Industrial and Commercial Data Systems3
IND ENG 130Methods of Manufacturing Improvement3
IND ENG 131Discrete Event Simulation3
IND ENG 150Production Systems Analysis3
IND ENG 151Service Operations Design and Analysis3
IND ENG 153Logistics Network Design and Supply Chain Management3
IND ENG 160Nonlinear and Discrete Optimization3
IND ENG 162Linear Programming and Network Flows3
IND ENG 166Decision Analytics3
IND ENG 170Industrial Design and Human Factors3
IND ENG 171Technology Firm Leadership3
INFO 114User Experience Research3
INFO 152Mobile Application Design and Development3
INTEGBI C101
& INTEGBI C101L
Course Not Available
and Course Not Available
0
INTEGBI 102LFIntroduction to California Plant Life with Laboratory4
INTEGBI 103Course Not Available4
INTEGBI 106Course Not Available4
INTEGBI 106APhysical and Chemical Environment of the Ocean4
INTEGBI C107Course Not Available4
INTEGBI 113LPaleobiological Perspectives on Ecology and Evolution4
INTEGBI 115Introduction to Systems in Biology and Medicine4
INTEGBI 117
117LF
Medical Ethnobotany
and Medical Ethnobotany Laboratory
4
INTEGBI 118Host-Pathogen Interactions: A Trans-Discipline Outlook4
INTEGBI 119Evaluating Scientific Evidence in Medicine3
INTEGBI C125LIntroduction to the Biomechanical Analysis of Human Movement4
INTEGBI 128Sports Medicine3
INTEGBI C129LHuman Physiological Assessment3
INTEGBI 131General Human Anatomy3
INTEGBI 132Survey of Human Physiology4
INTEGBI 135The Mechanics of Organisms4
INTEGBI 137Human Endocrinology4
INTEGBI 138Comparative Endocrinology4
INTEGBI 140Biology of Human Reproduction4
INTEGBI C142LIntroduction to Human Osteology6
INTEGBI C143ABiological Clocks: Physiology and Behavior3
INTEGBI C143BHormones and Behavior3
INTEGBI C144Animal Behavior4
INTEGBI 148Comparative Animal Physiology3
INTEGBI C149Molecular Ecology4
INTEGBI 151Plant Physiological Ecology4
INTEGBI 152Environmental Toxicology4
INTEGBI 153Ecology3
INTEGBI 154Plant Ecology3
INTEGBI C155Holocene Paleoecology: How Humans Changed the Earth3
INTEGBI C156Principles of Conservation Biology4
INTEGBI 157LFEcosystems of California4
INTEGBI 158LFBiology and Geomorphology of Tropical Islands13
INTEGBI 160Evolution4
INTEGBI 161Population and Evolutionary Genetics4
INTEGBI 162Ecological Genetics4
INTEGBI 163Molecular and Genomic Evolution3
INTEGBI 164Human Genetics and Genomics4
INTEGBI 165Course Not Available4
INTEGBI 166Evolutionary Biogeography4
INTEGBI 168
168L
Systematics of Vascular Plants
and Systematics of Vascular Plants with Laboratory
6
INTEGBI 169Evolutionary Medicine4
INTEGBI 173LFMammalogy with Laboratory5
INTEGBI 174LFOrnithology with Laboratory4
INTEGBI 175LFHerpetology with Laboratory4
INTEGBI C185LHuman Paleontology5
INTEGBI C187Human Biogeography of the Pacific3
ISF C101Course Not Available
LD ARCH 122Hydrology for Planners4
LD ARCH 132Computer Applications in Environmental Design4
LD ARCH C188Geographic Information Systems4
L & S C140UThe Archaeology of Health and Disease4
L & S 170ACCourse Not Available4
LINGUIS 100Introduction to Linguistic Science4
LINGUIS C109Course Not Available4
LINGUIS 110Introduction to Phonetics and Phonology4
LINGUIS 113Experimental Phonetics3
LINGUIS 140Introduction to Field Methods3
LINGUIS C147Language Disorders3
LINGUIS C160Quantitative Methods in Linguistics4
MATH: All courses that meet the above criteria
MEC ENG: All courses that meet the above criteria
MCELLBI: All courses that meet the above criteria
