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. 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
Beginning Fall 2024, new students are admitted directly into the Statistics major in the College of Computing, Data Science, and Society (CDSS) and will already be declared. Students in other colleges at UC Berkeley interested in the Statistics major should refer to the Statistics Prospective Majors/Minors page and CDSS Frequently Asked Questions.
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.
For detailed information regarding the process of declaring the minor, please see the Statistics 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
- 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.
- 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.
- 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 residency requirements and unit requirements, please see the College Requirements tab.
Prerequisites
| Code | Title | Units |
|---|---|---|
| Students must earn a minimum 2.0 grade point average in the following prerequisites with no lower than a C in Math 53, Math 54, and Stat 20 / Data C8. | ||
| MATH 51/1A | Calculus I (MATH 51 as of Fall 2025) | 4 |
| MATH 52/1B | Calculus II (MATH 52 as of Fall 2025) | 4 |
| MATH 53 | Multivariable Calculus | 4 |
| or MATH H53 | Honors Multivariable Calculus | |
| or MATH N53 | Multivariable Calculus | |
| or MATH W53 | Multivariable Calculus | |
| MATH 54 | Linear Algebra and Differential Equations | 4 |
| or MATH H54 | Honors Linear Algebra and Differential Equations | |
| or MATH N54 | Linear Algebra and Differential Equations | |
| or MATH 56 | Linear Algebra | |
| STAT 20 | Introduction to Probability and Statistics | 4 |
| or DATA C8 | Foundations of Data Science | |
Upper Division Requirements (Nine Courses)
| Code | Title | Units |
|---|---|---|
| Core Statistics Courses (3) | ||
| STAT 133 | Concepts in Computing with Data | 3 |
| or DATA C100 AND STAT 33B | Course Not Available | |
| STAT 134 | Concepts of Probability 1 | 4 |
| or DATA C140 | Probability for Data Science | |
| or EECS 126 | Probability and Random Processes | |
| or MATH 106 | Mathematical Probability Theory | |
| STAT 135 | Concepts of Statistics | 4 |
| Statistics Electives (3) | ||
| Select three statistics electives from the following; at least one of the selections must have a lab: | 10-12 | |
| DATA C102 | Data, Inference, and Decisions (LAB COURSE) | 4 |
| STAT 150 | Stochastic Processes | 3 |
| STAT 151A | Linear Modelling: Theory and Applications (LAB COURSE) | 4 |
| STAT 152 | Sampling Surveys (LAB COURSE) | 4 |
| STAT 153 | Introduction to Time Series (LAB COURSE) | 4 |
| STAT 154 | Modern Statistical Prediction and Machine Learning (LAB COURSE) 2 | 4 |
| STAT 155 | Game Theory 6 | 3 |
| STAT 156 | Causal Inference (LAB COURSE) | 4 |
| STAT 157 | Seminar on Topics in Probability and Statistics | 3 |
| STAT 158 | Experimental Design (LAB COURSE) | 4 |
| STAT 159 | Reproducible and Collaborative Statistical Data Science (LAB COURSE) | 4 |
| STAT 165 | Forecasting | 3 |
| 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)
| Code | Title | Units |
|---|---|---|
| Core Statistics Courses (3) | ||
| STAT 133 | Concepts in Computing with Data | 3 |
| or DATA C100 AND STAT 33B | Course Not Available | |
| STAT 134 | Concepts of Probability 1 | 4 |
| or DATA C140 | Probability for Data Science | |
| or EECS 126 | Probability and Random Processes | |
| or MATH 106 | Mathematical Probability Theory | |
| STAT 135 | Concepts of Statistics | 4 |
| Statistics Electives (2) | ||
| Select two of the following; at least one course must include a lab: | 7-8 | |
| DATA C102 | Data, Inference, and Decisions (LAB COURSE) | 4 |
| STAT 150 | Stochastic Processes | 3 |
| STAT 151A | Linear Modelling: Theory and Applications (LAB COURSE) | 4 |
| STAT 152 | Sampling Surveys (LAB COURSE) | 4 |
| STAT 153 | Introduction to Time Series (LAB COURSE) | 4 |
| STAT 154 | Modern Statistical Prediction and Machine Learning (LAB COURSE) 2 | 4 |
| STAT 155 | Game Theory 6 | 3 |
| STAT 156 | Causal Inference (LAB COURSE) | 4 |
| STAT 157 | Seminar on Topics in Probability and Statistics | 3 |
| STAT 158 | Experimental Design (LAB COURSE) | 4 |
| STAT 159 | Reproducible and Collaborative Statistical Data Science (LAB COURSE) | 4 |
| STAT 165 | Forecasting | 3 |
| Teaching Track Cluster (4) | ||
| MATH 110 | Abstract Linear Algebra | 4 |
| MATH 113 | Introduction to Abstract Algebra | 4 |
| MATH 151 | Mathematics of the Secondary School Curriculum I | 4 |
| MATH 152 | Mathematics of the Secondary School Curriculum II | 4 |
| or MATH 153 | Course Not Available | |
Cluster Course Information
Two of the best reasons to study statistics are the immense variety of important and exciting real-world questions we can answer through careful data analysis, as well as the broad range of technical fields with close connections to statistics. No major is complete without encountering the fields that interface closely with statistics.
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 your own Cluster. The courses should have a unifying theme. Picking your own Cluster is a valuable exercise that gives you a chance to explore and refine your interests and to develop a coherent course of study. A pre-approved list has been provided. However, it is not exhaustive. Clusters may consist of courses from more than one department, but at least two must be approved courses from the same department. If students would like to use a course that is not on the list or select three courses from three different departments, the Head Undergraduate Faculty Adviser must approve the proposed cluster.
Economics and Business courses are treated as though they are in the same department for purposes of evaluating clusters. The same is true for courses in EE, CS, and EECS. Likewise, courses concerning social and ethical aspects of statistics including CYPLAN 101, INFO 188, PHILOS 121, and DATA C104 / HISTORY C184D / STS C1040 are treated as though they are in the same department even if offered in different departments.
Cluster Guidelines
Courses must be: upper division courses, at least 3 units, and must be taken for a letter grade.
Courses with statistics prerequisites are often acceptable. Courses that are similar to Statistics courses are not acceptable. If an approved cluster course has a credit restriction with another approved course, both cannot be used for the applied cluster (refer to the Berkeley Academic Guide for credit restrictions, click on “read more” under the course description).
