About the Program
Bachelor of Arts (BA)
The Data Science Major degree program combines computational and inferential reasoning to draw conclusions based on data about some aspect of the real world. Data scientists come from all walks of life, all areas of study, and all backgrounds. They share an appreciation for the practical use of mathematical and scientific thinking and the power of computing to understand and solve problems for business, research, and societal impact.
The Data Science Major will equip students to draw sound conclusions from data in context, using knowledge of statistical inference, computational processes, data management strategies, domain knowledge, and theory. Students will learn to carry out analyses of data through the full cycle of the investigative process in scientific and practical contexts. Students will gain an understanding of the human and ethical implications of data analytics and integrate that knowledge in designing and carrying out their work.
The Data Science major requirements include DATA C8 and DATA C100, the core lower-division and upper-division elements of the major, along with courses from each of the following requirement groups:
- Foundations in Mathematics and Computing
- Computational and Inferential Depth
- Modeling, Learning and Decision Making
- Probability
- Human Contexts and Ethics
- Domain Emphasis
All students will select a Domain Emphasis, a cluster of one lower division course and two upper division courses, that brings them into the context of a domain and allows them to build bridges with data science.
Minor Program
The Minor in Data Science at UC Berkeley aims to provide students with practical knowledge of the methods and techniques of data analysis, as well as the ability to think critically about the construction and implications of data analysis and models. The minor will empower students across the wide array of campus disciplines with a working knowledge of statistics, probability, and computation that allow students not just to participate in data science projects, but to design and carry out rigorous computational and inferential analysis for their field of interest. Check the Data Science Minor program website for details.
Major Requirements
In addition to the University, campus, and college requirements, students must fulfill the below requirements specific to the major program. Please check the Data Science program website for updates.
General Guidelines
- All courses taken to fulfill the major requirements below must be taken for letter-graded credit.
- No more than two upper-division courses can overlap between two majors.
- A minimum grade point average (GPA) of 2.0 must be maintained in all courses toward the major, and in all upper-division courses toward the major.
Lower Division Prerequisites
| Code | Title | Units |
|---|---|---|
| DATA/COMPSCI/STAT/INFO C8 | Foundations of Data Science 1 | 4 |
| or STAT 20 | Introduction to Probability and Statistics | |
| MATH 51 | Calculus I (MATH 51 as of Fall 2025) | 4 |
| or MATH 10A | Methods of Mathematics: Calculus, Statistics, and Combinatorics | |
| or MATH 16A | Analytic Geometry and Calculus | |
| MATH 52 | Calculus II (MATH 52 as of Fall 2025) | 4 |
| MATH 54 | Linear Algebra and Differential Equations | 4 |
| or MATH 56 | Linear Algebra | |
| or STAT 89A | Linear Algebra for Data Science | |
| or EECS 16A & EECS 16B | Foundations of Signals, Dynamical Systems, and Information Processing and Introduction to Circuits & Devices | |
| or PHYSICS 89 | Introduction to Mathematical Physics | |
| COMPSCI 61A | The Structure and Interpretation of Computer Programs | 4 |
| or DATA C88C | Computational Structures in Data Science | |
| or COMPSCI C88C | Computational Structures in Data Science | |
| or ENGIN 7 | Introduction to Computer Programming and Numerical Methods | |
| COMPSCI 61B | Data Structures | 4 |
1Students may substitute Stat 20 for Data C8 toward the major when combined with CS 61A or CS 88/Data C88C; this option is not available for students who take Engin 7 for their Program Structures requirement. See the lower-division requirements page on the Data Science program website for more details.
Lower Division Requirements
Students will also be required to take one lower division course towards their choice of Domain Emphasis.
Upper Division Requirements
Students will be required to complete 8 unique upper-division courses for a total of 28 or more units from the following requirement categories.
Principles and techniques of data science
| Code | Title | Units |
|---|---|---|
| DATA/COMPSCI/STAT C100 | Principles & Techniques of Data Science | 4 |
Computational and Inferential Depth
Students will be required to take two upper division courses comprising 7 or more units that provide computational and inferential depth beyond that provided in Data 100 and the lower-division courses.
| Code | Title | Units |
|---|---|---|
| Choose two courses comprising 7+ units from the following: | ||
| ASTRON 128 | Astronomy Data Science Laboratory | 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 |
| or COMPSCI 169A | Introduction to Software Engineering | |
| or COMPSCI W169A | Software Engineering | |
| COMPSCI 170 | Efficient Algorithms and Intractable Problems | 4 |
| COMPSCI 186 | Introduction to Database Systems | 4 |
| or COMPSCI W186 | Introduction to Database Systems | |
| COMPSCI 188 | Introduction to Artificial Intelligence | 4 |
| DATA C101 | Data Engineering | 4 |
| DATA 144 | Data Mining and Analytics | 3 |
| ECON 140 | Econometrics | 4 |
| or ECON 141 | Econometrics (Math Intensive) | |
| EECS 127 | Optimization Models in Engineering | 4 |
| EL ENG 120 | Signals and Systems | 4 |
| EL ENG 123 | Digital Signal Processing | 4 |
| ENVECON C118 | Introductory Applied Econometrics | 4 |
| ESPM 174 | Design and Analysis of Ecological Research | 4 |
| IAS C118 | Introductory Applied Econometrics | 4 |
| IND ENG 115 | Industrial and Commercial Data Systems | 3 |
| IND ENG 135 | Applied Data Science with Venture Applications | 3 |
| IND ENG 142B | Machine Learning and Data Analytics II | 4 |
| IND ENG 160 | Nonlinear and Discrete Optimization | 3 |
| IND ENG 162 | Linear Programming and Network Flows | 3 |
| IND ENG 164 | Introduction to Optimization Modeling | 3 |
| IND ENG 165 | Engineering Statistics, Quality Control, and Forecasting | 4 |
| IND ENG 166 | Decision Analytics | 3 |
| IND ENG 173 | Introduction to Stochastic Processes | 3 |
| INFO 159 | Natural Language Processing | 4 |
| INFO 190 | Special Topics in Information (Introduction to Data Visualization - only when offered on this topic) | 4 |
| MATH 156 | Numerical Analysis for Data Science and Statistics | 4 |
| NUC ENG 175 | Methods of Risk Analysis | 3 |
| PHYSICS 188 | Bayesian Data Analysis and Machine Learning for Physical Sciences (previously PHYSICS 188) | 4 |
| STAT 135 | Concepts of Statistics | 4 |
| STAT 150 | Stochastic Processes | 3 |
| STAT 151A | Linear Modelling: Theory and Applications | 4 |
| STAT 152 | Sampling Surveys | 4 |
| STAT 153 | Introduction to Time Series | 4 |
| STAT 158 | Experimental Design | 4 |
| STAT 159 | Reproducible and Collaborative Statistical Data Science | 4 |
| STAT 165 | Forecasting | 3 |
| UGBA 142 | Advanced Business Analytics | 3 |
Probability
Students will be required to take one upper-division course on probability.
| Code | Title | Units |
|---|---|---|
| Choose one of the following: | ||
| DATA/STAT C140 | Probability for Data Science | 4 |
| MATH 106 | Mathematical Probability Theory | 4 |
| EL ENG 126 | Probability and Random Processes | 4 |
| IND ENG 172 | Probability and Risk Analysis for Engineers | 4 |
| STAT 134 | Concepts of Probability | 4 |
Modeling, Learning, and Decision-Making
Students will be required to take one upper-division course on modeling, learning, and decision-making.
| Code | Title | Units |
|---|---|---|
| Choose one of the following: | ||
| COMPSCI C182 | Designing, Visualizing and Understanding Deep Neural Networks | 4 |
| COMPSCI 189 | Introduction to Machine Learning | 4 |
| DATA/STAT C102 | Data, Inference, and Decisions | 4 |
| IND ENG 142A | Introduction to Machine Learning and Data Analytics | 4 |
| or IND ENG 142 | Introduction to Machine Learning and Data Analytics | |
| STAT 154 | Modern Statistical Prediction and Machine Learning | 4 |
Human Contexts and Ethics
Students will be required to take one course from a curated list of courses that establish a human, social, and ethical context in which data analytics and computational inference play a central role.
| Code | Title | Units |
|---|---|---|
| AFRICAM 134 | Information Technology and Society | 4 |
| or AFRICAM/AMERSTD C134 | Information Technology and Society | |
| BIO ENG 100 | Ethics in Science and Engineering | 3 |
| CY PLAN 101 | Introduction to Urban Data Analytics | 4 |
| DATA C104/HISTORY C184D/STS C104D | Human Contexts and Ethics of Data - DATA/History/STS | 4 |
| DIGHUM 100 | Theory and Method in the Digital Humanities | 3 |
| INFO 188 | Behind the Data: Humans and Values | 3 |
| ISF 100J | The Social Life of Computing | 4 |
| NWMEDIA 151AC | Transforming Tech: Issues and Interventions in STEM and Silicon Valley | 4 |
| PHILOS 121 | Moral Questions of Data Science | 4 |
| PB HLTH C160/ESPM C167 | Environmental Health and Development | 4 |
Domain Emphasis
Students will also be required to take two upper-division courses towards their choice of Domain Emphasis.