MUSIC 108Music Perception and Cognition3
MUSIC 108MMusic Perception and Cognition3
MUSIC 109Music Cognition: The Mind Behind the Musical Ear3
NUC ENG: All courses that meet the above criteria
NUSCTX: All courses that meet the above criteria
PHILOS 128Philosophy of Science4
PHILOS 140AIntermediate Logic4
PHILOS 140BIntermediate Logic4
PHILOS 142Philosophical Logic4
PHILOS 146Philosophy of Mathematics4
PHILOS 148Course Not Available4
PHYS ED C129Human Physiological Assessment3
PHYS ED C165Introduction to the Biomechanical Analysis of Human Movement4
PHYSICS: All courses that meet the above criteria
PLANTBI: All courses of at least 3 units
PLANTBI C102/C102LCourse Not Available4
PLANTBI 120
120L
Biology of Algae
and Laboratory for Biology of Algae
4
POL SCI C131AApplied Econometrics and Public Policy4
PSYCH 110Introduction to Biological Psychology3
PSYCH C113Biological Clocks: Physiology and Behavior3
PSYCH 114Biology of Learning and Neural Plasticity3
PSYCH C116Hormones and Behavior3
PSYCH 117Human Neuropsychology3
PSYCH 119Course Not Available3
PSYCH C120Basic Issues in Cognition3
PSYCH 121Animal Cognition3
PSYCH 122Introduction to Human Learning and Memory3
PSYCH C123Course Not Available
PSYCH C124Course Not Available
PSYCH 125The Developing Brain3
PSYCH C126Perception3
PSYCH C127Cognitive Neuroscience3
PSYCH C129Scientific Approaches to Consciousness3
PSYCH 130Clinical Psychology3
PSYCH 131Developmental Psychopathology3
PSYCH 133Psychology of Sleep3
PSYCH 140Developmental Psychology3
PSYCH 141Development During Infancy3
PSYCH C143Language Acquisition3
PSYCH 164Social Cognition3
PB HLTH C102Bacterial Pathogenesis3
PB HLTH 112Global Health: A Multidisciplinary Examination4
PB HLTH 126Health Economics and Public Policy3
PB HLTH C129The Aging Human Brain3
PB HLTH 140Introduction to Risk and Demographic Statistics4
PB HLTH 150AIntroduction to Epidemiology and Human Disease4
PB HLTH 150BIntroduction to Environmental Health Sciences3
PB HLTH 162APublic Health Microbiology3
PB HLTH C170BCourse Not Available
PB HLTH C172Course Not Available4
PUB POL 101Introduction to Public Policy Analysis4
PUB POL 103Wealth and Poverty4
PUB POL C103Wealth and Poverty4
PUB POL C142Applied Econometrics and Public Policy4
PUB POL 184Course Not Available4
RHETOR 107Rhetoric of Scientific Discourse4
RHETOR 170Rhetoric of Social Science4
SOCIOL 105Research Design and Sociological Methods5
SOCIOL 106Quantitative Sociological Methods4
UGBA 101AMicroeconomic Analysis for Business Decisions3
UGBA 101BMacroeconomic Analysis for Business Decisions3
UGBA 102AIntroduction to Financial Accounting3
UGBA 102BIntroduction to Managerial Accounting3
UGBA 106Marketing3
UGBA 113Managerial Economics3
UGBA 118International Trade3
UGBA 119Leading Strategy Implementation3
UGBA 120AAIntermediate Financial Accounting 14
UGBA 120ABIntermediate Financial Accounting 24
UGBA 120BAdvanced Financial Accounting4
UGBA 122Financial Information Analysis4
UGBA 126Auditing4
UGBA 131Corporate Finance and Financial Statement Analysis3
UGBA 132Financial Institutions and Markets3
UGBA 133Investments3
UGBA 136FBehavioral Finance3
UGBA 141Production and Operations Management3
UGBA 160Consumer Behavior3
UGBA 161Marketing Research: Data and Analytics3
UGBA 162Brand Management and Strategy3
UGBA 165Advertising Strategy3
UGBA 169Pricing3
UGBA 180Introduction to Real Estate and Urban Land Economics3
UGBA 183Introduction to Real Estate Finance3
UGBA 184Urban and Real Estate Economics3