Content Criteria:
Generally, to be an acceptable cluster course, a course should meet at least one of the following three criteria:
-
The course centers on questions about ethical data analysis or experimental methodology.
-
The course is focused on a substantive area of natural sciences or social sciences, and includes a significant quantitative or data analysis component as part of the course requirements.
-
The course is in a related technical field like mathematics, computer science, engineering, or operations research.
Sample Clusters
Below is a list of sample clusters for students to consider if they would like an idea of courses to combine for their cluster based on a topic of interest.
- Economics and Finance: Econ 101A, Econ 101B and UGBA 103
- Math: Math 110, Math 104, and Math 128B
- Public Health/Biostatistics graduate school: Math 110, Pb Hlth 150A, and Pb Hlth 150B
- Public Policy: Pub Policy C103, Pub Pol C142/Econ C142/Pol Sci C131A, and Pb Hlth 126
- Social and Ethical Aspects of Statistics: CYPLAN 101, INFO 188, and DATA C104 / HISTORY C184D / STS C1040
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.
| Code | Title | Units |
|---|---|---|
| ANTHRO 115 | Introduction to Medical Anthropology | 4 |
| ANTHRO 121C | Historical Archaeology: Historical Artifact Identification and Analysis | 4 |
| ANTHRO 127A | Bioarchaeology: Introduction to Skeletal Biology and Bioarchaeology | 4 |
| ANTHRO 127B | Bioarchaeology: Reconstruction of Life in Bioarchaeology | 4 |
| ANTHRO C129D/INTEGBI C155 | Course Not Available | 3 |
| ANTHRO 132A | Analysis of Archaeological Materials: Ceramics | 4 |
| ANTHRO 135 | Paleoethnobotany: Archaeological Methods and Laboratory Techniques | 4 |
| ANTHRO 169B | Research Theory and Methods in Socio-Cultural Anthropology | 5 |
| ARCH 140 | Energy and Environment | 4 |
| ARCH 150 | Introduction to Structures | 4 |
| ARCH 154 | Design and Computer Analysis of Structure | 3 |
| ASTRON 128 | Astronomy Data Science Laboratory | 4 |
| ASTRON 160 | Stellar Physics | 4 |
| ASTRON C161 | Relativistic Astrophysics and Cosmology | 4 |
| ASTRON C162 | Planetary Astrophysics | 4 |
| BIO ENG 104 | Biological Transport Phenomena | 4 |
| BIO ENG C112 | Molecular Biomechanics and Mechanobiology of the Cell | 4 |
| BIO ENG C117 | Structural Aspects of Biomaterials | 4 |
| BIO ENG C119 | Orthopedic Biomechanics | 4 |
| BIO ENG C125 | Introduction to Robotics | 4 |
| BIO ENG C125B | Robotic Manipulation and Interaction | 4 |
| BIO ENG 131 | Introduction to Computational Molecular and Cell Biology | 4 |
| BIO ENG C136L | Laboratory in the Mechanics of Organisms | 3 |
| BIO ENG C137 | Designing for the Human Body | 4 |
| BIO ENG 144 | Introduction to Protein Informatics | 4 |
| BIO ENG C145L | Introductory Electronic Transducers Laboratory | 3 |
| BIO ENG C145M | Introductory Microcomputer Interfacing Laboratory | 3 |
| BIO ENG 147 | Principles of Synthetic Biology | 4 |
| BIO ENG C165 | Medical Imaging Signals and Systems | 4 |
| BIO ENG C181 | The Berkeley Lectures on Energy: Energy from Biomass | 3 |
| CHM ENG 140 | Introduction to Chemical Process Analysis | 4 |
| CHM ENG 141 | Chemical Engineering Thermodynamics | 4 |
| CHM ENG C195A | The Berkeley Lectures on Energy: Energy from Biomass | 3 |
| CHEM C110L | General Biochemistry and Molecular Biology Laboratory | 4 |
| CHEM 120A | Physical Chemistry | 3 |
| CHEM 120B | Physical Chemistry | 3 |
| CHEM C130 | Biophysical Chemistry: Physical Principles and the Molecules of Life | 4 |
| CHEM C138 | The Berkeley Lectures on Energy: Energy from Biomass | 3 |
| CY PLAN 101 | Introduction to Urban Data Analytics | 4 |
| CY PLAN 118AC | The Urban Community | 4 |
| CY PLAN 119 | Planning for Sustainability | 4 |
| CIV ENG 100 | Elementary Fluid Mechanics | 4 |
| CIV ENG C106 | Air Pollution | 3 |
| CIV ENG C116 | Chemistry of Soils | 3 |
| CIV ENG C133 | Engineering Analysis Using the Finite Element Method | 3 |
| CIV ENG 155 | Transportation Systems Engineering | 3 |
| COG SCI C100 | Basic Issues in Cognition | 3 |
| COG SCI C101 | Cognitive Linguistics | 4 |
| COG SCI C126 | Perception | 3 |
| COG SCI C127 | Cognitive Neuroscience | 3 |
| COG SCI 131 | Computational Models of Cognition | 4 |
| COG SCI C140 | Quantitative Methods in Linguistics | 4 |
| COMPSCI 152 | Computer Architecture and Engineering | 4 |
| COMPSCI 160 | User Interface Design and Development | 4 |
| COMPSCI 161 | Computer Security | 4 |
| COMPSCI 162 | Operating Systems and System Programming | 4 |
| COMPSCI 164 | Programming Languages and Compilers | 4 |
| COMPSCI 168 | Introduction to the Internet: Architecture and Protocols | 4 |
| COMPSCI 169 | Course Not Available | 4 |
| COMPSCI 169A | Introduction to Software Engineering | 4 |
| COMPSCI W169A | Software Engineering | 3 |
| COMPSCI 170 | Efficient Algorithms and Intractable Problems | 4 |
| COMPSCI 172 | Computability and Complexity | 4 |
