Data Science

University of California, Berkeley

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.

VISIT PROGRAM WEBSITE

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

DATA/COMPSCI/STAT/INFO C8Foundations of Data Science 14
or STAT 20 Introduction to Probability and Statistics
MATH 1ACalculus3-4
or MATH 10A Methods of Mathematics: Calculus, Statistics, and Combinatorics
or MATH 16A Analytic Geometry and Calculus
MATH 1BCalculus4
MATH 54Linear Algebra and Differential Equations4
or MATH 56 Linear Algebra
or STAT 89A Linear Algebra for Data Science
or EECS 16A
EECS 16B
Designing Information Devices and Systems I
and Designing Information Devices and Systems II
or PHYSICS 89 Introduction to Mathematical Physics
COMPSCI 61AThe Structure and Interpretation of Computer Programs4
or DATA C88C Computational Structures in Data Science
or COMPSCI C88C Computational Structures in Data Science
or ENGIN 7 Introduction to Computer Programming for Scientists and Engineers
COMPSCI 61BData Structures4

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
DATA/COMPSCI/STAT C100Principles & Techniques of Data Science4
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. 

Choose two courses comprising 7+ units from the following:
ASTRON 128Astronomy Data Science Laboratory4
COMPSCI 161Computer Security4
COMPSCI 162Operating Systems and System Programming4
COMPSCI 164Programming Languages and Compilers4
COMPSCI 168Introduction to the Internet: Architecture and Protocols4
COMPSCI 169Course Not Available4
or COMPSCI 169A Introduction to Software Engineering
or COMPSCI W169A Software Engineering
COMPSCI 170Efficient Algorithms and Intractable Problems4
COMPSCI 186Introduction to Database Systems4
or COMPSCI W186 Introduction to Database Systems
COMPSCI 188Introduction to Artificial Intelligence4
DATA 101Course Not Available4
DATA 144Data Mining and Analytics3
ECON 140Econometrics4
or ECON 141 Econometrics (Math Intensive)
EECS 127Optimization Models in Engineering4
EL ENG 120Signals and Systems4
EL ENG 123Digital Signal Processing4
ENVECON C118Introductory Applied Econometrics4
ESPM 174Design and Analysis of Ecological Research4
IAS C118Introductory Applied Econometrics4
IND ENG 115Industrial and Commercial Data Systems3
IND ENG 135Applied Data Science with Venture Applications3
IND ENG 142BMachine Learning and Data Analytics II4
IND ENG 160Nonlinear and Discrete Optimization3
IND ENG 162Linear Programming and Network Flows3
IND ENG 164Introduction to Optimization Modeling3
IND ENG 165Engineering Statistics, Quality Control, and Forecasting4
IND ENG 166Decision Analytics3
IND ENG 173Introduction to Stochastic Processes3
INFO 159Natural Language Processing4
INFO 190Special Topics in Information (Introduction to Data Visualization - only when offered on this topic)4
NUC ENG 175Methods of Risk Analysis3
PHYSICS 188Bayesian Data Analysis and Machine Learning for Physical Sciences (previously PHYSICS 188)4
STAT 135Concepts of Statistics4
STAT 150Stochastic Processes3
STAT 151ALinear Modelling: Theory and Applications4
STAT 152Sampling Surveys4
STAT 153Introduction to Time Series4
STAT 158Experimental Design4
STAT 159Reproducible and Collaborative Statistical Data Science4
STAT 165Forecasting3
UGBA 142Advanced Business Analytics3
Probability

Students will be required to take one upper-division course on probability. 

Choose one of the following:
DATA/STAT C140Probability for Data Science4
MATH 106Mathematical Probability Theory4
EL ENG 126Probability and Random Processes4
IND ENG 172Probability and Risk Analysis for Engineers4
STAT 134Concepts of Probability4
Modeling, Learning, and Decision-Making

Students will be required to take one upper-division course on modeling, learning, and decision-making.

Choose one of the following:
COMPSCI 182Designing, Visualizing and Understanding Deep Neural Networks4
COMPSCI 189Introduction to Machine Learning4
DATA/STAT C102Data, Inference, and Decisions4
IND ENG 142AIntroduction to Machine Learning and Data Analytics4
or IND ENG 142 Introduction to Machine Learning and Data Analytics
STAT 154Modern Statistical Prediction and Machine Learning4
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. 