Domain Emphases that students can choose from:
- Applied Mathematics and Modeling
- Business and Industrial Analytics
- Cognition
- Computational Methods in Molecular and Genomic Biology
- Data Arts and Humanities
- Ecology and the Environment
- Economics
- Environment, Resource Management, and Society
- Evolution and Biodiversity
- Geospatial Information and Technology
- Human and Population Health
- Human Behavior and Psychology
- Inequalities in Society
- Linguistic Sciences
- Neurosciences
- Organizations and the Economy
- Philosophical Foundations: Evidence and Inference
- Philosophical Foundations: Minds, Morals, and Machines
- Physical Science Analytics
- Quantitative Social Science
- Robotics
- Science, Technology, and Society
- Social Welfare, Health, and Poverty
- Social Policy and Law
- Sustainable Development and Engineering
- Urban Science
From the lists shown below, students will select one course from the lower-division, and two courses from the upper-division. The lower division course is a required element of the Domain Emphasis.
NOTE: Courses in each domain emphasis may be restricted by major to enroll and/or have extensive prerequisites. It may be difficult to complete an emphasis given these restrictions. Students are advised to make appropriate alternate plans. Prerequisites can be viewed by clicking on a course link.
Applied Mathematics and Modeling
The Applied Mathematics and Modeling domain emphasis gives students the opportunity to explore mathematical techniques essential to data science and mathematical modeling. Apart from gaining core competencies in advanced calculus and linear algebra, students can learn numerical approximation and optimal decision methods, as well as gain experience in their implementation in parallel programming.
The Honors versions of these courses (where applicable) will also be accepted.
| Code | Title | Units |
|---|---|---|
| Lower Division (choose one) | ||
| MATH 53 | Multivariable Calculus | 4 |
| MATH 55 | Discrete Mathematics | 4 |
| Upper Division (choose two) | ||
| CIV ENG C133/MEC ENG C180 | Engineering Analysis Using the Finite Element Method | 3 |
| EECS 127 | Optimization Models in Engineering | 4 |
| ENGIN 150 | Basic Modeling and Simulation Tools for Industrial Research Applications | 4 |
| IND ENG 160 | Nonlinear and Discrete Optimization | 3 |
| IND ENG 162 | Linear Programming and Network Flows | 3 |
| MATH 104 | Introduction to Analysis | 4 |
| MATH 110 | Abstract Linear Algebra | 4 |
| MATH 113 | Introduction to Abstract Algebra | 4 |
| MATH 118 | Fourier Analysis, Wavelets, and Signal Processing | 4 |
| MATH 128A | Numerical Analysis | 4 |
| MATH 156 | Numerical Analysis for Data Science and Statistics | 4 |
| COMPSCI C267/ENGIN C233 | Applications of Parallel Computers | 3 |
| We recognize in general that to satisfy the prerequisites for these courses below, a student will have already satisfied the Domain Emphasis. Because these courses are natural to include in this emphasis, they will function as an elective for many students who take them. They are included here merely for those students who get to these courses from nontraditional paths, for whom these courses should count towards the DE. | ||
| MATH 128B | Numerical Analysis | 4 |
Business and Industrial Analytics
The Business and Industrial Analytics domain emphasis allows students to explore the principles and methods of making data-driven decisions under uncertainty in the worlds of business and industry. Students will learn how to approach management decisions from economic, probabilistic, and computational perspectives, and how to analyze and manage risk.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| ECON 1 | Introduction to Economics | 4 |
| ECON 2 | Introduction to Economics--Lecture Format | 4 |
| MATH 53 | Multivariable Calculus | 4 |
| Upper Division (select two) | ||
| IND ENG 115 | Industrial and Commercial Data Systems | 3 |
| IND ENG 120 | Principles of Engineering Economics | 3 |
| IND ENG 130 | Methods of Manufacturing Improvement | 3 |
| IND ENG 153 | Logistics Network Design and Supply Chain Management | 3 |
| IND ENG 156 | Healthcare Analytics | 3 |
| IND ENG 166 | Decision Analytics | 3 |
| UGBA 104 | Introduction to Business Analytics | 3 |
| UGBA 134 | Introduction to Financial Engineering | 3 |
| UGBA 141 | Production and Operations Management (when completed for 3 units) | 3 |
| UGBA 142 | Advanced Business Analytics | 3 |
| UGBA 161 | Market Research: Tools and Techniques for Data Collection and Analysis | 3 |
| For students completing the lower-division requirement outside of UC Berkeley at a college where microeconomics and macroeconomics are offered as separate courses, only microeconomics is required for the Data Science BA. However, note that full equivalence to Econ 1 may still be required as a prerequisite to other courses you wish to take at UC Berkeley. | ||
COgnition
The Cognition domain emphasis introduces students to fundamental scientific questions about how the human mind works. It gives them the opportunity to pursue one or more disciplinary approaches, including psychology, neuroscience, and linguistics, and to consider computational models of mind.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| COG SCI 1/1B/N1 | Introduction to Cognitive Science | 4 |
| PSYCH C61 | Brain, Mind, and Behavior | 3 |
| PSYCH C64 | Exploring the Brain: Introduction to Neuroscience | 3 |
| Upper Division (select two) | ||
| COG SCI C100/PSYCH C120 | Basic Issues in Cognition | 3 |
| COG SCI C101/LINGUIS C105 | Cognitive Linguistics | 4 |
| COG SCI/PSYCH C126 | Perception | 3 |
| COG SCI/PSYCH C127 | Cognitive Neuroscience | 3 |
| COG SCI 131/PSYCH C123 | Computational Models of Cognition | 4 |
| COG SCI 132 | Rhythms of the Brain: from Neuronal Communication to Function | 4 |
| COG SCI 150 | Sensemaking and Organizing | 3 |
| COG SCI 180 | Mind, Brain, and Identity | 3 |
| COG SCI 190 | Special Topics in Cognitive Science (Data Science and Cognition -- only when offered with this topic) | 3 |
| COMPSCI 188 | Introduction to Artificial Intelligence | 4 |
| MUSIC 108 | Music Perception and Cognition | 4 |
| or MUSIC 108M | Music Perception and Cognition | |
| PSYCH 114 | Biology of Learning | 3 |
| PSYCH 117 | Human Neuropsychology | 3 |
| PSYCH 131 | Developmental Psychopathology | 3 |
| PSYCH C143/LINGUIS C146 | Language Acquisition | 3 |
Computational Methods in Molecular and Genomic Biology
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| BIOLOGY 1A | General Biology Lecture | 3 |
| BIOLOGY 1B | General Biology Lecture and Laboratory | 4 |
| MATH 53 | Multivariable Calculus | 4 |
| Upper Division (select two) | ||
| BIO ENG 131/CMPBIO C131 | Introduction to Computational Molecular and Cell Biology | 4 |
| BIO ENG 134 | Genetic Design Automation | 4 |
| BIO ENG 145 | Introduction to Machine Learning for Computational Biology | 4 |
| BIO ENG C149 | Computational Functional Genomics | 4 |
| CMPBIO C149 | Computational Functional Genomics | 4 |
| CMPBIO 156 | Human Genome, Environment and Public Health | 4 |
| CMPBIO/COMPSCI C176 | Algorithms for Computational Biology | 4 |
| INTEGBI 120 | Introduction to Quantitative Methods In Biology | 4 |
| INTEGBI 134L | Practical Genomics | 4 |
| INTEGBI 141 | Human Genetics | 3 |
| or INTEGBI 164 | Human Genetics and Genomics | |
| or MCELLBI 149 | The Human Genome | |
| INTEGBI 161 | Population and Evolutionary Genetics | 4 |
| MATH 127 | Mathematical and Computational Methods in Molecular Biology | 4 |
| MCELLBI C100A/CHEM C130 | Biophysical Chemistry: Physical Principles and the Molecules of Life | 4 |