| COMPSCI 176 | Algorithms for Computational Biology | 4 |
| COMPSCI 182 | Course Not Available 2 | 4 |
| COMPSCI 184 | Foundations of Computer Graphics | 4 |
| COMPSCI 186 | Introduction to Database Systems | 4 |
| COMPSCI W186 | Introduction to Database Systems | 4 |
| COMPSCI 188 | Introduction to Artificial Intelligence | 4 |
| COMPSCI 189 | Introduction to Machine Learning 2 | 4 |
| NOT COMPSCI/DATA/STAT C100 | ||
| DATA 101 | Course Not Available | 4 |
| DATA C104 | Human Contexts and Ethics of Data - DATA/History/STS | 4 |
| DATA 144 | Data Mining and Analytics | 3 |
| DEMOG 110 | Introduction to Population Analysis | 3 |
| DEMOG C126 | Sex, Death, and Data | 4 |
| DEMOG C175 | Economic Demography | 4 |
| DEMOG 180 | Social Networks | 4 |
| DEMOG 260 | Special Topics in Demography Seminar | 1-4 |
| DIGHUM 150C | Digital Humanities and Text and Language Analysis | 3 |
| EPS 101 | Field Geology and Digital Mapping | 4 |
| EPS 109 | Computer Simulations with Jupyter Notebooks | 4 |
| EPS C129 | Biometeorology | 3 |
| EPS 130 | Strong Motion Seismology | 3 |
| EPS C162 | Planetary Astrophysics | 4 |
| EPS C180 | Air Pollution | 3 |
| EPS C181 | Atmosphere, Ocean, and Climate Dynamics | 3 |
| ECON 101A | Microeconomics (Math Intensive) | 4 |
| ECON 101B | Macroeconomics (Math Intensive) | 4 |
| ECON C102 | Natural Resource Economics | 4 |
| ECON C103 | Introduction to Mathematical Economics | 4 |
| ECON 104 | Advanced Microeconomic Theory | 4 |
| ECON C110 | Game Theory in the Social Sciences 6 | 4 |
| ECON 119 | Psychology and Economics | 4 |
| ECON 121 | Industrial Organization and Public Policy | 4 |
| ECON C125 | Environmental Economics | 4 |
| ECON 131 | Public Economics | 4 |
| ECON 136 | Financial Economics 4 | 4 |
| ECON 138 | Financial and Behavioral Economics | 4 |
| ECON 139 | Asset Pricing and Portfolio Choice | 4 |
| ECON 141 | Econometrics (Math Intensive) | 4 |
| ECON C142 | Applied Econometrics and Public Policy | 4 |
| ECON 144 | Empirical Asset Pricing | 4 |
| ECON 148 | Data Science for Economists | 4 |
| ECON 157 | Health Economics | 4 |
| ECON C171 | Development Economics | 4 |
| ECON 174 | Global Poverty and Impact Evaluation | 4 |
| ECON C175 | Economic Demography | 3 |
| or ECON N175 | Economic Demography | |
| ECON C181 | International Trade | 4 |
| ECON 182 | International Monetary Economics | 4 |
| EL ENG 105 | Microelectronic Devices and Circuits | 4 |
| EL ENG C106A | Introduction to Robotics | 4 |
| EL ENG C106B | Robotic Manipulation and Interaction | 4 |
| EL ENG 113 | Power Electronics | 4 |
| EL ENG 117 | Electromagnetic Fields and Waves | 4 |
| EL ENG 118 | Introduction to Optical Engineering | 4 |
| EL ENG 120 | Signals and Systems | 4 |
| EL ENG 121 | Introduction to Digital Communication Systems | 4 |
| EL ENG 122 | Introduction to Communication Networks | 4 |
| EL ENG 123 | Digital Signal Processing | 4 |
| EL ENG C128 | Feedback Control Systems | 4 |
| EL ENG 130 | Integrated-Circuit Devices | 4 |
| EL ENG 134 | Fundamentals of Photovoltaic Devices | 4 |
| EL ENG 137A | Introduction to Electric Power Systems | 4 |
| EL ENG 137B | Introduction to Electric Power Systems | 4 |
| EL ENG 140 | Linear Integrated Circuits | 4 |
| EL ENG 142 | Integrated Circuits for Communications | 4 |
| EL ENG 143 | Microfabrication Technology | 4 |
| EL ENG 144 | Fundamental Algorithms for Systems Modeling, Analysis, and Optimization | 4 |
| EL ENG C145B | Medical Imaging Signals and Systems | 4 |
| EL ENG C145L | Introductory Electronic Transducers Laboratory | 3 |
| EL ENG C145M | Introductory Microcomputer Interfacing Laboratory | 3 |
| EL ENG C145O | Laboratory in the Mechanics of Organisms | 3 |
| EL ENG 147 | Introduction to Microelectromechanical Systems (MEMS) | 3 |
| EECS 127 | Optimization Models in Engineering | 4 |
| EECS C106A | Introduction to Robotics | 4 |
| EECS C106B | Robotic Manipulation and Interaction | 4 |
| ENE,RES C100 | Energy and Society | 4 |
| ENE,RES 102 | Quantitative Aspects of Global Environmental Problems | 4 |
| ENE,RES 131 | Data, Environment and Society | 4 |
| ENE,RES 175 | Water and Development | 4 |
| ENE,RES C176 | Climate Change Economics | 4 |
| ENGIN 117 | Methods of Engineering Analysis | 3 |
| ENGIN 120 | Principles of Engineering Economics 4 | 3 |
| ENVECON C101 | Environmental Economics | 4 |
| ENVECON C102 | Natural Resource Economics | 4 |
| ENVECON C115 | Modeling and Management of Biological Resources | 4 |
| ENVECON 131 | Globalization and the Natural Environment | 3 |
| ENVECON 140AC | Economics of Race, Agriculture, and the Environment | 3 |
| ENVECON 141 | Agricultural and Environmental Policy | 4 |
| ENVECON 142 | Industrial Organization with Applications to Agriculture and Natural Resources | 4 |
| ENVECON 143 | Economics of Innovation and Intellectual Property | 4 |
| ENVECON 145 | Health and Environmental Economic Policy | 4 |
| ENVECON 147 | The Economics of the Clean Energy Transition | 4 |