AFRICAM 134Information Technology and Society4
or AFRICAM/AMERSTD C134 Information Technology and Society
BIO ENG 100Ethics in Science and Engineering3
CY PLAN 101Introduction to Urban Data Analytics4
DATA C104/HISTORY C184D/STS C104DHuman Contexts and Ethics of Data - DATA/History/STS4
DIGHUM 100Theory and Method in the Digital Humanities3
INFO 188Behind the Data: Humans and Values3
ISF 100JThe Social Life of Computing4
NWMEDIA 151ACTransforming Tech: Issues and Interventions in STEM and Silicon Valley4
PHILOS 121Moral Questions of Data Science4
PB HLTH C160/ESPM C167Environmental Health and Development4

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:

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.

Lower Division (choose one)
MATH 53Multivariable Calculus4
MATH 55Discrete Mathematics4
Upper Division (choose two)
CIV ENG C133/MEC ENG C180Engineering Analysis Using the Finite Element Method3
EECS 127Optimization Models in Engineering4
ENGIN 150Basic Modeling and Simulation Tools for Industrial Research Applications4
IND ENG 160Nonlinear and Discrete Optimization3
IND ENG 162Linear Programming and Network Flows3
MATH 104Introduction to Analysis4
MATH 110Abstract Linear Algebra4
MATH 113Introduction to Abstract Algebra4
MATH 118Fourier Analysis, Wavelets, and Signal Processing4
MATH 128ANumerical Analysis4
COMPSCI C267/ENGIN C233Applications of Parallel Computers3
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 128BNumerical Analysis4
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.

Lower Division (select one)
ECON 1Introduction to Economics4
ECON 2Introduction to Economics--Lecture Format4
MATH 53Multivariable Calculus4
Upper Division (select two)
IND ENG 115Industrial and Commercial Data Systems3
IND ENG 120Principles of Engineering Economics3
IND ENG 130Methods of Manufacturing Improvement3
IND ENG 153Logistics Network Design and Supply Chain Management3
IND ENG 166Decision Analytics3
UGBA 104Introduction to Business Analytics3
UGBA 134Introduction to Financial Engineering3
UGBA 141Production and Operations Management (when completed for 3 units)3
UGBA 142Advanced Business Analytics3
UGBA 161Market Research: Tools and Techniques for Data Collection and Analysis3
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.

Lower Division (select one)
COG SCI 1/1B/N1Introduction to Cognitive Science4
PSYCH C61Brain, Mind, and Behavior3
PSYCH C64Exploring the Brain: Introduction to Neuroscience3
Upper Division (select two)
COG SCI C100/PSYCH C120Basic Issues in Cognition3
COG SCI C101/LINGUIS C105Cognitive Linguistics4
COG SCI/PSYCH C126Perception3
COG SCI/PSYCH C127Cognitive Neuroscience3
COG SCI 131/PSYCH C123Computational Models of Cognition4
COG SCI 132Rhythms of the Brain: from Neuronal Communication to Function4
COG SCI 150Sensemaking and Organizing3
COG SCI 180Mind, Brain, and Identity3
COG SCI 190Special Topics in Cognitive Science (Data Science and Cognition -- only when offered with this topic)3
COMPSCI 188Introduction to Artificial Intelligence4
MUSIC 108Music Perception and Cognition4
or MUSIC 108M Music Perception and Cognition
PSYCH 114Biology of Learning3
PSYCH 117Human Neuropsychology3
PSYCH 131Developmental Psychopathology3
PSYCH C143/LINGUIS C146Language Acquisition3
Computational Methods in Molecular and Genomic Biology
This domain emphasis will prepare students for work or graduate school in bioinformatics and computational biology with a focus on molecular biology and genomics. Students with this emphasis will be able to understand how computational and statistical methods are used to elucidate the mechanisms of cellular processing of genetic data and will prepare them for computational analyses of DNA sequencing data and other molecular biological data.
Lower Division (select one)
BIOLOGY 1AGeneral Biology Lecture3
BIOLOGY 1BGeneral Biology Lecture and Laboratory4
MATH 53Multivariable Calculus4
Upper Division (select two)
BIO ENG 131/CMPBIO C131Introduction to Computational Molecular and Cell Biology4
BIO ENG 134Genetic Design Automation4
BIO ENG 145Introduction to Machine Learning for Computational Biology4
CMPBIO 156Human Genome, Environment and Public Health4
CMPBIO/COMPSCI C176Algorithms for Computational Biology4
INTEGBI 120Introduction to Quantitative Methods In Biology4
INTEGBI 141Human Genetics3
or INTEGBI 164 Human Genetics and Genomics
or MCELLBI 149 The Human Genome
INTEGBI 161Population and Evolutionary Genetics4
MATH 127Mathematical and Computational Methods in Molecular Biology4
MCELLBI C100A/CHEM C130Biophysical Chemistry: Physical Principles and the Molecules of Life4
MCELLBI 102Survey of the Principles of Biochemistry and Molecular Biology4
MCELLBI 104Genetics, Genomics, and Cell Biology4
MCELLBI 132Biology of Human Cancer4
MCELLBI 137LPhysical Biology of the Cell4
MCELLBI 140General Genetics4
MCELLBI 143Evolution of Genomes, Cells, and Development3
MCELLBI/PLANTBI C148Microbial Genomics and Genetics4
MCELLBI 153Molecular Medicine4
PLANTBI 160Plant Molecular Genetics3
 
