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:

• Minor application form
• Copy of their transcript (an unofficial one will do)
• Petition for Confirmation of Minor Program completed

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)

Upper Division Requirements (Nine Courses)

Upper Division Requirements: Teaching Option (Nine Courses)

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.

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

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

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.
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Peter L. Bartlett, Professor. Statistics, machine learning, statistical learning theory, adaptive control.
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David R. Brillinger, Professor. Risk analysis, statistical methods, data analysis, animal and fish motion trajectories, statistical applications in engineering and science, sports statistics.
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James Bentley Brown, Assistant Adjunct Professor.

Joan Bruna Estrach, Assistant Professor.
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Peng Ding, Assistant Professor.
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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.
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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.
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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.
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Will Fithian, Assistant Professor.
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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.
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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.
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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.
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Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization.
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Michael J. Klass, Professor. Statistics, mathematics, probability theory, combinatorics independent random variables, iterated logarithm, tail probabilities, functions of sums.
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Michael William Mahoney, Associate Adjunct Professor.

Jon Mcauliffe, Associate Adjunct Professor. Bioinformatics, machine learning, nonparametrics, convex optimization, statistical computing, prediction, supervised learning.
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Elchanan Mossel, Professor. Applied probability, statistics, mathematics, finite markov chains, markov random fields, phlylogeny.
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Rasmus Nielsen, Professor. Statistical and computational aspects of evolutionary theory and genetics.
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+ Deborah Nolan, Professor. Statistics, empirical process, high-dimensional modeling, technology in education.
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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.
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Elizabeth Purdom, Assistant Professor. Computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics.
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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.
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Allan M. Sly, Associate Professor.
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Yun Song, Associate Professor. Computational biology, population genomics, applied probability and statistics.
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Philip B. Stark, Professor. Astrophysics, law, statistics, litigation, causal inference, inverse problems, geophysics, elections, uncertainty quantification, educational technology.
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Bernd Sturmfels, Professor. Mathematics, combinatorics, computational algebraic geometry.
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Nike Sun, Assistant Professor.
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Mark J. Van Der Laan, Professor. Statistics, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies.
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Martin Wainwright, Professor. Statistical machine learning, High-dimensional statistics, information theory, Optimization and algorithmss.
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Bin Yu, Professor. Neuroscience, remote sensing, networks, statistical machine learning, high-dimensional inference, massive data problems, document summarization.
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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.
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Ching-Shui Cheng, Professor Emeritus. Statistics, statistical design of experiments, combinatorial problems, efficient experimental design.
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Kjell A. Doksum, Professor Emeritus. Statistics, curve estimation, nonparametric regression, correlation curves, survival analysis, semiparametric, nonparametric settings, regression quantiles, analysis of financial data.
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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.
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Roger A. Purves, Professor Emeritus. Statistics, foundations of probability, measurability.
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John A. Rice, Professor Emeritus. Transportation, astronomy, statistics, functional data analysis, time series analysis.
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Terence P. Speed, Professor Emeritus. Genomics, statistics, genetics and molecular biology, protein sequences.
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Charles J. Stone, Professor Emeritus. Statistical modeling with splines, statistical education.
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Kenneth Wachter, Professor Emeritus. Mathematical demography stochastic models, simulation, biodemography, federal statistical system.
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