| ENVECON C151 | Development Economics | 4 |
| ENVECON 152 | Advanced Topics in Development and International Trade | 3 |
| ENVECON 153 | Population, Environment, and Development | 3 |
| ENVECON 154 | Economics of Poverty and Technology | 3 |
| ENVECON 161 | Advanced Topics in Environmental and Resource Economics | 4 |
| ENVECON 162 | Economics of Water Resources | 3 |
| ENVECON C175 | The Economics of Climate Change | 4 |
| ENVECON C176 | Climate Change Economics | 4 |
| ENVECON C181 | International Trade | 4 |
| ENVECON C183 | Forest Ecosystem Management | 4 |
| ESPM 100 | Environmental Problem Solving | 4 |
| ESPM 102C | Resource Management | 4 |
| ESPM 102D | Climate and Energy Policy | 4 |
| ESPM C103 | Principles of Conservation Biology | 4 |
| ESPM C104 | Modeling and Management of Biological Resources | 4 |
| ESPM C107 | Biology and Geomorphology of Tropical Islands | 13 |
| ESPM 108A | Trees: Taxonomy, Growth, and Structures | 3 |
| ESPM 108B | Environmental Change Genetics | 3 |
| ESPM 111 | Ecosystem Ecology | 4 |
| ESPM 112 | Microbial Ecology | 3 |
| ESPM 114 | Wildlife Ecology | 3 |
| ESPM 115C | Fish Ecology | 3 |
| ESPM 116B | Grassland and Woodland Ecology | 4 |
| ESPM 116C | Tropical Forest Ecology | 3 |
| ESPM 117 | Urban Garden Ecosystems | 4 |
| ESPM 118 | Agricultural Ecology | 4 |
| ESPM 120 | Science of Soils | 3 |
| ESPM 121 | Development and Classification of Soils | 3 |
| ESPM C126 | Animal Behavior | 4 |
| ESPM C128 | Chemistry of Soils | 3 |
| ESPM C129 | Biometeorology | 3 |
| ESPM 131 | Soil Microbiology and Biogeochemistry | 3 |
| ESPM 132 | Spider Biology | 4 |
| ESPM C138 | Introduction to Comparative Virology | 4 |
| ESPM 140 | General Entomology | 4 |
| ESPM 142 | Insect Behavior | 3 |
| ESPM 144 | Insect Physiology | 3 |
| ESPM C148 | Pesticide Chemistry and Toxicology | 3 |
| ESPM C149 | Course Not Available | 4 |
| ESPM 152 | Global Change Biology | 3 |
| ESPM 164 | GIS and Environmental Science | 3 |
| ESPM 165 | International Rural Development Policy | 4 |
| ESPM 172 | Remote Sensing of the Environment | 3 |
| ESPM 173 | Introduction to Ecological Data Analysis | 3 |
| ESPM C177 | GIS and Environmental Spatial Data Analysis | 4 |
| ESPM C180 | Air Pollution | 3 |
| ESPM 181A | Fire Ecology | 3 |
| ESPM 182 | Forest Operations Management | 3 |
| ESPM 183 | Forest Ecosystem Management and Planning | 4 |
| ESPM C183 | Forest Ecosystem Management | 4 |
| ESPM 185 | Applied Forest Ecology | 4 |
| ESPM 186 | Grassland and Woodland Management and Conservation | 4 |
| ESPM 187 | Restoration Ecology | 4 |
| GEOG C136 | Terrestrial Hydrology | 4 |
| GEOG C139 | Atmosphere, Ocean, and Climate Dynamics | 3 |
| GEOG 140A | Physical Landscapes: Process and Form | 4 |
| GEOG 142 | Global Climate Variability and Change | 4 |
| GEOG 143 | Global Change Biogeochemistry | 3 |
| GEOG C145 | Course Not Available | |
| GEOG 148 | Course Not Available | 4 |
| GEOG 187 | Geographic Information Analysis | 4 |
| GEOG C188 | Geographic Information Science | 4 |
| HISTORY C184D | Human Contexts and Ethics of Data - DATA/History/STS | 4 |
| IND ENG 115 | Industrial and Commercial Data Systems | 3 |
| IND ENG 130 | Methods of Manufacturing Improvement | 3 |
| IND ENG 135 | Applied Data Science with Venture Applications | 3 |
| IND ENG 142 | Introduction to Machine Learning and Data Analytics 2 | 3 |
| IND ENG 150 | Production Systems Analysis | 3 |
| IND ENG 151 | Service Operations Design and Analysis | 3 |
| IND ENG 153 | Logistics Network Design and Supply Chain Management | 3 |
| IND ENG 160 | Nonlinear and Discrete Optimization 5 | 3 |
| IND ENG 162 | Linear Programming and Network Flows 5 | 3 |
| IND ENG 166 | Decision Analytics | 3 |
| IND ENG 170 | Industrial Design and Human Factors | 3 |
| IND ENG 221 | Introduction to Financial Engineering | 3 |
| IND ENG 222 | Financial Engineering Systems I | 3 |
| NOT Ind Eng 165, Ind Eng 171, Ind Eng 172 or Ind Eng 173 | ||
| INFO 159 | Natural Language Processing | 4 |
| INFO 188 | Behind the Data: Humans and Values | 3 |
| INFO 213 | Introduction to User Experience Design | 4 |
| INFO 247 | Information Visualization and Presentation | 4 |
| INFO 256 | Applied Natural Language Processing | 3 |
| INFO 271B | Quantitative Research Methods for Information Systems and Management | 3 |
| INFO 272 | Qualitative Research Methods for Information Systems and Management | 3 |
| INTEGBI 102LF | Introduction to California Plant Life with Laboratory | 4 |
| INTEGBI 103LF | Invertebrate Zoology with Laboratory | 5 |
| INTEGBI 104LF | Natural History of the Vertebrates with Laboratory | 5 |
| INTEGBI C107L | Principles of Plant Morphology with Laboratory | 4 |
| INTEGBI C109 | Evolution and Ecology of Development | 3 |
| INTEGBI C110L | Biology of Fungi with Laboratory | 4 |
| INTEGBI 113L | Paleobiological Perspectives on Ecology and Evolution | 4 |
| INTEGBI 117 & 117LF | Medical Ethnobotany and Medical Ethnobotany Laboratory | 4 |