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

Lower Division (select one)
ART 23ACDIGITAL MEDIA: FOUNDATIONS4
HISTORY 88How Does History Count?2
L & S 88Data Science Connector (Rediscovering Text as Data (only when offered with this topic))2-4
L & S 88Data Science Connector (Aesthetics and Data (only when offered with this topic))2-4
MUSIC 30Computational Creativity for Music and the Arts4
RHETOR 10Introduction to Practical Reasoning and Critical Analysis of Argument4
Upper Division (select two)
DIGHUM 100Theory and Method in the Digital Humanities (summer only)3
DIGHUM 101Python Programming for Digital Humanities (summer only)3
DIGHUM 150ADigital Humanities and Archival Design (summer only)3
DIGHUM 150BDigital Humanities and Visual and Spatial Analysis (summer only)3
DIGHUM 150CDigital Humanities and Text and Language Analysis (summer only)3
DIGHUM 160Critical Digital Humanities (summer only)3
GLOBAL 140Special 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 C181Digital Humanities, Visual Cultures4
HISTORY 133DCalculating Americans: Big Histories of Small Data4
HISTART 190TTranscultural (VR and Its Prehistories (only when offered with this topic))4
HISTART 192DHUndergraduate Seminar: Digital Imaging and Forensic Art History4
INFO 103History of Information4
INFO 159Natural Language Processing4
INFO 190Special Topics in Information (Introduction to Data Visualization)4
MUSIC 158ASound and Music Computing with CNMAT Technologies4
MUSIC 159Computer Programming for Music Applications4
MELC 110Digital Humanities and Egyptology4
RHETOR 107Rhetoric of Scientific Discourse4
RHETOR 114Rhetoric of New Media4
RHETOR 115Technology and Culture4
RHETOR 137Rhetoric of the Image4
RHETOR 145Science, Narrative, and Image4
RHETOR 170Rhetoric of Social Science4
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 H110Honors Seminar: Special Topics in American Studies (Bay Area in the 1970s (only when offered with this topic))3-4
ENGLISH 166Special Topics (Slavery and Conspiracy (only when offered with this topic))4
HISTORY 100SSpecial Topics in the History of Science (Text Analysis for Digital Humanists and Social Scientists (only when offered with this topic))4
HISTORY 104The Craft of History4
THEATER 166/NWMEDIA 190Special Topics: Theater Arts ("Making Sense of Cultural Data (only when offered with this topic))1-4
MELC 114Beyond Wikipedia: The Ancient Middle East3
MELC 190ASpecial 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 135Studies 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

