Uc berkeley statistics certificate programs

Effects of departures from the underlying assumptions. Robust alternatives to least squares. Spring Semester Total Units: The program is for full-time students and is designed to be completed in two semesters fall and spring. In order to obtain the M. In the first semester, all students will take intensive graduate courses in probability, theoretical statistics, and statistical computing; the typical courses are STAT A, B, and In the second semester, students will take an advanced course in modern applied statistics STAT , an elective, and a capstone course.

The capstone will consist of a team-based learning experience that will give students the opportunity to work on a real-world problem and carry out a substantial data analysis project. It will culminate with a written report and an oral presentation of findings. For a complete list of courses offered by the department and course descriptions, please visit the academic guide.

All coursework used for the M. Elective courses are chosen with the guidance and approval of the MA program Chair. Generally, the elective must be a graduate level course related to statistics.

Such courses can be within the Statistics Department or from other departments. Some examples of past electives:. If an elective that you would like to take is not on the list the course can be submitted for department approval. You can submit your request using the Google Form here. In extremely rare cases, a thesis option may be considered by the MA Chair. Typically, this will be when either the option has been offered to the student at the time of admission, or if the student arrives with substantial progress in research in an area of interest to our faculty.

If approved by the MA Chair for the thesis option you will not have to take the comprehensive exam. If approved for the thesis option, you must find three faculty to be on your thesis committee. Though not required, it is strongly encouraged that one of the faculty be from outside the Statistics Department. Both you and the thesis committee chair must agree on the topic of your thesis.

Please provide a short description of your thesis topic, the names of your committee members and the signature of your committee chair on the Worksheet for the M. The MA program includes students who are admitted directly into the department and students obtaining advanced degrees in other departments at Berkeley. Coursework consists of intensive graduate courses in probability, theoretical statistics, and statistical computing as well as an advanced course in modern applied statistics and a capstone course.

Students will have the option to take elective courses. Some students do take additional courses, including courses in other departments, depending on their background and level of preparation. Other professional graduate programs on campus all have their own policies for enrollment in their courses.

After appropriate consultation, students will need to check these policies before registering for such courses. There is no transfer arrangement into the PhD program. To gain acceptance into the PhD program, you must apply along with all other applicants, and you will be considered in the same way as other applicants. All coursework for the M. The Graduate Certificate in Global Urban Humanites provides a framework for the study of cities and urban life that brings together approaches from many disciplines.

Participation in the program introduces you to an active community of scholars, urban practitioners and artists engaged in developing new methods of research and teaching. The certificate is intended to provide you with the opportunity to find new ways of thinking about cities by exploring methods and theories from outside your home discipline.

Forms and instructions for admission are available on the Global Urban Humanites site. The Graduate Certificate in Geographic Information Science and Technology GIST provides an academic structure for an interdisciplinary exchange of ideas around geospatial information and analysis.

Geographic information science and technology, along with geostatistics and analysis of satellite imagery, have emerged as major cross-disciplinary tools used in academic research, industrial applications, and public policy analysis. The GIST enables specialized, multidisciplinary training and research opportunities in various emerging areas of geospatial information science and technology.

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

Avi Feller, Assistant Professor. Applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences. Will Fithian, Assistant Professor. Theoretical and Applied Statistics. Shirshendu Ganguly, Assistant Professor.

Probability theory, statistical mechanics. Adityanand Guntuboyina, Associate Professor. Nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information theory. Alan Hammond, Professor. Statistical mechanics. Haiyan Huang, Professor. Jiantao Jiao, Assistant Professor. Artificial intelligence, control and intelligent systems and robotics, communications and networking.

Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization. Jon Mcauliffe, Adjunct Professor. Bioinformatics, machine learning, nonparametrics, convex optimization, statistical computing, prediction, supervised learning. Song Mei, Assistant Professor. Data science, statistics, machine learning.

Rasmus Nielsen, Professor. Statistical and computational aspects of evolutionary theory and genetics. Statistics, empirical process, high-dimensional modeling, technology in education.

Fernando Perez, Associate Professor. High-level languages, interactive and literate computing, and reproducible research. Sam Pimentel, Assistant Professor.

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. Elizabeth Purdom, Associate Professor. Computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics. 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.

Yun Song, Professor. Computational biology, population genomics, applied probability and statistics. Philip B. Stark, Professor. Astrophysics, law, statistics, litigation, causal inference, inverse problems, geophysics, elections, uncertainty quantification, educational technology. Jacob Steinhardt, Assistant Professor. Artificial intelligence, machine learning. Bernd Sturmfels, Professor. Mathematics, combinatorics, computational algebraic geometry.

Mark J. Van Der Laan, Professor. Statistics, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies. Martin Wainwright, Professor. Statistical machine learning, High-dimensional statistics, information theory, Optimization and algorithmss.

Bin Yu, Professor. Neuroscience, remote sensing, networks, statistical machine learning, high-dimensional inference, massive data problems, document summarization. Mathematical probability, applied probability, analysis of algorithms, phylogenetic trees, complex networks, random networks, entropy, spatial networks.

Peter J. Statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology. David R. Risk analysis, statistical methods, data analysis, animal and fish motion trajectories, statistical applications in engineering and science, sports statistics.

Ching-Shui Cheng, Professor Emeritus. Statistics, statistical design of experiments, combinatorial problems, efficient experimental design. Kjell A. Doksum, Professor Emeritus. Statistics, curve estimation, nonparametric regression, correlation curves, survival analysis, semiparametric, nonparametric settings, regression quantiles, analysis of financial data. Nicholas P. Michael J. Klass, Professor Emeritus. Statistics, mathematics, probability theory, combinatorics independent random variables, iterated logarithm, tail probabilities, functions of sums.