| INTEGBI 118 | Organismal Microbiomes and Host-Pathogen Interactions | 4 |
| INTEGBI 123AL | Exercise and Environmental Physiology with Laboratory | 5 |
| INTEGBI C125L | Introduction to the Biomechanical Analysis of Human Movement | 4 |
| INTEGBI 128 | Sports Medicine | 3 |
| INTEGBI C129L | Human Physiological Assessment | 3 |
| INTEGBI 131 | General Human Anatomy | 3 |
| INTEGBI 132 | Human Physiology | 4 |
| INTEGBI 134L | Practical Genomics | 4 |
| INTEGBI 135 | The Mechanics of Organisms | 4 |
| INTEGBI C135L | Laboratory in the Mechanics of Organisms | 3 |
| INTEGBI 137 | Human Endocrinology | 4 |
| INTEGBI 138 | Comparative Endocrinology | 4 |
| INTEGBI 139 | The Neurobiology of Stress | 4 |
| INTEGBI 140 | Biology of Human Reproduction | 4 |
| INTEGBI C142L | Course Not Available | |
| INTEGBI C143A | Biological Clocks: Physiology and Behavior | 3 |
| INTEGBI C144 | Animal Behavior | 4 |
| INTEGBI 146LF | Behavioral Ecology with Laboratory | 5 |
| INTEGBI 148 | Comparative Animal Physiology | 3 |
| INTEGBI 151 | Plant Physiological Ecology | 4 |
| INTEGBI 153 | Course Not Available | |
| INTEGBI 154 | Plant Ecology | 3 |
| INTEGBI C156 | Principles of Conservation Biology | 4 |
| INTEGBI 157LF | Ecosystems of California | 4 |
| INTEGBI 158LF | Course Not Available | |
| INTEGBI 160 | Course Not Available | |
| INTEGBI 161 | Population and Evolutionary Genetics | 4 |
| INTEGBI 162 | Ecological Genetics | 4 |
| INTEGBI 164 | Human Genetics and Genomics | 4 |
| INTEGBI 166 | Course Not Available | |
| INTEGBI 168L | Plants: Diversity and Evolution | 4 |
| INTEGBI 169 | Evolutionary Medicine | 4 |
| INTEGBI 173LF | Mammalogy with Laboratory | 5 |
| INTEGBI 174LF | Ornithology with Laboratory | 4 |
| INTEGBI 175LF | Herpetology with Laboratory | 4 |
| INTEGBI 181L | Paleobotany - The 500-Million Year History of a Greening Planet | 4 |
| INTEGBI 184L | Morphology of the Vertebrate Skeleton with Laboratory | 4 |
| IAS C175 | The Economics of Climate Change | 4 |
| IAS C176 | Climate Change Economics | 4 |
| LD ARCH 122 | Hydrology for Planners | 4 |
| LD ARCH C177 | GIS and Environmental Spatial Data Analysis | 4 |
| LD ARCH C188 | Geographic Information Science | 4 |
| L & S C180U | Wealth and Poverty | 4 |
| LEGALST 123 | Data, Prediction & Law | 4 |
| LINGUIS 100 | Introduction to Linguistic Science | 4 |
| LINGUIS C105 | Cognitive Linguistics | 4 |
| LINGUIS 110 | Phonetics | 4 |
| LINGUIS 113 | Experimental Phonetics | 3 |
| LINGUIS 140 | Field Methods | 3 |
| LINGUIS C146 | Language Acquisition | 3 |
| LINGUIS C160 | Quantitative Methods in Linguistics | 4 |
| MATH C103 | Introduction to Mathematical Economics | 4 |
| MATH 104 | Introduction to Analysis | 4 |
| MATH H104 | Honors Introduction to Analysis | 4 |
| MATH 105 | Second Course in Analysis | 4 |
| MATH 110 | Abstract Linear Algebra | 4 |
| MATH H110 | Honors Linear Algebra | 4 |
| MATH 113 | Introduction to Abstract Algebra | 4 |
| MATH H113 | Honors Introduction to Abstract Algebra | 4 |
| MATH 114 | Second Course in Abstract Algebra | 4 |
| MATH 115 | Introduction to Number Theory | 4 |
| MATH 116 | Cryptography | 4 |
| MATH 118 | Fourier Analysis, Wavelets, and Signal Processing | 4 |
| MATH 121A | Mathematical Tools for the Physical Sciences | 4 |
| MATH 121B | Mathematical Tools for the Physical Sciences | 4 |
| MATH 123 | Ordinary Differential Equations | 4 |
| MATH 124 | Programming for Mathematical Applications | 4 |
| MATH 125A | Mathematical Logic | 4 |
| MATH 126 | Introduction to Partial Differential Equations | 4 |
| MATH 127 | Mathematical and Computational Methods in Molecular Biology | 4 |
| MATH 128A | Numerical Analysis | 4 |
| MATH 128B | Numerical Analysis | 4 |
| MATH 130 | Groups and Geometries | 4 |
| MATH 135 | Introduction to the Theory of Sets | 4 |
| MATH 136 | Incompleteness and Undecidability | 4 |
| MATH 140 | Metric Differential Geometry | 4 |
| MATH 141 | Elementary Differential Topology | 4 |
| MATH 142 | Elementary Algebraic Topology | 4 |
| MATH 143 | Elementary Algebraic Geometry | 4 |
| MATH 170 | Mathematical Methods for Optimization 5 | 4 |
| MATH 172 | Combinatorics | 4 |
| MATH 185 | Introduction to Complex Analysis | 4 |
| MATH H185 | Honors Introduction to Complex Analysis | 4 |
| MATH 189 | Mathematical Methods in Classical and Quantum Mechanics | 4 |
| MATH 221 | Advanced Matrix Computations | 4 |
| MEC ENG 101 | Introduction to Lean Manufacturing Systems | 3 |
| MEC ENG 102B | Mechatronics Design | 4 |
| MEC ENG 104 | Engineering Mechanics II | 3 |
| MEC ENG 106 | Fluid Mechanics | 3 |
| MEC ENG 108 | Mechanical Behavior of Engineering Materials | 4 |
| MEC ENG 109 | Heat Transfer | 3 |
| MEC ENG 110 | Introduction to Product Development | 3 |
| MEC ENG C115 | Molecular Biomechanics and Mechanobiology of the Cell | 4 |
| MEC ENG C117 | Structural Aspects of Biomaterials | 4 |
| MEC ENG 118 | Introduction to Nanotechnology and Nanoscience | 3 |