Lower Division (select one)
L & S/ESPM C46Climate Change and the Future of California4
EPS 80Environmental Earth Sciences3
ESPM 15Introduction to Environmental Sciences3
ESPM 2The Biosphere3
ESPM 6Environmental Biology3
ESPM 88BData Sciences in Ecology and the Environment2
GEOG 40Introduction to Earth System Science4
Upper Division (select two)
ENE,RES 102Quantitative Aspects of Global Environmental Problems4
ESPM 102B
102BL
Natural Resource Sampling
and Laboratory in Natural Resource Sampling
2, 2
ESPM C103/INTEGBI C156Principles of Conservation Biology4
ESPM 111Ecosystem Ecology4
ESPM/EPS C129Biometeorology3
ESPM 130AForest Hydrology4
ESPM/INTEGBI C153Ecology3
INTEGBI 170LFMethods in Population and Community Ecology3
ESPM 157Data Science in Global Change Ecology4
ESPM C170/EPS C183Carbon Cycle Dynamics3
ESPM 174AApplied Time Series Analysis for Ecology and Environmental Sciences3
CIV ENG C106/EPS C180/ESPM C180Air Pollution3
Economics
Lower Division (select one)
ECON 1Introduction to Economics4
ECON 2Introduction to Economics--Lecture Format4
DATA 88EEconomic Models2
Upper Division (select two)
ECON 100AMicroeconomics4
or ECON 101A Microeconomics (Math Intensive)
ECON/MATH C103Introduction to Mathematical Economics4
MATH C103Introduction to Mathematical Economics4
ECON 104Advanced Microeconomic Theory4
ECON C110/N110/POL SCI C135Game Theory in the Social Sciences4
ECON 119Psychology and Economics4
ECON 121Industrial Organization and Public Policy4
ECON C125/ENVECON C101Environmental Economics4
ECON 136Financial Economics4
ECON 139Asset Pricing and Portfolio Choice4
ECON 140Econometrics4
or ECON 141 Econometrics (Math Intensive)
ECON/PUB POL C142/POL SCI C131AApplied Econometrics and Public Policy4
ECON 148Data Science for Economists4
ECON/DEMOG C175Economic Demography4
ENVECON/IAS C118Introductory Applied Econometrics4
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.

Lower Division (select one)
ECON C3/ENVECON C1Introduction to Environmental Economics and Policy4
ESPM 50ACIntroduction to Culture and Natural Resource Management4
Upper Division (select two)
ENVECON 100Intermediate Microeconomics with Applications to Sustainability4
ENVECON C101/ECON C125Environmental Economics4
ENVECON C102Natural Resource Economics4
ENVECON C115/ESPM C104Modeling and Management of Biological Resources4
ENVECON 141Agricultural and Environmental Policy4
ENVECON 142Industrial Organization with Applications to Agriculture and Natural Resources4
ENVECON 145Health and Environmental Economic Policy4
ENVECON 147The Economics of the Clean Energy Transition4
ENVECON 153Population, Environment, and Development3
ENE,RES C100/PUB POL C184Energy and Society4
OR
Energy and Society [4]
ENE,RES 131Data, Environment and Society4
ENE,RES/ENVECON/IAS C176Climate Change Economics4
ESPM 102CResource Management4
ESPM 102DClimate and Energy Policy4
ESPM 151Society, Environment, and Culture4
ESPM 155ACSociology and Political Ecology of Agro-Food Systems4
ESPM 168Political Ecology4
ESPM 186Grassland and Woodland Management and Conservation4
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.

Lower Division (select one)
BIOLOGY 1AGeneral Biology Lecture3
BIOLOGY 1BGeneral Biology Lecture and Laboratory4
Upper Division (select two)
ESPM/INTEGBI C105Natural History Museums and Biodiversity Science3
ESPM 108BEnvironmental Change Genetics3
ESPM C125/GEOG C148/INTEGBI C166Biogeography4
ESPM 152Global Change Biology3
INTEGBI/PLANTBI C109Evolution and Ecology of Development3
INTEGBI 113LPaleobiological Perspectives on Ecology and Evolution4
INTEGBI 117
117LF
Medical Ethnobotany
and Medical Ethnobotany Laboratory
2
INTEGBI 141Human Genetics3
or INTEGBI 164 Human Genetics and Genomics
INTEGBI C160/MCELLBI C144Evolution4
or INTEGBI 167 Evolution and Earth History: From Genes to Fossils
INTEGBI 161Population and Evolutionary Genetics4
INTEGBI 162Ecological Genetics4
INTEGBI 169Evolutionary Medicine4
INTEGBI 172Coevolution: From Genes to Ecosystems4
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.