| MCELLBI 102 | Survey of the Principles of Biochemistry and Molecular Biology | 4 |
| MCELLBI 104 | Genetics, Genomics, and Cell Biology | 4 |
| MCELLBI 132 | Biology of Human Cancer | 4 |
| MCELLBI 137L | Physical Biology of the Cell | 4 |
| MCELLBI 140 | General Genetics | 4 |
| MCELLBI 143 | Evolution of Genomes, Cells, and Development | 3 |
| MCELLBI/PLANTBI C148 | Microbial Genomics and Genetics | 4 |
| MCELLBI 153 | Molecular Medicine | 4 |
| PLANTBI 160 | Plant Molecular Genetics | 3 |
DATA ARTS AND HUMANITIES
The Data Arts and Humanities domain emphasis allows students to explore and engage data science practices across the humanities and arts. In addition to investigating the place of data in humanistic inquiry and creative work in broad terms, students can learn current data arts and humanities methods specific to different disciplines and departments, as and together with critical inquiry
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| ART 23AC | DIGITAL MEDIA: FOUNDATIONS | 4 |
| HISTORY 88 | How Does History Count? | 2 |
| L & S 88 | Data Science Connector (Rediscovering Text as Data (only when offered with this topic)) | 2-4 |
| L & S 88 | Data Science Connector (Aesthetics and Data (only when offered with this topic)) | 2-4 |
| MUSIC 29 | Music Now | 4 |
| MUSIC 30 | Computational Creativity for Music and the Arts | 4 |
| RHETOR 10 | Introduction to Practical Reasoning and Critical Analysis of Argument | 4 |
| Upper Division (select two) | ||
| ART 172 | Advanced Digital Media: Computer Graphics Studio | 4 |
| DIGHUM 100 | Theory and Method in the Digital Humanities (summer only) | 3 |
| DIGHUM 101 | Python Programming for Digital Humanities (summer only) | 3 |
| DIGHUM 150A | Digital Humanities and Archival Design (summer only) | 3 |
| DIGHUM 150B | Digital Humanities and Visual and Spatial Analysis (summer only) | 3 |
| DIGHUM 150C | Digital Humanities and Text and Language Analysis (summer only) | 3 |
| DIGHUM 160 | Critical Digital Humanities (summer only) | 3 |
| GLOBAL 140 | Special Topics in Global Societies and Cultures (Mapping Diasporas: Jewish Culture, Museums, and Digital Humanities (only when offered with this topic)) | 4 |
| or JEWISH 121 | Topics in Jewish Arts and Culture | |
| HISTART C109/ENGLISH C181 | Digital Humanities, Visual Cultures | 4 |
| HISTORY 133D | Calculating Americans: Big Histories of Small Data | 4 |
| HISTART 190T | Transcultural (VR and Its Prehistories (only when offered with this topic)) | 4 |
| HISTART 192DH | Undergraduate Seminar: Digital Imaging and Forensic Art History | 4 |
| INFO 103 | History of Information | 4 |
| INFO 159 | Natural Language Processing | 4 |
| INFO 190 | Special Topics in Information (Introduction to Data Visualization) | 4 |
| MUSIC 107 | Independent Projects in Computer Music | 4 |
| MUSIC 158A | Sound and Music Computing with CNMAT Technologies | 4 |
| MUSIC 158B | Situated Instrument Design for Musical Expression | 4 |
| MUSIC 159 | Computer Programming for Music Applications | 4 |
| MELC 110 | Digital Humanities and Egyptology | 4 |
| RHETOR 107 | Rhetoric of Scientific Discourse | 4 |
| RHETOR 114 | Rhetoric of New Media | 4 |
| RHETOR 115 | Technology and Culture | 4 |
| RHETOR 137 | Rhetoric of the Image | 4 |
| RHETOR 145 | Science, Narrative, and Image | 4 |
| RHETOR 170 | Rhetoric of Social Science | 4 |
| Additionally, many classes in this area have been taught on an experimental or infrequent basis. Students may petition to include the classes below, or other classes they believe meet the goals of this DE: | ||
| AMERSTD H110 | Honors Seminar: Special Topics in American Studies (Bay Area in the 1970s (only when offered with this topic)) | 3-4 |
| ENGLISH 166 | Special Topics (Slavery and Conspiracy (only when offered with this topic)) | 4 |
| HISTORY 100S | Special Topics in the History of Science (Text Analysis for Digital Humanists and Social Scientists (only when offered with this topic)) | 4 |
| HISTORY 104 | The Craft of History | 4 |
| THEATER 166/NWMEDIA 190 | Special Topics: Theater Arts ("Making Sense of Cultural Data (only when offered with this topic)) | 1-4 |
| MELC 114 | Beyond Wikipedia: The Ancient Middle East | 3 |
| MELC 190A | Special Topics in Fields of Middle Eastern Languages and Cultures: Ancient Middle Eastern Studies (Introduction to Digital Humanities: From Analog to Digital (only when offered with this topic)) | 4 |
| SPANISH 135 | Studies in Hispanic Literature (Electronic Literature: A Critical Writing & Making Course (only when offered with this topic)) | 4 |
Ecology and the Environment
The domain emphasis in Ecology and Environment explores the rapidly emerging diverse data sources from gene sequencing to satellites that shed light on the behavior, abundance and distribution of living organisms and the ecosystems they inhabit
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| L & S/ESPM C46 | Climate Change and the Future of California | 4 |
| EPS 80 | Environmental Earth Sciences | 3 |
| ESPM 15 | Introduction to Environmental Sciences | 3 |
| ESPM 2 | The Biosphere | 3 |
| ESPM 6 | Environmental Biology | 3 |
| ESPM 88B | Data Sciences in Ecology and the Environment | 2 |
| GEOG 40 | Introduction to Earth System Science | 4 |
| Upper Division (select two) | ||
| ENE,RES 102 | Quantitative Aspects of Global Environmental Problems | 4 |
| ESPM 102B & 102BL | Natural Resource Sampling and Laboratory in Natural Resource Sampling | 2, 2 |
| ESPM C103/INTEGBI C156 | Principles of Conservation Biology | 4 |
| ESPM 111 | Ecosystem Ecology | 4 |
| ESPM/EPS C129 | Biometeorology | 3 |
| ESPM 130A | Forest Hydrology | 4 |
| ESPM/INTEGBI C153 | Ecology | 3 |
| INTEGBI 170LF | Methods in Population and Community Ecology | 3 |
| ESPM 157 | Data Science in Global Change Ecology | 4 |
| ESPM C170/EPS C183 | Carbon Cycle Dynamics | 3 |
| ESPM 174A | Applied Time Series Analysis for Ecology and Environmental Sciences | 3 |
| CIV ENG C106/EPS C180/ESPM C180 | Air Pollution | 3 |
Economics
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| ECON 1 | Introduction to Economics | 4 |
| ECON 2 | Introduction to Economics--Lecture Format | 4 |
| DATA 88E | Economic Models | 2 |
| Upper Division (select two) | ||
| ECON 100A | Microeconomics | 4 |
| or ECON 101A | Microeconomics (Math Intensive) | |
| or ECON 100B | Macroeconomics | |
| or ECON 101B | Macroeconomics (Math Intensive) | |
| ECON/MATH C103 | Introduction to Mathematical Economics | 4 |
| MATH C103 | Introduction to Mathematical Economics | 4 |
| ECON 104 | Advanced Microeconomic Theory | 4 |
| ECON C110/N110/POL SCI C135 | Game Theory in the Social Sciences | 4 |
| ECON 119 | Psychology and Economics | 4 |
| ECON 121 | Industrial Organization and Public Policy | 4 |
| ECON C125/ENVECON C101 | Environmental Economics | 4 |
| ECON 131 | Public Economics | 4 |
| ECON 134 | Macroeconomic Policy from the Great Depression to Today | 4 |
| ECON 136 | Financial Economics | 4 |
| ECON 139 | Asset Pricing and Portfolio Choice | 4 |
| ECON 140 | Econometrics | 4 |
| or ECON 141 | Econometrics (Math Intensive) | |
| ECON/PUB POL C142/POL SCI C131A | Applied Econometrics and Public Policy | 4 |
| ECON 143 | Econometrics: Advanced Methods and Applications | 4 |
| ECON 144/COMPSCI C177 | Empirical Asset Pricing | 4 |
| ECON 148 | Data Science for Economists | 4 |
| ECON 151 | Labor Economics | 4 |
| ECON 152 | Wage Theory and Policy | 4 |
| ECON 172 | Case Studies in Economic Development | 4 |
| ECON 174 | Global Poverty and Impact Evaluation | 4 |
| ECON/DEMOG C175 | Economic Demography | 4 |
| ECON C184 | International Environmental Economics | 4 |