Minor Requirements

Students who have a strong interest in an area of study outside their major often decide to complete a minor program. These programs have set requirements and are noted officially on the transcript in the memoranda section, but they are not noted on diplomas.

General Guidelines

  1. All courses taken to fulfill the minor requirements below must be taken for graded credit.
  2. A minimum of three of the upper division courses taken to fulfill the minor requirements must be completed at UC Berkeley.
  3. A minimum grade point average (GPA) of 2.0 is required for courses used to fulfill the minor requirements.
  4. Courses used to fulfill the minor requirements may be applied toward the Seven-Course Breadth requirement, for Letters & Science students.
  5. No more than one upper division course may be used to simultaneously fulfill requirements for a student's major and minor programs.
  6. All minor requirements must be completed prior to the last day of finals during the semester in which the student plans to graduate. Students who cannot finish all courses required for the minor by that time should see a College of Letters & Science adviser.
  7. All minor requirements must be completed within the unit ceiling. (For further information regarding the unit ceiling, please see the College Requirements tab.)

Requirements

Lower Division Prerequisites
MATH 1ACalculus4
MATH 1BCalculus4
MATH 53Multivariable Calculus4
MATH 54Linear Algebra and Differential Equations4
Upper Division Requirements
STAT 134Concepts of Probability3
STAT 135Concepts of Statistics4
Select three statistics electives from the following; at least one of the selections must have a lab:
Stochastic Processes
Linear Modelling: Theory and Applications (LAB COURSE)
Linear Modelling: Theory and Applications
Sampling Surveys (LAB COURSE)
Introduction to Time Series (LAB COURSE)
Modern Statistical Prediction and Machine Learning (LAB COURSE)
Game Theory
Seminar on Topics in Probability and Statistics
The Design and Analysis of Experiments (LAB COURSE)
Reproducible and Collaborative Statistical Data Science (LAB COURSE)

College Requirements

Undergraduate students in the College of Letters & Science must fulfill the following requirements in addition to those required by their major program.

For detailed lists of courses that fulfill college requirements, please review the College of Letters & Sciences page in this Guide.

Entry Level Writing

All students who will enter the University of California as freshmen must demonstrate their command of the English language by fulfilling the Entry Level Writing requirement. Fulfillment of this requirement is also a prerequisite to enrollment in all reading and composition courses at UC Berkeley. 

American History and American Institutions

The American History and Institutions requirements are based on the principle that a US resident graduated from an American university should have an understanding of the history and governmental institutions of the United States.

American Cultures

American Cultures is the one requirement that all undergraduate students at Cal need to take and pass in order to graduate. The requirement offers an exciting intellectual environment centered on the study of race, ethnicity and culture of the United States. AC courses offer students opportunities to be part of research-led, highly accomplished teaching environments, grappling with the complexity of American Culture.

Quantitative Reasoning

The Quantitative Reasoning requirement is designed to ensure that students graduate with basic understanding and competency in math, statistics, or computer science. The requirement may be satisfied by exam or by taking an approved course.

Foreign Language

The Foreign Language requirement may be satisfied by demonstrating proficiency in reading comprehension, writing, and conversation in a foreign language equivalent to the second semester college level, either by passing an exam or by completing approved course work.

Reading and Composition

In order to provide a solid foundation in reading, writing and critical thinking the College requires two semesters of lower division work in composition in sequence. Students must complete a first-level reading and composition course by the end of their second semester and a second-level course by the end of their fourth semester.

Breadth Requirements

The undergraduate breadth requirements provide Berkeley students with a rich and varied educational experience outside of their major program. As the foundation of a liberal arts education, breadth courses give students a view into the intellectual life of the University while introducing them to a multitude of perspectives and approaches to research and scholarship. Engaging students in new disciplines and with peers from other majors, the breadth experience strengthens interdisciplinary connections and context that prepares Berkeley graduates to understand and solve the complex issues of their day.