CIV ENG/CY PLAN C88Data Science for Smart Cities2
ESPM 72Introduction to Geographic Information Systems3
ESPM 88AExploring Geospatial Data2
EPS 50The Planet Earth4
GEOG 80An Introduction to Geospatial Technologies: Mapping, Space and Power4
GEOG 88Data Science Applications in Geography2
Upper Division (select two)
GEOG 183Cartographic Representation5
GEOG 185Earth System Remote Sensing3
GEOG 186Web Cartography5
GEOG 187Geographic Information Analysis4
GEOG/LD ARCH C188Geographic Information Science4
EPS 101Field Geology and Digital Mapping4
EPS 115Stratigraphy and Earth History4
ESPM 137Landscape Ecology3
ESPM 164GIS and Environmental Science3
ESPM 172Remote Sensing of the Environment3
ESPM 173Introduction to Ecological Data Analysis3
ESPM/LD ARCH C177GIS and Environmental Spatial Data Analysis4
PB HLTH 177AGIS and Spatial Analysis for Health Equity3
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.

Lower Division (select one)
BIOLOGY 1AGeneral Biology Lecture3
BIOLOGY 1BGeneral Biology Lecture and Laboratory4
MCELLBI 50The Immune System and Disease4
Upper Division (select two)
DEMOG 110Introduction to Population Analysis3
INTEGBI 114Infectious Disease Dynamics4
INTEGBI 116LMedical Parasitology4
INTEGBI 132Human Physiology4
INTEGBI 137Human Endocrinology4
INTEGBI 140Biology of Human Reproduction4
MCELLBI 132Biology of Human Cancer4
NUSCTX 110Toxicology4
NUSCTX 121Computational Toxicology3
NUSCTX 160Metabolic Bases of Human Health and Diseases4
PB HLTH 132Artificial Intelligence for Health and Healthcare3
PB HLTH 150AIntroduction to Epidemiology and Human Disease4
PB HLTH 150BHuman Health and the Environment in a Changing World3
PB HLTH 162APublic Health Microbiology4
PB HLTH 181Poverty and Population3
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.

Lower Division (select one)
COG SCI 1/1B/N1Introduction to Cognitive Science4
PSYCH 1General Psychology3
PSYCH 2Principles of Psychology3
Upper Division (select two)
COG SCI C131/PSYCH C123Computational Models of Cognition4
ECON C110/POL SCI C135Game Theory in the Social Sciences4
ECON 119Psychology and Economics4
PSYCH 101DData Science for Research Psychology4
PSYCH 110Introduction to Biological Psychology3
PSYCH 124The Evolution of Human Behavior3
PSYCH 130Clinical Psychology3
PSYCH 134Health Psychology3
or PSYCH N134 Health Psychology
PSYCH 140Developmental Psychology3
PSYCH 150Psychology of Personality3
PSYCH 156Human Emotion3
PSYCH 160Social Psychology3
or SOCIOL 150 Social Psychology
PSYCH 167ACStigma and Prejudice3
UGBA 160Customer Insights3
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.

Lower Division (select one)
DATA 4ACData and Justice4
SOCIOL 1Introduction to Sociology4
SOCIOL 3ACPrinciples of Sociology: American Cultures4
Upper Division (select two)
AFRICAM 101Research Methods for African American Studies4
or ETH STD 101A Social Science Methods in Ethnic Studies
AFRICAM 111Race, Class, and Gender in the United States3
GEOG C155/AFRICAM C156Race, Space, and Inequality4
GWS 131Gender and Science4
PHILOS 117ACThe Philosophy of Race, Ethnicity, and Citizenship4
POL SCI 167Racial and Ethnic Politics in the New American Century4
PSYCH 167ACStigma and Prejudice3
PUB POL C103Wealth and Poverty4
PUB POL 117ACRace, Ethnicity, and Public Policy4
SOCIOL 111ACSociology of the Family4
SOCIOL 113Sociology of Education4
SOCIOL 113ACSociology of Education4
SOCIOL 124Sociology of Poverty4
SOCIOL 127Development and Globalization4
SOCIOL 130Social Inequalities4
SOCIOL 130ACSocial Inequalities: American Cultures4
SOCIOL 131ACRace and Ethnic Relations: U.S. American Cultures4
SOCIOL 133Sociology of Gender4
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.