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. Roger A. Purves, Senior Lecturer Emeritus. Statistics, foundations of probability, measurability. John A. Rice, Professor Emeritus. Transportation, astronomy, statistics, functional data analysis, time series analysis.

Terence P. Speed, Professor Emeritus. Genomics, statistics, genetics and molecular biology, protein sequences. Kenneth Wachter, Professor Emeritus. Mathematical demography stochastic models, simulation, biodemography, federal statistical system. When you print this page, you are actually printing everything within the tabs on the page you are on: this may include all the Related Courses and Faculty, in addition to the Requirements or Overview.

If you just want to print information on specific tabs, you're better off downloading a PDF of the page, opening it, and then selecting the pages you really want to print. Home Undergraduate Degree Programs Statistics. About the Program Bachelor of Arts BA The undergraduate major at Berkeley provides a systematic and thorough grounding in applied and theoretical statistics as well as probability.

Declaring the Major Students should apply in the semester they will complete their prerequisites. Minor Program The minor is for students who want to study a significant amount of statistics and probability at the upper division level. 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. Other exceptions to this requirement are noted as applicable.

A minimum grade point average GPA of 2. The requirements below apply to freshmen entering Berkeley in Fall , and transfer students entering in Fall Freshmen students admitted to Berkeley prior to Fall and transfer students admitted prior to Fall are required to complete the requirements as published in the Berkeley Academic Guide.

Minor Requirements Students who have a strong interest in an area of study outside their major often decide to complete a minor program. To declare a minor, contact the department advisor for information on requirements, and the declaration process.

All courses taken to fulfill the minor requirements below must be taken for graded credit. A minimum of three of the upper division courses taken to fulfill the minor requirements must be completed at UC Berkeley. No more than one upper division course may be used to simultaneously fulfill requirements for a student's major and minor programs.

All minor requirements must be completed prior to the last day of finals during the semester in which the student plans to graduate. All minor requirements must be completed within the unit ceiling. For further information regarding the unit ceiling, please see the College Requirements tab. College Requirements Undergraduate students must fulfill the following requirements in addition to those required by their major program. University of California Requirements 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.

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.

Berkeley Campus Requirement American Cultures All undergraduate students at Cal need to take and pass this course in order to graduate. 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 Composit ion 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. 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.

Upper Division Residence Requirement You must complete in residence a minimum of 18 units of upper division courses excluding UCEAP 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. 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: The fundamentals of probability theory Statistical reasoning and inferential methods Statistical computing Statistical modeling and its limitations Skills Graduates should also have skills in the following: Description, interpretation, and exploratory analysis of data by graphical and other means Effective communication. Major Map Major Maps help undergraduate students discover academic, co-curricular, and discovery opportunities at UC Berkeley based on intended major or field of interest.

Developed by the Division of Undergraduate Education in collaboration with academic departments, these experience maps will help you: Explore your major and gain a better understanding of your field of study Connect with people and programs that inspire and sustain your creativity, drive, curiosity and success Discover opportunities for independent inquiry, enterprise, and creative expression Engage locally and globally to broaden your perspectives and change the world Reflect on your academic career and prepare for life after Berkeley Use the major map below as a guide to planning your undergraduate journey and designing your own unique Berkeley experience.

Instructor: Purves. STAT 21 Introductory Probability and Statistics for Business 4 Units Terms offered: Summer 8 Week Session, Fall , Fall Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. STAT W21 Introductory Probability and Statistics for Business 4 Units Terms offered: Summer 8 Week Session, Summer 8 Week Session, Summer 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.

Formerly known as: N STAT 24 Freshman Seminars 1 Unit Terms offered: Spring , Fall , Fall 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.

STAT 33B Introduction to Advanced Programming in R 1 Unit Terms offered: Spring , Fall , Spring The course is designed primarily for those who are already familiar with programming in another language, such as python, and want to understand how R works, and for those who already know the basics of R programming and want to gain a more in-depth understanding of the language in order to improve their coding.

STAT C79 Societal Risks and the Law 3 Units Terms offered: Spring 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. STAT C Data, Inference, and Decisions 4 Units Terms offered: Spring , Fall , Spring This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications.

STAT Game Theory 3 Units Terms offered: Spring , Summer 8 Week Session, Spring General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples. Prerequisites might vary with instructor and topics Repeat rules: Course may be repeated for credit with instructor consent. Alternative to final exam.

Repeat rules: Course may be repeated for credit without restriction. Faculty Peter L. Research Profile Sandrine Dudoit, Professor. Research Profile Steven N. Research Profile Alan Hammond, Professor. Research Profile Haiyan Huang, Professor. Research Profile Michael I. Research Profile Rasmus Nielsen, Professor. Research Profile James W.

Research Profile Jasjeet S. Research Profile Yun Song, Professor. Research Profile Philip B. Research Profile Bernd Sturmfels, Professor. Research Profile Mark J. Research Profile Martin Wainwright, Professor. Research Profile Bin Yu, Professor. Thomas Bengtsson, Lecturer. Jared Fisher, Lecturer, Postdoctoral Scholar. Fletcher H. Ibser, Lecturer. Cari Kaufman, Lecturer. Adam R. Lucas, Lecturer. Libor Pospisil, Lecturer.

Swupnil Sahai, Lecturer. Gaston Sanchez Trujillo, Lecturer. Shobhana Murali Stoyanov, Lecturer. Brett Kolesnik, Visiting Assistant Professor. Research Profile Peter J.