Unit Requirements

  • 120 total units, including at least 60 L&S units

  • Of the 120 units, 36 must be upper division units

  • Of the 36 upper division units, 6 must be taken in courses offered outside your major department

Residence Requirements

For units to be considered in "residence," you must be registered in courses on the Berkeley campus as a student in the College of Letters & Science. Most students automatically fulfill the residence requirement by attending classes here for four years. In general, there is no need to be concerned about this requirement, unless you go abroad for a semester or year or want to take courses at another institution or through UC Extension during your senior year. In these cases, you should make an appointment to meet an adviser to determine how you can meet the Senior Residence Requirement.

Note: Courses taken through UC Extension do not count toward residence.

Senior Residence Requirement

After you become a senior (with 90 semester units earned toward your BA degree), you must complete at least 24 of the remaining 30 units in residence in at least two semesters. To count as residence, a semester must consist of at least 6 passed units. Intercampus Visitor, EAP, and UC Berkeley-Washington Program (UCDC) units are excluded.

You may use a Berkeley Summer Session to satisfy one semester of the Senior Residence requirement, provided that you successfully complete 6 units of course work in the Summer Session and that you have been enrolled previously in the college.

Modified Senior Residence Requirement

Participants in the UC Education Abroad Program (EAP) or the UC Berkeley Washington Program (UCDC) may meet a Modified Senior Residence requirement by completing 24 (excluding EAP) of their final 60 semester units in residence. At least 12 of these 24 units must be completed after you have completed 90 units.

Upper Division Residence Requirement

You must complete in residence a minimum of 18 units of upper division courses (excluding EAP units), 12 of which must satisfy the requirements for your major.

Student Learning Goals

Mission

Statisticians help to design data collection plans, analyze data appropriately, and interpret and draw conclusions from those analyses. The central objective of the undergraduate major in Statistics is to equip students with consequently requisite quantitative skills that they can employ and build on in flexible ways.

Learning Goals for the Major

Majors are expected to learn concepts and tools for working with data and have experience in analyzing real data that goes beyond the content of a service course in statistical methods for non-majors. Majors should understand the following:

  1. The fundamentals of probability theory
  2. Statistical reasoning and inferential methods
  3. Statistical computing
  4. Statistical modeling and its limitations 

Skills

Graduates should also have skills in the following:

  1. Description, interpretation, and exploratory analysis of data by graphical and other means
  2. Effective communication

Courses

Statistics

STAT 0PX Preparatory Statistics 1 Unit

Terms offered: Summer 2017 8 Week Session, Summer 2016 10 Week Session, Summer 2016 8 Week Session
This course assists entering Freshman students with basic statistical concepts and problem solving. Designed for students who do not meet the prerequisites for 2. Offered through the Student Learning Center.

Preparatory Statistics: Read More [+]

STAT 2 Introduction to Statistics 4 Units

Terms offered: Fall 2017, Summer 2017 8 Week Session, Spring 2017
Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Binomial distribution. Interval estimation. Some standard significance tests.

Introduction to Statistics: Read More [+]

STAT C8 Foundations of Data Science 4 Units

Terms offered: Fall 2017, Spring 2017, Fall 2016
Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections
, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.
Foundations of Data Science: Read More [+]

STAT 20 Introduction to Probability and Statistics 4 Units

Terms offered: Fall 2017, Summer 2017 8 Week Session, Spring 2017
For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.

Introduction to Probability and Statistics: Read More [+]

STAT 21 Introductory Probability and Statistics for Business 4 Units

Terms offered: Fall 2017, Fall 2016, Fall 2015
Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.

Introductory Probability and Statistics for Business: Read More [+]

STAT W21 Introductory Probability and Statistics for Business 4 Units

Terms offered: Summer 2017 8 Week Session, Spring 2017, Summer 2016 8 Week Session
Reasoning and fallacies, descriptive statistics, probability models and related concepts, combinatorics, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.