Lower Division (select one)
LINGUIS 100Introduction to Linguistic Science *4
PHILOS 12AIntroduction to Logic4
Upper Division (select two)
LINGUIS 100Introduction to Linguistic Science *4
LINGUIS 108Psycholinguistics3
LINGUIS 110Phonetics4
LINGUIS 111Phonology4
LINGUIS 113Experimental Phonetics3
LINGUIS 115Morphology4
LINGUIS 120Syntax4
LINGUIS 121Formal Semantics4
LINGUIS/COG SCI C142Language and Thought3
LINGUIS C160/COG SCI C140Quantitative Methods in Linguistics4
LINGUIS 188LINGUISTIC DATA3
INFO 159Natural Language Processing4
PHILOS 133Philosophy of Language4
*

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.

PSYCH C61Brain, Mind, and Behavior3
PSYCH C64Exploring the Brain: Introduction to Neuroscience3
Upper Division (select two)
ANTHRO 107Evolution of the Human Brain4
COG SCI C127Cognitive Neuroscience3
INTEGBI 139The Neurobiology of Stress4
MCELLBI 160Cellular and Molecular Neurobiology4
MCELLBI 166Biophysical Neurobiology3
PSYCH C113/INTEGBI C143ABiological Clocks: Physiology and Behavior3
PSYCH 117Human Neuropsychology3
PSYCH 125The Developing Brain3
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?

Lower Division (select one)
DATA 4ACData and Justice4
SOCIOL 1Introduction to Sociology4
SOCIOL 3ACPrinciples of Sociology: American Cultures4
Upper Division (select two)
ECON 121Industrial Organization and Public Policy4
ECON 131Public Economics4
ENVECON 142Industrial Organization with Applications to Agriculture and Natural Resources4
GEOG 110Critical Economic Geographies4
GWS 139Why Work? Gender and Labor Under Capitalism4
SOCIOL 110Organizations and Social Institutions4
SOCIOL 116Sociology of Work4
SOCIOL 119SOrganizational Strategy and Design: A Sociological Perspective4
SOCIOL 120Economy and Society4
SOCIOL 121Innovation and Entrepreneurship: Social and Cultural Context4
UGBA 105Leading People3
UGBA 107The Social, Political, and Ethical Environment of Business3
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?

Lower Division (select one)
L & S 22Sense and Sensibility and Science4
MATH 55Discrete Mathematics4
PHILOS 4Knowledge and Its Limits4
PHILOS 5Science and Human Understanding4
PHILOS 12AIntroduction to Logic4
Upper Division (select two)
MATH 125AMathematical Logic4
MATH 135Introduction to the Theory of Sets4
MATH 136Incompleteness and Undecidability4
PHILOS 122Theory of Knowledge4
PHILOS 125Metaphysics4
PHILOS 128Philosophy of Science4
PHILOS 134Form and Meaning4
PHILOS 140AIntermediate Logic4
PHILOS 140BIntermediate Logic4
PHILOS 142Philosophical Logic4
PHILOS 143Modal Logic4
PHILOS 146Philosophy of Mathematics4
PHILOS 148Probability and Induction4
PHILOS 149Special Topics in Philosophy of Logic and Mathematics4
RHETOR 107Rhetoric of Scientific Discourse4
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?

Lower Division (select one)
COG SCI 1/1B/N1Introduction to Cognitive Science4
PHILOS 2Individual Morality and Social Justice4
PHILOS 3The Nature of Mind4
PHILOS 14Philosophy of Artificial Intelligence4
Upper Division (select two)
COG SCI C100/PSYCH C120Basic Issues in Cognition3
COG SCI C101/LINGUIS C105Cognitive Linguistics4
COG SCI C131/PSYCH C123Computational Models of Cognition4
COG SCI/LINGUIS C142Language and Thought3
ECON C110/POL SCI C135Game Theory in the Social Sciences4
STAT 155Game Theory3
PHILOS 104Ethical Theories4
PHILOS 115Political Philosophy4
PHILOS 132Philosophy of Mind4
PHILOS 133Philosophy of Language4
PHILOS 135Theory of Meaning4
PHILOS 136Philosophy of Perception4
PHILOS 141Philosophy and Game Theory4
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.