| MEC ENG 119 | Introduction to MEMS (Microelectromechanical Systems) | 3 |
| MEC ENG 120 | Computational Biomechanics Across Multiple Scales | 3 |
| MEC ENG 122 | Processing of Materials in Manufacturing | 3 |
| MEC ENG 130 | Design of Planar Machinery | 3 |
| MEC ENG 131 | Vehicle Dynamics and Control | 4 |
| MEC ENG 132 | Dynamic Systems and Feedback | 3 |
| MEC ENG 133 | Mechanical Vibrations | 3 |
| MEC ENG C134 | Feedback Control Systems | 4 |
| MEC ENG 135 | Design of Microprocessor-Based Mechanical Systems | 4 |
| MEC ENG 138 | Introduction to Micro/Nano Mechanical Systems Laboratory | 3 |
| MEC ENG 140 | Combustion Processes | 3 |
| MEC ENG 146 | Energy Conversion Principles | 3 |
| MEC ENG 150A | Solar-Powered Vehicles: Analysis, Design and Fabrication | 3 |
| MEC ENG 151 | Advanced Heat Transfer | 3 |
| MEC ENG 163 | Engineering Aerodynamics | 3 |
| MEC ENG 164 | Marine Statics and Structures | 3 |
| MEC ENG 165 | Ocean-Environment Mechanics | 3 |
| MEC ENG 167 | Microscale Fluid Mechanics | 3 |
| MEC ENG 168 | Mechanics of Offshore Systems | 3 |
| MEC ENG 170 | Engineering Mechanics III | 3 |
| MEC ENG 173 | Fundamentals of Acoustics | 3 |
| MEC ENG 175 | Intermediate Dynamics | 3 |
| MEC ENG C176 | Orthopedic Biomechanics | 4 |
| MEC ENG C178 | Designing for the Human Body | 4 |
| MEC ENG C180 | Engineering Analysis Using the Finite Element Method | 3 |
| MEC ENG 185 | Introduction to Continuum Mechanics | 3 |
| MCELLBI 100B | Biochemistry: Pathways, Mechanisms, and Regulation | 4 |
| MCELLBI C100A | Biophysical Chemistry: Physical Principles and the Molecules of Life | 4 |
| MCELLBI 102 | Survey of the Principles of Biochemistry and Molecular Biology | 4 |
| MCELLBI C103 | Bacterial Pathogenesis | 3 |
| MCELLBI 104 | Genetics, Genomics, and Cell Biology | 4 |
| MCELLBI 110 | Molecular Biology: Macromolecular Synthesis and Cellular Function | 4 |
| MCELLBI C110L | General Biochemistry and Molecular Biology Laboratory | 4 |
| MCELLBI C112 | General Microbiology | 4 |
| MCELLBI C114 | Introduction to Comparative Virology | 4 |
| MCELLBI C116 | Microbial Diversity | 3 |
| MCELLBI 130 | Course Not Available | 4 |
| MCELLBI 132 | Biology of Human Cancer | 4 |
| MCELLBI 133L | Physiology and Cell Biology Laboratory | 4 |
| MCELLBI C134 | Genome Organization and Nuclear Dynamics | 3 |
| MCELLBI 135A | Topics in Cell and Developmental Biology: Molecular Endocrinology | 3 |
| MCELLBI 136 | Physiology | 4 |
| MCELLBI 137L | Physical Biology of the Cell | 4 |
| MCELLBI 140 | General Genetics | 4 |
| MCELLBI 140L | Genetics Laboratory | 4 |
| MCELLBI 141 | Developmental Biology | 4 |
| MCELLBI 143 | Evolution of Genomes, Cells, and Development | 3 |
| MCELLBI C148 | Microbial Genomics and Genetics | 4 |
| MCELLBI 149 | The Human Genome | 3 |
| MCELLBI 150 | Molecular Immunology | 4 |
| MCELLBI 150L | Immunology Laboratory | 4 |
| MCELLBI 160 | Cellular and Molecular Neurobiology | 4 |
| MCELLBI 160L | Neurobiology Laboratory | 4 |
| MCELLBI 166 | Course Not Available | 3 |
| MUSIC 108 | Music Perception and Cognition | 4 |
| MUSIC 108M | Music Perception and Cognition | 4 |
| MUSIC 109 | Music Cognition: The Mind Behind the Musical Ear | 3 |
| MUSIC 109M | Music Cognition: The Mind Behind the Musical Ear | 3 |
| NUC ENG 100 | Introduction to Nuclear Energy and Technology | 3 |
| NUC ENG 130 | Analytical Methods for Non-proliferation | 3 |
| NUC ENG 175 | Methods of Risk Analysis | 3 |
| NUSCTX 103 | Nutrient Function and Metabolism | 4 |
| NUSCTX 110 | Course Not Available | 4 |
| NUSCTX C114 | Pesticide Chemistry and Toxicology | 3 |
| NUSCTX 121 | Course Not Available | 3 |
| PHILOS 121 | Moral Questions of Data Science | 4 |
| PHILOS 128 | Philosophy of Science | 4 |
| PHILOS 140A | Intermediate Logic | 4 |
| PHILOS 140B | Intermediate Logic | 4 |
| PHILOS 142 | Philosophical Logic | 4 |
| PHILOS 143 | Modal Logic | 4 |
| PHILOS 146 | Philosophy of Mathematics | 4 |
| PHYS ED C129 | Human Physiological Assessment | 3 |
| PHYS ED C165 | Introduction to the Biomechanical Analysis of Human Movement | 4 |
| PHYSICS 105 | Analytic Mechanics | 4 |
| PHYSICS 110A | Electromagnetism and Optics | 4 |
| PHYSICS 110B | Electromagnetism and Optics | 4 |
| PHYSICS 111A | Instrumentation Laboratory | 4 |
| PHYSICS 111B | Advanced Experimentation Laboratory (only when taken for 3 units) | 3 |
| PHYSICS 112 | Introduction to Statistical and Thermal Physics | 4 |
| PHYSICS 129 | Particle Physics | 4 |
| PHYSICS 130 | Quantum and Nonlinear Optics | 3 |
| PHYSICS 137A | Quantum Mechanics | 4 |
| PHYSICS 137B | Quantum Mechanics | 4 |
| PHYSICS 138 | Modern Atomic Physics | 3 |
| PHYSICS 139 | Special Relativity and General Relativity | 3 |
| PHYSICS 141A | Solid State Physics | 4 |
| PHYSICS 141B | Solid State Physics | 3 |
| PHYSICS 142 | Introduction to Plasma Physics | 4 |
| PHYSICS 151 | Elective Physics: Special Topics | 3 |