Introductory Probability and Statistics for Business: Read More [+]

STAT 24 Freshman Seminars 1 Unit

Terms offered: Fall 2016, Fall 2003, Spring 2001
The Berkeley Seminar Program has been designed to provide new students with the opportunity to explore an intellectual topic with a faculty member in a small-seminar setting. Berkeley seminars are offered in all campus departments, and topics vary from department to department and semester to semester. Enrollment limited to 15 freshmen.

Freshman Seminars: Read More [+]

STAT 28 Statistical Methods for Data Science 4 Units

Terms offered: Spring 2017
This is a lower-division course that is a follow-up to STAT8/CS8 (Foundations of Data Science). The course will teach a broad range of statistical methods that are used to solve data problems. Topics will include group comparisons and ANOVA, standard parametric statistical models, multivariate data visualization, multiple linear regression and classification, classification and regression trees and random forests. An important focus of the course will be on statistical
computing and reproducible statistical analysis. The students will be introduced to the widely used R statistical language and they will obtain hands-on experience in implementing a range of commonly used statistical methods on numerous real world datasets.
Statistical Methods for Data Science: Read More [+]

STAT 39D Freshman/Sophomore Seminar 2 - 4 Units

Terms offered: Fall 2009, Fall 2008, Fall 2007
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester.

Freshman/Sophomore Seminar: Read More [+]

STAT C79 Societal Risks and the Law 3 Units

Terms offered: Spring 2013
Defining, perceiving, quantifying and measuring risk; identifying risks and estimating their importance; determining whether laws and regulations can protect us from these risks; examining how well existing laws work and how they could be improved; evaluting costs and benefits. Applications may vary by term. This course cannot be used to complete engineering unit or technical elective requirements for students in the College of Engineering.

Societal Risks and the Law: Read More [+]

STAT 88 Probability and Mathematical Statistics in Data Science 2 Units

Terms offered: Fall 2017, Spring 2017, Fall 2016
In this connector course we will state precisely and prove results discovered in the foundational data science course through working with data. Topics include: total variation distance between discrete distributions; the mean, standard deviation, and tail bounds; correlation, and the derivation of the regression equation; probabilities, random variables, and the Central Limit Theorem; probabilistic models; symmetries in random permutations;
prior and posterior distributions, and Bayes’ rule.
Probability and Mathematical Statistics in Data Science: Read More [+]

STAT 89A Introduction to Matrices and Graphs in Data Science 2 Units

Terms offered: Spring 2017, Spring 2016
This connector will cover introductory topics in the mathematics of data science, focusing on discrete probability and linear algebra and the connections between them that are useful in modern theory and practice. We will focus on matrices and graphs as popular mathematical structures with which to model data. For examples, as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties
of social network data, etc.
Introduction to Matrices and Graphs in Data Science: Read More [+]

STAT 94 Special Topics in Probability and Statistics 1 - 4 Units

Terms offered: Spring 2016, Fall 2015
Topics will vary semester to semester.

Special Topics in Probability and Statistics: Read More [+]

STAT 97 Field Study in Statistics 1 - 3 Units

Terms offered: Fall 2017, Summer 2017 8 Week Session, Summer 2017 Second 6 Week Session
Supervised experience relevant to specific aspects of statistics in off-campus settings. Individual and/or group meetings with faculty.

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STAT 98 Directed Group Study 1 - 3 Units

Terms offered: Summer 2017 8 Week Session, Spring 2017, Summer 2016 8 Week Session
Must be taken at the same time as either Statistics 2 or 21. This course assists lower division statistics students with structured problem solving, interpretation and making conclusions.

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STAT C100 Principles & Techniques of Data Science 4 Units

Terms offered: Fall 2017, Spring 2017
In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction​, and decision-making.​ This class will focus on quantitative critical thinking​ and key principles and techniques needed to carry out this cycle. These include languages for transforming, querying and analyzing data; algorithms for machine learning
methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.
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STAT 131A Introduction to Probability and Statistics for Life Scientists 4 Units

Terms offered: Fall 2017, Spring 2017, Fall 2016
Ideas for estimation and hypothesis testing basic to applications, including an introduction to probability. Linear estimation and normal regression theory.