Lower Division (select one)
PHYSICS 5BL
PHYSICS 5CL
Introduction to Experimental Physics I
and Introduction to Experimental Physics II
2
PHYSICS 7APhysics for Scientists and Engineers4
PHYSICS 77Introduction to Computational Techniques in Physics3
Upper Division (select two)
ASTRON 120Optical and Infrared Astronomy Laboratory4
ASTRON 121Radio Astronomy Laboratory4
ASTRON 128Astronomy Data Science Laboratory4
ASTRON C161Relativistic Astrophysics and Cosmology4
ASTRON C162Planetary Astrophysics4
CIV ENG C133/MEC ENG C180Engineering Analysis Using the Finite Element Method3
ENGIN 150Basic Modeling and Simulation Tools for Industrial Research Applications4
EPS 108Geodynamics4
EPS 109Computer Simulations with Jupyter Notebooks4
EPS 122Physics of the Earth and Planetary Interiors3
EPS C183/ESPM C170Carbon Cycle Dynamics3
GEOG C136/ESPM C130Terrestrial Hydrology4
GEOG C139/EPS C181Atmosphere, Ocean, and Climate Dynamics3
NUC ENG 101Nuclear Reactions and Radiation4
NUC ENG 130Analytical Methods for Non-proliferation3
NUC ENG 155Introduction to Numerical Simulations in Radiation Transport3
PHYSICS 105Analytic Mechanics4
PHYSICS 111AInstrumentation Laboratory4
PHYSICS 112Introduction to Statistical and Thermal Physics4
PHYSICS 129Particle Physics4
PHYSICS 188Bayesian Data Analysis and Machine Learning for Physical Sciences4
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.

Lower Division (select one)
ECON 1Introduction to Economics4
or ECON 2 Introduction to Economics--Lecture Format
SOCIOL 1Introduction to Sociology4
SOCIOL 3ACPrinciples of Sociology: American Cultures4
SOCIOL 5Evaluation of Evidence4
POL SCI 3Introduction to Empirical Analysis and Quantitative Methods4
POL SCI 88The Scientific Study of Politics2
Upper Division (select two)
DEMOG 110Introduction to Population Analysis3
DEMOG/SOCIOL C126Sex, Death, and Data4
DEMOG/ECON C175Economic Demography4
DEMOG 180Social Networks4
ECON C110/POL SCI C135/W135Game Theory in the Social Sciences4
ENVECON/IAS C118Introductory Applied Econometrics4
MEDIAST 130Research Methods in Media Studies4
POL SCI 132BMachine Learning for Social Scientists4
POL SCI 133Selected Topics in Quantitative Methods4
SOCIOL 106Quantitative Sociological Methods4
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.

Lower Division
MATH 53Multivariable Calculus4
Upper Division (select two)
BIO ENG 105Engineering Devices 14
BIO ENG/EECS C106AIntroduction to Robotics4
BIO ENG/EECS C106BRobotic Manipulation and Interaction4
EECS 149Introduction to Embedded and Cyber Physical Systems4
EL ENG 192Mechatronic Design Laboratory4
INTEGBI C135LLaboratory in the Mechanics of Organisms3
MEC ENG 119Introduction to MEMS (Microelectromechanical Systems)3
MEC ENG C134/EL ENG C128Feedback Control Systems4
MEC ENG 135Design of Microprocessor-Based Mechanical Systems4
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.