| ENVECON/IAS C118 | Introductory Applied Econometrics | 4 |
| ENVECON C132 | International Environmental Economics | 4 |
Environment, Resource Management, and Society
The Domain Emphasis in Environment, Resource Management, and Society explores the interface of economics and policy with ecological and environmental sciences. Topics include climate change, agro-ecology, energy policy, natural resources, sociology, and culture.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| ECON C3/ENVECON C1 | Introduction to Environmental Economics and Policy | 4 |
| ESPM 50AC | Introduction to Culture and Natural Resource Management | 4 |
| Upper Division (select two) | ||
| ENVECON 100 | Intermediate Microeconomics with Applications to Sustainability | 4 |
| ENVECON C101/ECON C125 | Environmental Economics | 4 |
| ENVECON C102 | Natural Resource Economics | 4 |
| ENVECON C115/ESPM C104 | Modeling and Management of Biological Resources | 4 |
| ENVECON 141 | Agricultural and Environmental Policy | 4 |
| ENVECON 142 | Industrial Organization with Applications to Agriculture and Natural Resources | 4 |
| ENVECON 145 | Health and Environmental Economic Policy | 4 |
| ENVECON 147 | The Economics of the Clean Energy Transition | 4 |
| ENVECON 153 | Population, Environment, and Development | 3 |
| ENE,RES C100/PUB POL C184 | Energy and Society | 4 |
OR | ||
| Energy and Society [4] | ||
| ENE,RES 131 | Data, Environment and Society | 4 |
| ENE,RES/ENVECON/IAS C176 | Climate Change Economics | 4 |
| ESPM 102C | Resource Management | 4 |
| ESPM 102D | Climate and Energy Policy | 4 |
| ESPM 151 | Society, Environment, and Culture | 4 |
| ESPM 155AC | Sociology and Political Ecology of Agro-Food Systems | 4 |
| ESPM 168 | Political Ecology | 4 |
| ESPM 186 | Grassland and Woodland Management and Conservation | 4 |
Evolution and Biodiversity
The domain emphasis in Evolution and Biodiversity explores the origins and evolution of the astounding diversity of life on earth. Topics include the analyses and understanding of diverse data from fossils to genomes from our deep past to better understand our planet today.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| BIOLOGY 1A | General Biology Lecture | 3 |
| BIOLOGY 1B | General Biology Lecture and Laboratory | 4 |
| Upper Division (select two) | ||
| ESPM/INTEGBI C105 | Natural History Museums and Biodiversity Science | 3 |
| ESPM 108B | Environmental Change Genetics | 3 |
| ESPM C125/GEOG C148/INTEGBI C166 | Biogeography | 4 |
| ESPM 152 | Global Change Biology | 3 |
| INTEGBI/PLANTBI C109 | Evolution and Ecology of Development | 3 |
| INTEGBI 113L | Paleobiological Perspectives on Ecology and Evolution | 4 |
| INTEGBI 117 & 117LF | Medical Ethnobotany and Medical Ethnobotany Laboratory | 2 |
| INTEGBI 141 | Human Genetics | 3 |
| or INTEGBI 164 | Human Genetics and Genomics | |
| INTEGBI C160/MCELLBI C144 | Evolution | 4 |
| or INTEGBI 167 | Evolution and Earth History: From Genes to Fossils | |
| INTEGBI 161 | Population and Evolutionary Genetics | 4 |
| INTEGBI 162 | Ecological Genetics | 4 |
| INTEGBI 169 | Evolutionary Medicine | 4 |
| INTEGBI 172 | Coevolution: From Genes to Ecosystems | 4 |
GEOSPATIAL INFORMATION AND TECHNOLOGY
This domain emphasis explores the use of geospatial approaches to understand geophysical and ecological processes. Topics of study include climate change, cartography, digital mapping, remote sensing, ecology, and environmental data analysis, among others.
| Code | Title | Units |
|---|---|---|
| CIV ENG/CY PLAN C88 | Data Science for Smart Cities | 2 |
| ESPM 72 | Introduction to Geographic Information Systems | 3 |
| ESPM 88A | Exploring Geospatial Data | 2 |
| EPS 50 | The Planet Earth | 4 |
| GEOG 80 | An Introduction to Geospatial Technologies: Mapping, Space and Power | 4 |
| GEOG 88 | Data Science Applications in Geography | 2 |
| Upper Division (select two) | ||
| GEOG 183 | Cartographic Representation | 5 |
| GEOG 185 | Earth System Remote Sensing | 3 |
| GEOG 186 | Web Cartography | 5 |
| GEOG 187 | Geographic Information Analysis | 4 |
| GEOG/LD ARCH C188 | Geographic Information Science | 4 |
| EPS 101 | Field Geology and Digital Mapping | 4 |
| EPS 115 | Stratigraphy and Earth History | 4 |
| ESPM 137 | Landscape Ecology | 3 |
| ESPM 164 | GIS and Environmental Science | 3 |
| ESPM 172 | Remote Sensing of the Environment | 3 |
| ESPM 173 | Introduction to Ecological Data Analysis | 3 |
| ESPM/LD ARCH C177 | GIS and Environmental Spatial Data Analysis | 4 |
| PB HLTH 177A | GIS and Spatial Analysis for Health Equity | 3 |
Human and Population Health
The goal of the domain emphasis in Human and Population Health is to expose students to questions, data structures, and methodology related to research in subject-matter areas such as epidemiology, environmental health, nutrition, toxicology, metabolic diseases, infectious diseases, and cancer. This includes the formulation of meaningful research questions, the development of sound study designs, data collection, exploratory data analysis, the application of pertinent statistical and computational methods, and the interpretation and validation of results.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| BIOLOGY 1A | General Biology Lecture | 3 |
| BIOLOGY 1B | General Biology Lecture and Laboratory | 4 |
| MCELLBI 50 | The Immune System and Disease | 4 |
| Upper Division (select two) | ||
| DEMOG 110 | Introduction to Population Analysis | 3 |
| INTEGBI 114 | Infectious Disease Dynamics | 4 |
| INTEGBI 116L | Medical Parasitology | 4 |
| INTEGBI 132 | Human Physiology | 4 |
| INTEGBI 137 | Human Endocrinology | 4 |
| INTEGBI 140 | Biology of Human Reproduction | 4 |
| MCELLBI 132 | Biology of Human Cancer | 4 |
| NUSCTX 110 | Course Not Available | 4 |
| NUSCTX 121 | Course Not Available | 3 |
| NUSCTX 160 | Metabolic Bases of Human Health and Diseases | 4 |
| 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 181 | Poverty and Population | 3 |
Human Behavior and Psychology
The domain emphasis in Human Behavior and Psychology engages students with fundamental aspects of individual and group behavior and the factors and processes that influence it, as explored in the cognitive, behavioral, and economic sciences.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| COG SCI 1/1B/N1 | Introduction to Cognitive Science | 4 |
| PSYCH 1 | General Psychology | 3 |
| PSYCH 2 | Principles of Psychology | 3 |
| Upper Division (select two) | ||
| COG SCI C131/PSYCH C123 | Computational Models of Cognition | 4 |
| ECON C110/POL SCI C135 | Game Theory in the Social Sciences | 4 |
| ECON 119 | Psychology and Economics | 4 |
| PSYCH 101D | Data Science for Research Psychology | 4 |
| PSYCH 110 | Introduction to Biological Psychology | 3 |
| PSYCH 124 | The Evolution of Human Behavior | 3 |
| PSYCH 130 | Clinical Psychology | 3 |
| PSYCH 134 | Health Psychology | 3 |
| or PSYCH N134 | Health Psychology | |
| PSYCH 140 | Developmental Psychology | 3 |
| PSYCH 150 | Psychology of Personality | 3 |
| PSYCH 156 | Human Emotion | 3 |
| PSYCH 160 | Social Psychology | 3 |
| or SOCIOL 150 | Social Psychology | |
| PSYCH 167AC | Stigma and Prejudice | 3 |
| UGBA 160 | Customer Insights | 3 |
Inequalities in Society
The Inequalities in Society domain emphasis explores the nature, causes, and consequences of social inequalities, with special attention to race and ethnicity, social class, and gender. Students will develop an understanding of how scientists conceptualize and study social inequalities and the methodological tools they use to do so.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| DATA C4AC | Data and Justice | 4 |