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STAT 132 Practical Machine Learning 3 Units

Terms offered: Summer 2012 8 Week Session, Summer 2011 10 Week Session, Summer 2011 8 Week Session
Machine learning is a collection of topics in which the focus is on large-scale statistical problems where computational issues are paramount. The goal is often one of prediction or classification, where based on a set of labeled data it is desired to predict the lablels of unlabeled data. Machine learning algorithms also often focus on exploratory data analysis. This course will introduce core
statistical machine learning algorithms in a non-mathematical way, emphasizing applied problem-solving.
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STAT 133 Concepts in Computing with Data 3 Units

Terms offered: Fall 2017, Summer 2017 10 Week Session, Spring 2017
An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results.

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STAT 134 Concepts of Probability 3 Units

Terms offered: Fall 2017, Summer 2017 8 Week Session, Spring 2017
An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.

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STAT 135 Concepts of Statistics 4 Units

Terms offered: Fall 2017, Summer 2017 8 Week Session, Spring 2017
A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, non-parametric methods, introduction to optimality, goodness-of-fit tests, analysis of variance, bootstrap and computer-intensive methods and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering.

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STAT 140 Probability for Data Science 4 Units

Terms offered: Spring 2017
An introduction to probability, emphasizing the combined use of mathematics and programming to solve problems. Random variables, discrete and continuous families of distributions. Bounds and approximations. Dependence, conditioning, Bayes methods. Convergence, Markov chains. Least squares prediction. Random permutations, symmetry, order statistics. Use of numerical computation, graphics, simulation, and computer algebra.

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STAT 150 Stochastic Processes 3 Units

Terms offered: Fall 2017, Fall 2016, Spring 2016
Random walks, discrete time Markov chains, Poisson processes. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes.

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STAT 151A Linear Modelling: Theory and Applications 4 Units

Terms offered: Fall 2017, Spring 2017, Fall 2016
A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.

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STAT 151B Linear Modelling: Theory and Applications 4 Units

Terms offered: Spring 2013, Spring 2012, Spring 2011
A coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.

Linear Modelling: Theory and Applications: Read More [+]

STAT 152 Sampling Surveys 4 Units

Terms offered: Spring 2017, Spring 2016, Spring 2015
Theory and practice of sampling from finite populations. Simple random, stratified, cluster, and double sampling. Sampling with unequal probabilities. Properties of various estimators including ratio, regression, and difference estimators. Error estimation for complex samples.

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STAT 153 Introduction to Time Series 4 Units

Terms offered: Fall 2017, Spring 2017, Fall 2016
An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.

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STAT 154 Modern Statistical Prediction and Machine Learning 4 Units

Terms offered: Fall 2017, Spring 2017, Fall 2016
Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience
with real data and assessing statistical assumptions.
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STAT 155 Game Theory 3 Units

Terms offered: Fall 2017, Summer 2017 8 Week Session, Spring 2017
General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples.

Game Theory: Read More [+]

STAT 157 Seminar on Topics in Probability and Statistics 3 Units

Terms offered: Fall 2016, Spring 2016, Fall 2015
Substantial student participation required. The topics to be covered each semester that the course may be offered will be announced by the middle of the preceding semester; see departmental bulletins. Recent topics include: Bayesian statistics, statistics and finance, random matrix theory, high-dimensional statistics.

Seminar on Topics in Probability and Statistics: Read More [+]

STAT 158 The Design and Analysis of Experiments 4 Units

Terms offered: Fall 2017, Spring 2016, Spring 2015
An introduction to the design and analysis of experiments. This course covers planning, conducting, and analyzing statistically designed experiments with an emphasis on hands-on experience. Standard designs studied include factorial designs, block designs, latin square designs, and repeated measures designs. Other topics covered include the principles of design, randomization, ANOVA, response surface methodoloy, and computer experiments.