| PHYSICS C161 | Relativistic Astrophysics and Cosmology | 4 |
| PHYSICS 177 | Principles of Molecular Biophysics | 3 |
| PLANTBI 101L | Experimental Plant Biology Laboratory | 3 |
| PLANTBI C103 | Bacterial Pathogenesis | 3 |
| PLANTBI C107L | Principles of Plant Morphology with Laboratory | 4 |
| PLANTBI C109 | Evolution and Ecology of Development | 3 |
| PLANTBI C110L | Biology of Fungi with Laboratory | 4 |
| PLANTBI C112 | General Microbiology | 4 |
| PLANTBI 113 | California Mushrooms | 3 |
| PLANTBI C114 | Introduction to Comparative Virology | 4 |
| PLANTBI C116 | Microbial Diversity | 3 |
| PLANTBI 120 & 120L | Biology of Algae and Laboratory for Biology of Algae | 4 |
| PLANTBI C124 | The Berkeley Lectures on Energy: Energy from Biomass | 3 |
| PLANTBI C134 | Genome Organization and Nuclear Dynamics | 3 |
| PLANTBI 135 | Physiology and Biochemistry of Plants | 3 |
| PLANTBI C148 | Microbial Genomics and Genetics | 4 |
| PLANTBI 150 | Plant Cell Biology | 3 |
| PLANTBI 160 | Plant Molecular Genetics | 3 |
| PLANTBI 165 | Plant-Microbe Interactions | 3 |
| PLANTBI 185 | Techniques in Light Microscopy | 3 |
| PLANTBI 190 | Special Topics in Plant and Microbial Biology (only when taken for 3-4 units) | 3-4 |
| POL SCI C131A | Applied Econometrics and Public Policy | 4 |
| POL SCI 133 | Selected Topics in Quantitative Methods | 4 |
| POL SCI C135 | Game Theory in the Social Sciences 6 | 4 |
| PSYCH 110 | Introduction to Biological Psychology | 3 |
| PSYCH C113 | Biological Clocks: Physiology and Behavior | 3 |
| PSYCH 114 | Biology of Learning | 3 |
| PSYCH C116 | Hormones and Behavior | 3 |
| PSYCH 117 | Human Neuropsychology | 3 |
| PSYCH C120 | Basic Issues in Cognition | 3 |
| PSYCH 121 | Animal Cognition | 3 |
| PSYCH 125 | The Developing Brain | 3 |
| PSYCH C126 | Perception | 3 |
| PSYCH C127 | Cognitive Neuroscience | 3 |
| PSYCH 130 | Clinical Psychology | 3 |
| PSYCH 131 | Developmental Psychopathology | 3 |
| PSYCH 133 | Psychology of Sleep | 3 |
| PSYCH 140 | Developmental Psychology | 3 |
| PSYCH 141 | Development During Infancy | 3 |
| PSYCH C143 | Language Acquisition | 3 |
| PSYCH 150 | Psychology of Personality | 3 |
| PSYCH 164 | Social Cognition | 3 |
| PB HLTH 112 | Global Health: A Multidisciplinary Examination | 4 |
| PB HLTH 126 | Health Economics and Public Policy | 3 |
| PB HLTH 129 | The Aging Human Brain | 3 |
| PB HLTH 132 | Artificial Intelligence for Health and Healthcare | 3 |
| PB HLTH 150A | Introduction to Epidemiology and Human Disease | 4 |
| PB HLTH 150B | Human Health and the Environment in a Changing World | 3 |
| PB HLTH 162A | Public Health Microbiology | 4 |
| PB HLTH 250A | Epidemiologic Methods I | 3 |
| PB HLTH 252B | Infectious Disease Modeling (only when taken for 3-4 units) | 3-4 |
| NOT Pb Hlth 141, 142, 142AB, W142, or 145 | ||
| PUB POL 101 | Introduction to Public Policy Analysis | 4 |
| PUB POL C103 | Wealth and Poverty | 4 |
| PUB POL C142 | Applied Econometrics and Public Policy | 4 |
| PUB POL C184 | Energy and Society | 4 |
| RHETOR 107 | Rhetoric of Scientific Discourse | 4 |
| RHETOR 170 | Rhetoric of Social Science | 4 |
| STS C104D | Human Contexts and Ethics of Data - DATA/History/STS | 4 |
| SOCIOL 105 | Research Design and Sociological Methods | 5 |
| SOCIOL 106 | Quantitative Sociological Methods | 4 |
| SOCIOL 108 | Advanced Methods: In-depth Interviewing | 4 |
| UGBA 101A | Microeconomic Analysis for Business Decisions | 3 |
| UGBA 101B | Macroeconomic Analysis for Business Decisions | 3 |
| UGBA 102A | Financial Accounting 3 | 3 |
| UGBA 102B | Managerial Accounting 3 | 3 |
| UGBA 103 | Introduction to Finance 4 | 4 |
| UGBA 106 | Marketing | 3 |
| UGBA 118 | International Trade | 3 |
| UGBA 120AA | Intermediate Financial Accounting 1 | 4 |
| UGBA 120AB | Intermediate Financial Accounting 2 | 4 |
| UGBA 120B | Advanced Financial Accounting | 4 |
| UGBA 122 | Financial Information Analysis | 4 |
| UGBA 126 | Auditing | 4 |
| UGBA 131 | Corporate Finance and Financial Statement Analysis | 3 |
| UGBA 131A | Corporate Strategy and Valuation | 3 |
| UGBA 132 | Financial Institutions and Markets | 3 |
| UGBA 133 | Investments | 3 |
| UGBA 134 | Introduction to Financial Engineering | 3 |
| UGBA 136F | Behavioral Finance | 3 |
| UGBA 141 | Production and Operations Management | 2-3 |
| UGBA 160 | Customer Insights | 3 |
| UGBA 161 | Market Research: Tools and Techniques for Data Collection and Analysis | 3 |
| UGBA 162 | Brand Management and Strategy | 3 |
| UGBA 165 | Advertising Strategy | 3 |
| UGBA 169 | Pricing | 3 |
| UGBA 180 | Introduction to Real Estate and Urban Land Economics | 3 |
| UGBA 183 | Introduction to Real Estate Finance | 3 |
| UGBA 184 | Urban and Real Estate Economics | 3 |
- 1
IND ENG 172 cannot be used to fulfill this requirement.
- 2
Due to overlap of course content, only one course from STAT 154, COMPSCI 182, COMPSCI 189, and IND ENG 142 can be used to satisfy Statistics major requirements.