| SOCIOL 1 | Introduction to Sociology | 4 |
| SOCIOL 3AC | Principles of Sociology: American Cultures | 4 |
| Upper Division (select two) | ||
| AFRICAM 101 | Research Methods for African American Studies | 4 |
| or ETH STD 101A | Social Science Methods in Ethnic Studies | |
| AFRICAM 111 | Race, Class, and Gender in the United States | 3 |
| GEOG C155/AFRICAM C156 | Race, Space, and Inequality | 4 |
| GWS 131 | Gender and Science | 4 |
| PHILOS 117AC | The Philosophy of Race, Ethnicity, and Citizenship | 4 |
| POL SCI 167 | Racial and Ethnic Politics in the New American Century | 4 |
| POL SCI 132C | Berkeley Changemaker: Algorithms, Public Policy, and Ethics | 4 |
| PSYCH 167AC | Stigma and Prejudice | 3 |
| PUB POL C103 | Wealth and Poverty | 4 |
| PUB POL 117AC | Race, Ethnicity, and Public Policy | 4 |
| SOCIOL 111AC | Sociology of the Family | 4 |
| SOCIOL 113 | Sociology of Education | 4 |
| SOCIOL 113AC | Sociology of Education | 4 |
| SOCIOL 124 | Sociology of Poverty | 4 |
| SOCIOL 127 | Development and Globalization | 4 |
| SOCIOL 130 | Social Inequalities | 4 |
| SOCIOL 130AC | Social Inequalities: American Cultures | 4 |
| SOCIOL 131AC | Race and Ethnic Relations: U.S. American Cultures | 4 |
| SOCIOL 133 | Sociology of Gender | 4 |
Linguistic Sciences
The domain emphasis in Linguistic Sciences explores the data-driven analysis of language. Topics include linguistic structure (phonetics, phonology, morphology, syntax), logic and the philosophy of language, natural language processing, and empirical approaches to reasoning about language as data.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| LINGUIS 100 | Introduction to Linguistic Science * | 4 |
| PHILOS 12A | Introduction to Logic | 4 |
| Upper Division (select two) | ||
| LINGUIS 100 | Introduction to Linguistic Science * | 4 |
| LINGUIS 108 | Psycholinguistics | 3 |
| LINGUIS 110 | Phonetics | 4 |
| LINGUIS 111 | Phonology | 4 |
| LINGUIS 113 | Experimental Phonetics | 3 |
| LINGUIS 115 | Morphology | 4 |
| LINGUIS 120 | Syntax | 4 |
| LINGUIS 121 | Formal Semantics | 4 |
| LINGUIS/COG SCI C142 | Language and Thought | 3 |
| LINGUIS C160/COG SCI C140 | Quantitative Methods in Linguistics | 4 |
| LINGUIS 188 | LINGUISTIC DATA | 3 |
| INFO 159 | Natural Language Processing | 4 |
| PHILOS 133 | Philosophy of Language | 4 |
- *
May count toward the lower-division or upper-division requirement, but not both. Students may fulfill this domain emphasis by completing LINGUIS 100 plus two additional upper-division courses from the list, without taking a lower-division course. Please note that there are a limited number of courses approved for this domain emphasis that can be taken without LINGUIS 100 as a prerequisite.
Neurosciences
The Neuroscience domain emphasis provides students with expertise in models and methods of computational neuroscience, including data analysis and theoretical models of information processing in the brain. Students with this emphasis will be able to apply statistical analyses to extract patterns embedded in high-dimensional neuroscience datasets (multi-unit recordings, optical imaging, EEG, fMRI), and develop computational models toward elucidating neural mechanisms of information processing in the brain.
| Code | Title | Units |
|---|---|---|
| PSYCH C61 | Brain, Mind, and Behavior | 3 |
| PSYCH C64 | Exploring the Brain: Introduction to Neuroscience | 3 |
| Upper Division (select two) | ||
| ANTHRO 107 | Evolution of the Human Brain | 4 |
| COG SCI C127 | Cognitive Neuroscience | 3 |
| INTEGBI 139 | The Neurobiology of Stress | 4 |
| MCELLBI 160 | Cellular and Molecular Neurobiology | 4 |
| NEU 100A | Cellular and Molecular Neurobiology | 4 |
| NEU 100B | Circuit, Systems and Behavioral Neuroscience | 4 |
| NEU 165 | Neurobiology of Disease | 3 |
| PSYCH C113/INTEGBI C143A | Biological Clocks: Physiology and Behavior | 3 |
| PSYCH 117 | Human Neuropsychology | 3 |
| PSYCH 125 | The Developing Brain | 3 |
Organizations and the Economy
The domain emphasis in Organizations and the Economy explores the social construction of markets and the role of organizations and institutions in the contemporary economy. How can we understand the economic behavior of firms and governments? What is the nature of work in modern capitalism?
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| DATA C4AC | Data and Justice | 4 |
| SOCIOL 1 | Introduction to Sociology | 4 |
| SOCIOL 3AC | Principles of Sociology: American Cultures | 4 |
| Upper Division (select two) | ||
| ECON 121 | Industrial Organization and Public Policy | 4 |
| ECON 131 | Public Economics | 4 |
| ENVECON 142 | Industrial Organization with Applications to Agriculture and Natural Resources | 4 |
| GEOG 110 | Critical Economic Geographies | 4 |
| GWS 139 | Why Work? Gender and Labor Under Capitalism | 4 |
| POL SCI 132C | Berkeley Changemaker: Algorithms, Public Policy, and Ethics | 4 |
| SOCIOL 110 | Organizations and Social Institutions | 4 |
| SOCIOL 116 | Sociology of Work | 4 |
| SOCIOL 119S | Organizational Strategy and Design: A Sociological Perspective | 4 |
| SOCIOL 120 | Economy and Society | 4 |
| SOCIOL 121 | Innovation and Entrepreneurship: Social and Cultural Context | 4 |
| UGBA 105 | Leading People | 3 |
| UGBA 107 | The Social, Political, and Ethical Environment of Business | 3 |
Philosophical Foundations: Evidence and Inference
When do data confirm a hypothesis or a theory? What do we do when several different hypotheses or theories are consistent with the data? When, if ever, is inductive inference justified? How are models related to what they model? When is reasoning good reasoning? Which conclusions can be inferred from which premises? How does it depend on what we are reasoning about: arithmetic, the physical world, what exists, what is possible, what is known? What are we saying when we say that something is likely or unlikely to occur? What are we saying when we say that one event caused another? Are we saying something about the world or merely something about us, about what we have observed and what we now expect?
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| L & S 22 | Sense and Sensibility and Science | 4 |
| MATH 55 | Discrete Mathematics | 4 |
| PHILOS 4 | Knowledge and Its Limits | 4 |
| PHILOS 5 | Science and Human Understanding | 4 |
| PHILOS 12A | Introduction to Logic | 4 |
| Upper Division (select two) | ||
| MATH 125A | Mathematical Logic | 4 |
| MATH 135 | Introduction to the Theory of Sets | 4 |
| MATH 136 | Incompleteness and Undecidability | 4 |
| PHILOS 122 | Theory of Knowledge | 4 |
| PHILOS 125 | Metaphysics | 4 |
| PHILOS 128 | Philosophy of Science | 4 |
| PHILOS 134 | Form and Meaning | 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 |
| PHILOS 148 | Probability and Induction | 4 |
| PHILOS 149 | Special Topics in Philosophy of Logic and Mathematics | 4 |
| RHETOR 107 | Rhetoric of Scientific Discourse | 4 |
Philosophical Foundations: Minds, Morals, and Machines
Can machines think? Can they be conscious? Do they have rights? To answer these questions, we need to understand the nature of thought and consciousness is, and the basis of rights. In virtue of what do we count as thinking or conscious? In virtue of what do we have rights? Increasingly, algorithms are replacing human beings as decision makers. When are algorithmic decisions fair? Are we entitled to an explanation of algorithmic decisions? Is it paternalistic or anti-democratic to design algorithms that don’t give you what you want, if that will mislead you or make you unhappy?