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STAT 159 Reproducible and Collaborative Statistical Data Science 4 Units

Terms offered: Fall 2017, Fall 2016, Fall 2015
A project-based introduction to statistical data analysis. Through case studies, computer laboratories, and a term project, students will learn practical techniques and tools for producing statistically sound and appropriate, reproducible, and verifiable computational answers to scientific questions. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git,
Python, and LaTeX.
Reproducible and Collaborative Statistical Data Science: Read More [+]

STAT H195 Special Study for Honors Candidates 1 - 4 Units

Terms offered: Fall 2017, Summer 2017 First 6 Week Session, Spring 2017

Special Study for Honors Candidates: Read More [+]

STAT 197 Field Study in Statistics 1 - 3 Units

Terms offered: Fall 2017, Summer 2017 8 Week Session, Summer 2017 First 6 Week Session
Supervised experience relevant to specific aspects of statistics in off-campus settings. Individual and/or group meetings with faculty.

Field Study in Statistics: Read More [+]

STAT 198 Directed Study for Undergraduates 1 - 3 Units

Terms offered: Fall 2017, Summer 2017 8 Week Session, Spring 2017
Special tutorial or seminar on selected topics.

Directed Study for Undergraduates: Read More [+]

STAT 199 Supervised Independent Study and Research 1 - 3 Units

Terms offered: Fall 2017, Summer 2017 10 Week Session, Summer 2017 8 Week Session

Supervised Independent Study and Research: Read More [+]

Faculty and Instructors

+ Indicates this faculty member is the recipient of the Distinguished Teaching Award.

Faculty

David Aldous, Professor. Mathematical probability, applied probability, analysis of algorithms, phylogenetic trees, complex networks, random networks, entropy, spatial networks.
Research Profile

Peter L. Bartlett, Professor. Statistics, machine learning, statistical learning theory, adaptive control.
Research Profile

David R. Brillinger, Professor. Risk analysis, statistical methods, data analysis, animal and fish motion trajectories, statistical applications in engineering and science, sports statistics.
Research Profile

James Bentley Brown, Assistant Adjunct Professor.

Joan Bruna Estrach, Assistant Professor.
Research Profile

Peng Ding, Assistant Professor.
Research Profile

Sandrine Dudoit, Professor. Genomics, classification, statistical computing, biostatistics, cross-validation, density estimation, genetic mapping, high-throughput sequencing, loss-based estimation, microarray, model selection, multiple hypothesis testing, prediction, RNA-Seq.
Research Profile

Noureddine El Karoui, Associate Professor. Applied statistics, theory and applications of random matrices, large dimensional covariance estimation and properties of covariance matrices, connections with mathematical finance.
Research Profile

Steven N. Evans, Professor. Genetics, random matrices, superprocesses & other measure-valued processes, probability on algebraic structures -particularly local fields, applications of stochastic processes to biodemography, mathematical finance, phylogenetics & historical linguistics.
Research Profile

Will Fithian, Assistant Professor.
Research Profile

Lisa Goldberg, Adjunct Professor.

Leo Goodman, Professor. Sociology, statistics, log-linear models, correspondence analysis models, mathematical demography, categorical data analysis, survey data analysis, logit models, log-bilinear models, association models.
Research Profile

Adityanand Guntuboyina, Assistant Professor.

Alan Hammond, Associate Professor.

Haiyan Huang, Associate Professor. Applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification.
Research Profile

Nicholas P. Jewell, Professor. AIDS, statistics, epidemiology, infectious diseases, Ebola Virus Disease, SARS, H1N1 influenza, adverse cardiovascular effects of pharmaceuticals, counting civilian casualties during conflicts.
Research Profile

Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization.
Research Profile

Michael J. Klass, Professor. Statistics, mathematics, probability theory, combinatorics independent random variables, iterated logarithm, tail probabilities, functions of sums.
Research Profile

Michael William Mahoney, Associate Adjunct Professor.