- 3
Students may use UGBA 102A and/or UGBA 102B for their cluster, but may NOT use UC Berkeley Extension's XB102A nor XB102B since, effective Spring 2014, the Haas School of Business no longer deems them equivalent (see http://www.haas.berkeley.edu/Undergrad/ugbacourses.html).
- 4
Due to overlap of course content, only one course from ECON 136, ENGIN 120 and UGBA 103 can be used to satisfy Statistics major requirements.
- 5
MATH 170 cannot be combined with either IND ENG 160 or IND ENG 162.
- 6
Due to overlap of course content, students may not use STAT 155 and ECON C110 / POL SCI C135 for the major.
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.
General Guidelines
- All minors must be declared before the first day of instruction of their Expected Graduation Term (EGT).
- 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.
- A minimum grade point average (GPA) of 2.0 is required for courses used to fulfill the minor requirements.
- Courses used to fulfill the minor requirements may be applied toward the Seven-Course Breadth requirements.
- No more than one upper division course may be used to simultaneously fulfill requirements for a student's major and minor programs.
Requirements
| Code | Title | Units |
|---|---|---|
| Lower Division Prerequisites | ||
| MATH 1A | Course Not Available | 4 |
| or MATH N1A | Calculus | |
| MATH 1B | Course Not Available | 4 |
| or MATH N1B | Calculus | |
| or MATH H1B | Honors Calculus | |
| MATH 53 | Multivariable Calculus | 4 |
| or MATH H53 | Honors Multivariable Calculus | |
| or MATH N53 | Multivariable Calculus | |
| or MATH W53 | Multivariable Calculus | |
| MATH 54 | Linear Algebra and Differential Equations | 4 |
| or MATH H54 | Honors Linear Algebra and Differential Equations | |
| or MATH N54 | Linear Algebra and Differential Equations | |
| or MATH 56 | Linear Algebra | |
| Upper Division Requirements | ||
| STAT 134 | Concepts of Probability | 4 |
| or DATA C140 | Probability for Data Science | |
| or EECS 126 | Probability and Random Processes | |
| or MATH 106 | Mathematical Probability Theory | |
| STAT 135 | Concepts of Statistics | 4 |
| Select three statistics electives from the following; at least one of the selections must have a lab: | ||
| DATA C102 | Data, Inference, and Decisions (LAB COURSE) | 4 |
| STAT 150 | Stochastic Processes | 3 |
| STAT 151A | Linear Modelling: Theory and Applications (LAB COURSE) | 4 |
| STAT 152 | Sampling Surveys (LAB COURSE) | 4 |
| STAT 153 | Introduction to Time Series (LAB COURSE) | 4 |
| STAT 154 | Modern Statistical Prediction and Machine Learning (LAB COURSE) | 4 |
| STAT 155 | Game Theory | 3 |
| STAT 156 | Causal Inference (LAB COURSE) | 4 |
| STAT 157 | Seminar on Topics in Probability and Statistics | 3 |
| STAT 158 | Experimental Design (LAB COURSE) | 4 |
| STAT 159 | Reproducible and Collaborative Statistical Data Science (LAB COURSE) | 4 |
| STAT 165 | Forecasting | 3 |
College Requirements
Essential Skills
Computational Reasoning
The Computational Reasoning requirement is designed to provide a basic understanding of and competency in concepts such as programming, algorithms, iteration, and data-structures.
Human and Social Dynamics of Data and Technology
The Human and Social Dynamics of Data and Technology requirement is designed for the purpose of developing an understanding of how technology and data interact with human and societal contexts, including ethical considerations and applications such as education, health, law, natural resources, and public policy.
Statistical Reasoning
The Statistical Reasoning requirement is designed to provide basic understanding of and competency in the scientific approach to statistical problem solving, including uncertainty, prediction, and estimation.
Reading and Composition
The Reading and Composition requirement is the same as for the College of Letters and Science; it requires two semesters of lower division work in composition in sequence. Students must complete parts A & B reading and composition courses in sequential order by the end of their fourth semester.
To see how to satisfy the R&C requirement, visit the College of Letters and Science Reading and Composition Requirement page.
Breadth Requirements
The undergraduate breadth requirements are the same for CDSS students as for the College of Letters and Science, with the exception that a second semester foreign language course can be used to satisfy the International Studies breadth. To learn more about the L&S Seven-Course Breadth Requirement, visit the L&S Breadth Requirements page. To learn more about using a foreign language course to satisfy the International Studies breadth, visit the CDSS website page on Satisfying International Studies Breadth with a Foreign Language Course.
The undergraduate major programs in computer science, data science, and statistics have transitioned from the College of Letters & Science to CDSS. Students who were admitted in Spring 2024 or earlier have the option of completing either the L&S College Requirements, i.e., the breadth and essential skills requirements, or the CDSS college requirements described above.
All students must meet CDSS general policy (below). The one exception is with time-to-degree. Students admitted Fall 2022 or earlier are subject to the 130 unit maximum, rather than the 8 semester maximum (5 for transfer students).
Class Schedule Requirements
-
Minimum units per semester: 12
-
Maximum units per semester: 20.5
Academic (Grade) Requirements
-
Minimum cumulative GPA: 2.0
-
Minimum GPA for one semester: 1.5
Bachelor’s Degree Requirements
-
Minimum total units: 120. Of these 120 units:
-
PE maximum units: 4
-
Special Studies maximum units: 16
-
Maximum 300-499 course units: 6
-
-
Minimum upper division units: 36
-
Maximum number of semesters: 8 for first-year entrants; 5 for transfer students; summer terms do not count toward the maximum
-
Minimum GPA in upper division and graduate courses identified for the major: 2.0
-
Meet all major requirements
-
Meet all general, curricular, and residence requirements of the University of California and the Berkeley campus
For more information about CDSS requirements, visit student resources and information on the College of Computing, Data Science, and Society website.
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:
- 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 are experience maps that help undergraduates plan their Berkeley journey based on intended major or field of interest. Featuring student opportunities and resources from your college and department as well as across campus, each map includes curated suggestions for planning your studies, engaging outside the classroom, and pursuing your career goals in a timeline format.
Use the major map below to explore potential paths and design your own unique undergraduate experience:
Contact Information
Undergraduate Major Advisor & Course and Curriculum Officer
Denise Yee
369 Evans Hall
Phone: 510-643-6131
Undergraduate Major Advisor & Course and Curriculum Officer
Natalie Poulos
371 Evans Hall
Phone: 510-664-5335