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| COG SCI 1/1B/N1 | Introduction to Cognitive Science | 4 |
| PHILOS 2 | Individual Morality and Social Justice | 4 |
| PHILOS 3 | The Nature of Mind | 4 |
| PHILOS 14 | Philosophy of Artificial Intelligence | 4 |
| Upper Division (select two) | ||
| COG SCI C100/PSYCH C120 | Basic Issues in Cognition | 3 |
| COG SCI C101/LINGUIS C105 | Cognitive Linguistics | 4 |
| COG SCI C131/PSYCH C123 | Computational Models of Cognition | 4 |
| COG SCI/LINGUIS C142 | Language and Thought | 3 |
| ECON C110/POL SCI C135 | Game Theory in the Social Sciences | 4 |
| STAT 155 | Game Theory | 3 |
| PHILOS 104 | Ethical Theories | 4 |
| PHILOS 115 | Political Philosophy | 4 |
| PHILOS 132 | Philosophy of Mind | 4 |
| PHILOS 133 | Philosophy of Language | 4 |
| PHILOS 135 | Theory of Meaning | 4 |
| PHILOS 136 | Philosophy of Perception | 4 |
| PHILOS 141 | Philosophy and Game Theory | 4 |
Physical Science Analytics
The Physical Science Analytics domain emphasis allows students to explore ways that data analytics, inference, computational simulation and modeling, uncertainty analysis, and prediction arise in physical science and engineering domains.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| PHYSICS 5BL & PHYSICS 5CL | Introduction to Experimental Physics I and Introduction to Experimental Physics II | 2 |
| PHYSICS 7A | Physics for Scientists and Engineers | 4 |
| PHYSICS 77 | Introduction to Computational Techniques in Physics | 3 |
| Upper Division (select two) | ||
| ASTRON 120 | Optical and Infrared Astronomy Laboratory | 4 |
| ASTRON 121 | Radio Astronomy Laboratory | 4 |
| ASTRON 128 | Astronomy Data Science Laboratory | 4 |
| ASTRON C161 | Relativistic Astrophysics and Cosmology | 4 |
| ASTRON C162 | Planetary Astrophysics | 4 |
| CIV ENG C133/MEC ENG C180 | Engineering Analysis Using the Finite Element Method | 3 |
| ENGIN 150 | Basic Modeling and Simulation Tools for Industrial Research Applications | 4 |
| EPS 108 | Geodynamics | 4 |
| EPS 109 | Computer Simulations with Jupyter Notebooks | 4 |
| EPS 122 | Physics of the Earth and Planetary Interiors | 3 |
| EPS C183/ESPM C170 | Carbon Cycle Dynamics | 3 |
| GEOG C136/ESPM C130 | Terrestrial Hydrology | 4 |
| GEOG C139/EPS C181 | Atmosphere, Ocean, and Climate Dynamics | 3 |
| NUC ENG 101 | Nuclear Reactions and Radiation | 4 |
| NUC ENG 130 | Analytical Methods for Non-proliferation | 3 |
| NUC ENG 155 | Introduction to Numerical Simulations in Radiation Transport | 3 |
| PHYSICS 105 | Analytic Mechanics | 4 |
| PHYSICS 111A | Instrumentation Laboratory | 4 |
| PHYSICS 112 | Introduction to Statistical and Thermal Physics | 4 |
| PHYSICS 129 | Particle Physics | 4 |
| PHYSICS 188 | Bayesian Data Analysis and Machine Learning for Physical Sciences | 4 |
Quantitative Social Science
The Quantitative Social Science domain emphasis provides students with expertise in various methodologies used in quantitative social science research and analysis. Topics include mathematical modeling, description of patterns and trends, statistical modeling, and testing of social scientific hypotheses.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| ECON 1 | Introduction to Economics | 4 |
| or ECON 2 | Introduction to Economics--Lecture Format | |
| SOCIOL 1 | Introduction to Sociology | 4 |
| SOCIOL 3AC | Principles of Sociology: American Cultures | 4 |
| SOCIOL 5 | Evaluation of Evidence | 4 |
| POL SCI 3 | Introduction to Empirical Analysis and Quantitative Methods | 4 |
| POL SCI 88 | The Scientific Study of Politics | 2 |
| POL SCI 132C | Berkeley Changemaker: Algorithms, Public Policy, and Ethics | 4 |
| Upper Division (select two) | ||
| DEMOG 110 | Introduction to Population Analysis | 3 |
| DEMOG/SOCIOL C126 | Sex, Death, and Data | 4 |
| DEMOG/ECON C175 | Economic Demography | 4 |
| DEMOG 180 | Social Networks | 4 |
| ECON C110/POL SCI C135/W135 | Game Theory in the Social Sciences | 4 |
| ENVECON/IAS C118 | Introductory Applied Econometrics | 4 |
| MEDIAST 130 | Research Methods in Media Studies | 4 |
| POL SCI 132B | Machine Learning for Social Scientists | 4 |
| POL SCI 133 | Selected Topics in Quantitative Methods | 4 |
| SOCIOL 106 | Quantitative Sociological Methods | 4 |
Robotics
The goal of the domain emphasis in Robotics is to provide a pathway into the field of robotics, which includes the design and control of robots as well as the study of relationships between robots and nature. Topics include manipulation and control, decision making grounded in the physical world, embedded systems, mechatronics, and human-robot interaction.
| Code | Title | Units |
|---|---|---|
| Lower Division | ||
| MATH 53 | Multivariable Calculus | 4 |
| Upper Division (select two) | ||
| BIO ENG 101 | Instrumentation in Biology and Medicine | 4 |
| BIO ENG 105 | Engineering Devices 1 | 4 |
| BIO ENG/EECS C106A | Introduction to Robotics | 4 |
| BIO ENG/EECS C106B | Robotic Manipulation and Interaction | 4 |
| COMPSCI 188 | Introduction to Artificial Intelligence | 4 |
| EECS 149 | Introduction to Embedded and Cyber Physical Systems | 4 |
| EL ENG 143 | Microfabrication Technology | 4 |
| EL ENG 147 | Introduction to Microelectromechanical Systems (MEMS) | 3 |
| EL ENG 192 | Mechatronic Design Laboratory | 4 |
| INTEGBI C135L | Laboratory in the Mechanics of Organisms | 3 |
| MEC ENG 100 | Electronics for the Internet of Things | 4 |
| MEC ENG 102B | Mechatronics Design | 4 |
| MEC ENG 119 | Introduction to MEMS (Microelectromechanical Systems) | 3 |
| MEC ENG 131 | Vehicle Dynamics and Control | 4 |
| MEC ENG 132 | Dynamic Systems and Feedback | 3 |
| MEC ENG C134/EL ENG C128 | Feedback Control Systems | 4 |
| MEC ENG 135 | Design of Microprocessor-Based Mechanical Systems | 4 |
| MEC ENG 139 | Robotic Locomotion | 4 |
| MEC ENG 150 | Modeling and Simulation of Advanced Manufacturing Processes | 3 |
Science, Technology, and Society
The Science, Technology, and Society (STS) domain emphasis provides students with critical capacities to engage with a world shaped by science, technology, and medicine. It explores how these fields are constructed, contingent, and contested and how they interact with institutions, policy, and various forms of global social inequality.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| DATA C4AC | Data and Justice | 4 |
| GEOG 80 | An Introduction to Geospatial Technologies: Mapping, Space and Power | 4 |
| HISTORY 30 | Science and Society | 4 |
| ISF 60 | Technology and Values | 3 |
| Upper Division (select two) | ||
| ANTHRO 115 | Introduction to Medical Anthropology | 4 |
| ANTHRO 119 | Special Topics in Medical Anthropology | 4 |
| ANTHRO 168 | Anthropology of Science, Technology and Data | 4 |
| ENGIN/IAS 157AC | Engineering, The Environment, and Society | 4 |
| ENGLISH 180Z | Science Fiction | 4 |
| ENVECON 143 | Economics of Innovation and Intellectual Property | 4 |
| ESPM 161 | Environmental Philosophy and Ethics | 4 |
| ESPM 162 | Bioethics and Society | 4 |
| ESPM 163AC/SOCIOL 137AC | Environmental Justice: Race, Class, Equity, and the Environment | 4 |
| FILM 155 | Media Technologies | 4 |
| GEOG 130/N130 | Food and the Environment | 4 |
| GWS 130AC | Gender, Race, Nation, and Health | 4 |
| HISTORY 100S/100ST | Special Topics in the History of Science | 4 |
| HISTORY 103S | Proseminar: Problems in Interpretation in the Several Fields of History: History of Science | 4 |
| HISTORY 138/138T | History of Science in the U.S. | 4 |
| HISTORY 180/180T | The Life Sciences since 1750 | 4 |
| HISTORY 182A/182AT | Science, Technology, and Society | 4 |
| INFO 103 | History of Information | 4 |
| ISF 100D | Introduction to Technology, Society, and Culture | 4 |
| ISF 100G | Introduction to Science, Society, and Ethics | 4 |
| POL SCI 132C | Berkeley Changemaker: Algorithms, Public Policy, and Ethics | 4 |
| RHETOR 107 | Rhetoric of Scientific Discourse | 4 |
| RHETOR 115 | Technology and Culture | 4 |
| RHETOR 145 | Science, Narrative, and Image | 4 |
| SOCIOL C115/PB HLTH C155 | Sociology of Health and Medicine | 4 |
| SOCIOL 166 | Society and Technology | 4 |
| SOCIOL 167 | Virtual Communities/Social Media | 4 |
| STS C100/HISTORY C182C/ISF C100G | Introduction to Science, Technology, and Society | 4 |