Jon Mcauliffe, Associate Adjunct Professor. Bioinformatics, machine learning, nonparametrics, convex optimization, statistical computing, prediction, supervised learning.
Research Profile

Elchanan Mossel, Professor. Applied probability, statistics, mathematics, finite markov chains, markov random fields, phlylogeny.
Research Profile

Rasmus Nielsen, Professor. Statistical and computational aspects of evolutionary theory and genetics.
Research Profile

+ Deborah Nolan, Professor. Statistics, empirical process, high-dimensional modeling, technology in education.
Research Profile

James W. Pitman, Professor. Fragmentation, statistics, mathematics, Brownian motion, distribution theory, path transformations, stochastic processes, local time, excursions, random trees, random partitions, processes of coalescence.
Research Profile

Elizabeth Purdom, Assistant Professor. Computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics.
Research Profile

Benjamin Recht, Associate Professor.

Jasjeet S. Sekhon, Professor. Program evaluation, statistical and computational methods, causal inference, elections, public opinion, American politics .

Alistair Sinclair, Professor. Algorithms, applied probability, statistics, random walks, Markov chains, computational applications of randomness, Markov chain Monte Carlo, statistical physics, combinatorial optimization.
Research Profile

Allan M. Sly, Associate Professor.
Research Profile

Yun Song, Associate Professor. Computational biology, population genomics, applied probability and statistics.
Research Profile

Philip B. Stark, Professor. Astrophysics, law, statistics, litigation, causal inference, inverse problems, geophysics, elections, uncertainty quantification, educational technology.
Research Profile

Bernd Sturmfels, Professor. Mathematics, combinatorics, computational algebraic geometry.
Research Profile

Nike Sun, Assistant Professor.
Research Profile

Mark J. Van Der Laan, Professor. Statistics, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies.
Research Profile

Martin Wainwright, Professor. Statistical machine learning, High-dimensional statistics, information theory, Optimization and algorithmss.
Research Profile

Bin Yu, Professor. Neuroscience, remote sensing, networks, statistical machine learning, high-dimensional inference, massive data problems, document summarization.
Research Profile

Lecturers

+ Ani Adhikari, Senior Lecturer SOE.

Fletcher H. Ibser, Lecturer.

Adam R. Lucas, Lecturer.

Christopher Paciorek, Lecturer.

Nusrat Rabbee, Lecturer.

Gaston Sanchez Trujillo, Lecturer.

Shobhana Stoyanov, Lecturer.

Visiting Faculty

Hermann Helmut Pitters, Visiting Assistant Professor.

Yuekai Sun, Visiting Assistant Professor.

Emeritus Faculty

Peter J. Bickel, Professor Emeritus. Statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology.
Research Profile

Ching-Shui Cheng, Professor Emeritus. Statistics, statistical design of experiments, combinatorial problems, efficient experimental design.
Research Profile

Kjell A. Doksum, Professor Emeritus. Statistics, curve estimation, nonparametric regression, correlation curves, survival analysis, semiparametric, nonparametric settings, regression quantiles, analysis of financial data.
Research Profile

Pressley W. Millar, Professor Emeritus. Statistics, Martingales, Markov processes, Gaussian processes, excursion theory, asymptotic statistical decision theory, nonparametrics, robustness, stochastic procedures, asymptotic minimas theory, bootstrap theory.
Research Profile

Roger A. Purves, Professor Emeritus. Statistics, foundations of probability, measurability.
Research Profile

John A. Rice, Professor Emeritus. Transportation, astronomy, statistics, functional data analysis, time series analysis.
Research Profile

Terence P. Speed, Professor Emeritus. Genomics, statistics, genetics and molecular biology, protein sequences.
Research Profile

Charles J. Stone, Professor Emeritus. Statistical modeling with splines, statistical education.
Research Profile

Kenneth Wachter, Professor Emeritus. Mathematical demography stochastic models, simulation, biodemography, federal statistical system.
Research Profile

Contact Information

Department of Statistics

367 Evans Hall

Phone: 510-642-2781

Fax: 510-642-7892

Visit Department Website

Department Chair

Michael I. Jordan, PhD

427 Evans Hall

chair@stat.berkeley.edu

Undergraduate Student Services Adviser & Course and Curriculum Officer

Denise Yee

367 Evans Hall

Phone: 510-643-6131

dyee@berkeley.edu

Undergraduate Student Services Adviser

Majabeen Samadi

367 Evans Hall

Phone: 510-643-2459

majabeen@berkeley.edu

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