| UGIS 110 | Introduction to Disability Studies | 3 |
| One additional course that meets the Data Science Human Contexts & Ethics requirement may be counted toward the Domain Emphasis in STS. If counted toward the STS DE, this course may not be used to satisfy the HCE requirement: | ||
| AMERSTD/AFRICAM C134 | Information Technology and Society | 4 |
| BIO ENG 100 | Ethics in Science and Engineering | 3 |
| CY PLAN 101 | Introduction to Urban Data Analytics | 4 |
| DATA C104/HISTORY C184D/STS C104D | Human Contexts and Ethics of Data - DATA/History/STS | 4 |
| DIGHUM 100 | Theory and Method in the Digital Humanities | 3 |
| ESPM C167/PB HLTH C160 | Environmental Health and Development | 4 |
| INFO 188 | Behind the Data: Humans and Values | 3 |
| ISF 100J | The Social Life of Computing | 4 |
| NWMEDIA 151AC | Transforming Tech: Issues and Interventions in STEM and Silicon Valley | 4 |
| PHILOS 121 | Moral Questions of Data Science | 4 |
Social Welfare, Health, and Poverty
The goal of the domain emphasis in Social Welfare, Health, and Poverty is to expose students to questions, data structures, and methodology related to research in the subject-matter areas of social welfare, health, and poverty. This includes the formulation of meaningful research questions, the development of sound study designs, data collection, exploratory data analysis, the application of pertinent statistical and computational methods, and the interpretation and validation of results.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| DATA C4AC | Data and Justice | 4 |
| SOCIOL 1 | Introduction to Sociology | 4 |
| SOCIOL 3AC | Principles of Sociology: American Cultures | 4 |
| Upper Division (select two) | ||
| ENVECON 153 | Population, Environment, and Development | 3 |
| GPP 105 | The Ethics, Methods, and Pragmatics of Global Practice | 4 |
| GPP 115 | Global Poverty: Challenges and Hopes | 4 |
| GLOBAL 102 | Critical Thinking In Global Studies | 4 |
| GWS 130AC | Gender, Race, Nation, and Health | 4 |
| PB HLTH 112 | Global Health: A Multidisciplinary Examination | 4 |
| PB HLTH 126 | Health Economics and Public Policy | 3 |
| PB HLTH 150D | Introduction to Health Policy and Management | 3 |
| PB HLTH C155/SOCIOL C115 | Sociology of Health and Medicine | 4 |
| PB HLTH C150E/CY PLAN C117 | Urban and Community Health | 3 |
| PB HLTH C160/ESPM C167 | Environmental Health and Development | 4 |
| PB HLTH 181 | Poverty and Population | 3 |
| POL SCI 132C | Berkeley Changemaker: Algorithms, Public Policy, and Ethics | 4 |
| POLECON 111 | Poverty and Social Policy | 3 |
| SOCIOL 115G | Health in a Global Society | 4 |
| SOCIOL 127 | Development and Globalization | 4 |
| SOC WEL 112 | Social Welfare Policy | 3 |
Social Policy and Law
The Social Policy and Law domain emphasis explores the foundations of legal institutions and its intersection with the history and analysis of social policy. Students can study the social construction of law, the nature of the criminal justice system, and the origins of contemporary social policies, such as health, welfare, and crime policies.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| DATA C4AC | Data and Justice | 4 |
| SOCIOL 1 | Introduction to Sociology | 4 |
| SOCIOL 3AC | Principles of Sociology: American Cultures | 4 |
| Upper Division (select two) | ||
| GWS 132AC | Gender, Race, and Law | 4 |
| LEGALST 100 | Foundations of Legal Studies | 4 |
| LEGALST 102 | Policing and Society | 4 |
| LEGALST 123 | Data, Prediction & Law | 4 |
| LEGALST 158 | Law and Development | 4 |
| LEGALST 160 | Punishment, Culture, and Society | 4 |
| PB HLTH 150D | Introduction to Health Policy and Management | 3 |
| POLECON 111 | Poverty and Social Policy | 3 |
| POL SCI 132C | Berkeley Changemaker: Algorithms, Public Policy, and Ethics | 4 |
| POL SCI 186 | Public Problems | 4 |
| PUB POL 101 | Introduction to Public Policy Analysis | 4 |
| SOC WEL 112 | Social Welfare Policy | 3 |
| SOC WEL 181 | Social Science and Crime Prevention Policy | 3 |
| SOCIOL 114 | Sociology of Law | 4 |
| SOCIOL 148 | Social Policy | 4 |
Sustainable Development and Engineering
The domain emphasis in Sustainable Development and Engineering explores research in environmental science, sustainable engineering, climate change, transportation systems, and water resources. Data science topics include data-driven modeling, environmental decision-making, and spatial-data analysis.
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| CIV ENG 11 | Engineered Systems and Sustainability | 3 |
| LD ARCH 12 | Environmental Science for Sustainable Development | 4 |
| Upper Division (select two) | ||
| ARCH 140 | Energy and Environment | 4 |
| CIV ENG 107 | Climate Change Mitigation | 3 |
| CIV ENG 110 | Water Systems of the Future | 3 |
| CIV ENG 111 | Environmental Engineering | 3 |
| CIV ENG 155 | Transportation Systems Engineering | 3 |
| CIV ENG 191 | Civil and Environmental Engineering Systems Analysis | 3 |
| ENE,RES 131 | Data, Environment and Society | 4 |
| ESPM C133/GEOG C135 | Water Resources and the Environment | 3 |
| ESPM/LD ARCH C177 | GIS and Environmental Spatial Data Analysis | 4 |
| LD ARCH 122 | Hydrology for Planners | 4 |
Urban Science
The Urban Science domain emphasis explores the theories and methods used to understand the deep structure of how cities function and the potential of urban policies and planning to shape more equitable futures. Topics include sustainability, mapping, visualization, design, urban economic analysis, smart urbanism, metropolitan structure, urban communities, and place-making, among others
| Code | Title | Units |
|---|---|---|
| Lower Division (select one) | ||
| CIV ENG C88 | Data Science for Smart Cities | 2 |
| ENV DES 4B | Global Cities | 3 |
| GEOG 70AC | The Urban Experience: Race, Class, Gender & The American City | 4 |
| Upper Division (select two) | ||
| ARCH 110AC | The Social and Cultural Processes in Architecture & Urban Design | 3 |
| CY PLAN 110 | Introduction to City Planning | 4 |
| CY PLAN 113A | Economic Analysis for Planning | 3 |
| CY PLAN 114 | Introduction to Urban and Regional Transportation | 3 |
| CY PLAN 119 | Planning for Sustainability | 4 |
| CY PLAN 140 | Urban Design: City-Building and Place-Making | 3 |
| ENE,RES 131 | Data, Environment and Society | 4 |
| ENV DES 100 | The City: Theories and Methods in Urban Studies | 4 |
| ENV DES 102 | Climate Change and City Planning: Adaptation and Resilience | 3 |
| GEOG 181 | Urban Field Study | 4 |
| GEOG 182 | Field Study of Buildings and Cities | 3 |
| LD ARCH 130 | Sustainable Landscapes and Cities | 4 |
| LD ARCH/GEOG C188 | Geographic Information Science | 4 |
| LD ARCH 187 | Representation as Research: Contemporary Topics in Landscape Visualization | 3 |
| SOCIOL 136 | Urban Sociology | 4 |
Minor Requirements
The Minor in Data Science at UC Berkeley aims to provide students with practical knowledge of the methods and techniques of data analysis, as well as the ability to think critically about the construction and implications of data analysis and models. The minor will empower students across the wide array of campus disciplines with a working knowledge of statistics, probability, and computation that allow students not just to participate in data science projects, but to design and carry out rigorous computational and inferential analysis for their field of interest.
General Guidelines
-
All minors must be declared prior to the first day of classes of the student's Expected Graduation Term (EGT). If the student's EGT is a summer term, the deadline to declare a minor is prior to the first day of classes of Summer Session A. To declare a minor, contact the department advisor for information on requirements, and the declaration process.
-
All courses for the minor must be taken for a letter grade.
-
Students must earn a C- or better in each course, and have a minimum 2.0 GPA in all courses towards the minor.
-
Students may overlap up to 1 course in the upper division requirements for the Data Science minor with each of their majors (for example, a Computer Science major may count COMPSCI/DATA/STAT C100 toward both their major and the Data Science minor).
-
A maximum of one course offered by or cross-listed with the student’s major department(s) may count toward the data science minor upper-division requirements, including any overlapping course (for example, if a Computer Science major takes COMPSCI/DATA/STAT C100 toward the Data Science minor, this is the only COMPSCI, ELENG, or EECS course which may count toward the upper-division requirements for the minor).
-
An upper-division course used to fulfill a lower-division requirement (for example, Stat 134 to fulfill the probability requirement) will not be counted toward the maximum 1 course allowed to overlap with the major, nor will it fulfill one of the four upper division course requirements.
-
There is no restriction on overlap with another minor.
-
Courses used to fulfill the minor requirements may be applied toward the Seven-Course Breadth requirement.
-
All minor requirements must be completed prior to the last day of finals during the semester in which you plan to graduate.
Lower-division Requirements
| Code | Title | Units |
|---|---|---|
| DATA/COMPSCI/STAT/INFO C8 | Foundations of Data Science 1 | 4 |
| or STAT 20 | Introduction to Probability and Statistics | |
| DATA/COMPSCI C88C | Computational Structures in Data Science | 3-4 |
| or COMPSCI 61A | The Structure and Interpretation of Computer Programs | |
| or ENGIN 7 | Introduction to Computer Programming and Numerical Methods | |
| Choose one of the following: 2 | ||
| DATA/STAT C88S | Probability and Mathematical Statistics in Data Science | 3-4 |
| or COMPSCI 70 | Discrete Mathematics and Probability Theory | |
| or MATH 10B | Methods of Mathematics: Calculus, Statistics, and Combinatorics | |
| or MATH 55 | Discrete Mathematics | |
| or CIV ENG 93 | Engineering Data Analysis | |
- 1
Students may substitute Stat 20 for Data C8 toward the Data Science minor when combined with CS 61A or CS 88/Data C88C; this option is not available for students who take Engin 7 for their Program Structures requirement.
- 2
Stat 134, Data C140, Ind Eng 172, EECS 126 or Math 106 may be substituted for the probability requirement.
Upper-division Requirements
Complete a total of 4 upper-division courses in one of the following pathways:
1-Core course Pathway
| Code | Title | Units |
|---|---|---|
| DATA/COMPSCI/STAT C100 | Principles & Techniques of Data Science | 4 |
| Choose one of the following: | ||
| Information Technology and Society [4] | ||
or AFRICAM 134 | Information Technology and Society | |
| Ethics in Science and Engineering [3] | ||
| Introduction to Urban Data Analytics [4] | ||
| Human Contexts and Ethics of Data - DATA/History/STS [4] | ||
| Theory and Method in the Digital Humanities [3] | ||
| Environmental Health and Development [4] | ||
| Behind the Data: Humans and Values [3] | ||
| The Social Life of Computing [4] | ||
| Transforming Tech: Issues and Interventions in STEM and Silicon Valley [4] | ||
| Moral Questions of Data Science [4] | ||
If completing the 1-core course pathway, choose TWO from the Approved Elective List.
2-core course PATHWAY
| Code | Title | Units |
|---|---|---|
| DATA/STAT C131A | Statistical Methods for Data Science | 4 |
| STAT 133 | Concepts in Computing with Data | 3 |
| Choose one of the following: | ||
| Information Technology and Society [4] | ||
or AFRICAM 134 | Information Technology and Society | |
| Ethics in Science and Engineering [3] | ||
| Introduction to Urban Data Analytics [4] | ||
| Human Contexts and Ethics of Data - DATA/History/STS [4] | ||
| Theory and Method in the Digital Humanities [3] | ||
| Environmental Health and Development [4] | ||
| Behind the Data: Humans and Values [3] | ||
| The Social Life of Computing [4] | ||
| Transforming Tech: Issues and Interventions in STEM and Silicon Valley [4] | ||
| Moral Questions of Data Science [4] | ||
If completing the 2-core course pathway, choose ONE from the Approved Elective List.
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.
Plans of Study
Sample plans for completing major coursework are included below. These are not comprehensive plans which will reflect the situation of every student. These sample plans are meant only to serve as a baseline guide for structuring a plan of study, and only include the minimum courses for meeting the Data Science major requirements.
For new freshmen (four-year plan):
| Freshman | |||
|---|---|---|---|
| Fall | Units | Spring | Units |
| DATA C8 | 4 | COMPSCI 61A or DATA C88C | 3-4 |
| MATH 1A (10A or 16A acceptable) | 4 | MATH 1B | 4 |
| Reading & Composition A | 4 | Reading & Composition B | 4 |
| Elective | 2 | Non-major Elective | 1-2 |
| 14 | 12-14 | ||
| Sophomore | |||
| Fall | Units | Spring | Units |
| COMPSCI 61B | 4 | MATH 54 or 56 | 4 |
| Breadth/Elective | 3-4 | Lower-division Domain Emphasis | 3-4 |
| Breadth/Elective | 3-4 | Breadth/Elective | 3-4 |
| 10-12 | 10-12 | ||
| Junior | |||
| Fall | Units | Spring | Units |
| DATA C104 | 4 | DATA C100 | 4 |
| DATA C140 (or other approved Probability) | 4 | Computational & Inferential Depth #1 | 3-4 |
| undefined | 4 | Breadth/Elective | 3-4 |
| Breadth/Elective | 3-4 | ||
| 15-16 | 10-12 | ||
| Senior | |||
| Fall | Units | Spring | Units |
| Domain Emphasis Upper-division #1 | DATA C102 (or other approved MLDM) | 4 | |
| Computational & Inferential Depth #2 | 3-4 | Domain Emphasis Upper-division #2 | |
| Breadth/Elective | 3-4 | Breadth/Elective | 3-4 |
| 6-8 | 7-8 | ||
| Total Units: 84-96 | |||
For transfer students (two-year plan):
*Note: this sample plan is based on a transfer student who has completed 1 year of calculus, linear algebra and data structures, as well as IGETC/7-Course Breadth at their previous college or university, which may not reflect the reality for every transfer student. Students should consult with a Data Science Advisor to make an individualized plan based on their specific situation.
| First Year | |||
|---|---|---|---|
| Fall | Units | Spring | Units |
| DATA C8 | 4 | DATA C100 | 4 |
| Lower-division Domain Emphasis | 2-4 | DATA C140 (or other approved Probability) | 4 |
| DATA C88C or COMPSCI 61A | 3-4 | American Cultures/Upper-division Elective | 3-4 |
| Non-major Elective | 1-2 | Non-major Elective | 1-2 |
| 10-14 | 12-14 | ||
| Second Year | |||
| Fall | Units | Spring | Units |
| Computational & Inferential Depth #1 | 3-4 | DATA C102 (or other approved MLDM) | 4 |
| Domain Emphasis Upper-division #1 | 3-4 | DATA C104 (or other approved HCE) | 4 |
| Domain Emphasis Upper-division #2 | 3-4 | Computational & Inferential Depth #2 | 3-4 |
| Non-major Elective | 1-2 | Non-major Elective | 1-2 |
| 10-14 | 12-14 | ||
| Total Units: 44-56 | |||
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:
Academic Opportunities
Student Teams
Each semester, we recruit dozens of students to participate in our student teams as interns and volunteers, with opportunities to advance into team lead roles and other leadership positions. Teams include Communications, Operations, External Relations, and Curriculum Development. Interested students can email ds-teams@berkeley.edu with questions about the opportunities. Learn more here.
Data Scholars
The Data Scholars program addresses issues of underrepresentation in the data science community by establishing a welcoming, educational, and empowering environment for underrepresented and nontraditional students. The program, which offers specialized tutoring, advising, mentorship, and workshops, is especially suited for students who can bring diverse perspectives to the field of Data Science. Learn more here.
Data Science Peer Advising
Data Science Peer Advisors are available to help fellow students choose classes, explore academic interests, and learn how to declare the Data Science major and minor. The Data Science Peer Advising services are available on a drop-in basis. Contact the Data Science Peer Advisors at ds-peer-consulting@berkeley.edu. Learn more here.
Data Science Course Staff
Data Science Undergraduate Studies appoints graduate and undergraduate students to support its instructional programs. Our outstanding staff teams bear significant responsibility for our students’ experience and learning in Data classes. Staff team members also form strong bonds with each other, mentor junior members, and create staff networks for academic and professional development. Learn more here.
Contact Information
Data Science Undergraduate Studies; College of Computing, Data Science, and Society
Faculty Director
John DeNero
Faculty Director of Pedagogy
Ani Adhikari