Lower Division (select one)
DATA 4ACData and Justice4
GEOG 80An Introduction to Geospatial Technologies: Mapping, Space and Power4
HISTORY 30Science and Society4
ISF 60Technology and Values3
Upper Division (select two)
ANTHRO 115Introduction to Medical Anthropology4
ANTHRO 119Special Topics in Medical Anthropology4
ENGIN/IAS 157ACEngineering, The Environment, and Society4
ENGLISH 180ZScience Fiction4
ENVECON 143Economics of Innovation and Intellectual Property4
ESPM 161Environmental Philosophy and Ethics4
ESPM 162Bioethics and Society4
ESPM 163AC/SOCIOL 137ACEnvironmental Justice: Race, Class, Equity, and the Environment4
FILM 155Media Technologies4
GEOG 130/N130Food and the Environment4
GWS 130ACGender, Race, Nation, and Health4
HISTORY 100S/100STSpecial Topics in the History of Science4
HISTORY 103SProseminar: Problems in Interpretation in the Several Fields of History: History of Science4
HISTORY 138/138THistory of Science in the U.S.4
HISTORY 180/180TThe Life Sciences since 17504
HISTORY 182A/182ATScience, Technology, and Society4
INFO 103History of Information4
ISF 100DIntroduction to Technology, Society, and Culture4
ISF 100GIntroduction to Science, Society, and Ethics4
RHETOR 107Rhetoric of Scientific Discourse4
RHETOR 115Technology and Culture4
RHETOR 145Science, Narrative, and Image4
SOCIOL C115/PB HLTH C155Sociology of Health and Medicine4
SOCIOL 166Society and Technology4
SOCIOL 167Virtual Communities/Social Media4
STS C100/HISTORY C182C/ISF C100GIntroduction to Science, Technology, and Society4
UGIS 110Introduction to Disability Studies3
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 C134Information Technology and Society4
BIO ENG 100Ethics in Science and Engineering3
CY PLAN 101Introduction to Urban Data Analytics4
DATA C104/HISTORY C184D/STS C104DHuman Contexts and Ethics of Data - DATA/History/STS4
DIGHUM 100Theory and Method in the Digital Humanities3
ESPM C167/PB HLTH C160Environmental Health and Development4
INFO 188Behind the Data: Humans and Values3
ISF 100JThe Social Life of Computing4
NWMEDIA 151ACTransforming Tech: Issues and Interventions in STEM and Silicon Valley4
PHILOS 121Moral Questions of Data Science4
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.

Lower Division (select one)
DATA 4ACData and Justice4
SOCIOL 1Introduction to Sociology4
SOCIOL 3ACPrinciples of Sociology: American Cultures4
Upper Division (select two)
ENVECON 153Population, Environment, and Development3
GPP 105The Ethics, Methods, and Pragmatics of Global Practice4
GPP 115Global Poverty: Challenges and Hopes4
GLOBAL 102Critical Thinking In Global Studies4
GWS 130ACGender, Race, Nation, and Health4
PB HLTH 112Global Health: A Multidisciplinary Examination4
PB HLTH 126Health Economics and Public Policy3
PB HLTH 150DIntroduction to Health Policy and Management3
PB HLTH 150EIntroduction to Community Health and Human Development3
PB HLTH C155/SOCIOL C115Sociology of Health and Medicine4
PB HLTH C160/ESPM C167Environmental Health and Development4
PB HLTH 181Poverty and Population3
POLECON 111Poverty and Social Policy3
SOCIOL 115GHealth in a Global Society4
SOCIOL 127Development and Globalization4
SOC WEL 112Social Welfare Policy3
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. 

Lower Division (select one)
DATA 4ACData and Justice4
SOCIOL 1Introduction to Sociology4
SOCIOL 3ACPrinciples of Sociology: American Cultures4
Upper Division (select two)
GWS 132ACGender, Race, and Law4
LEGALST 100Foundations of Legal Studies4
LEGALST 102Policing and Society4
LEGALST 123Data, Prediction & Law4
LEGALST 158Law and Development4
LEGALST 160Punishment, Culture, and Society4
PB HLTH 150DIntroduction to Health Policy and Management3
POLECON 111Poverty and Social Policy3
POL SCI 186Public Problems4
PUB POL 101Introduction to Public Policy Analysis4
SOC WEL 112Social Welfare Policy3
SOC WEL 181Social Science and Crime Prevention Policy3
SOCIOL 114Sociology of Law4
SOCIOL 148Social Policy4
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.  

Lower Division (select one)
CIV ENG 11Engineered Systems and Sustainability3
LD ARCH 12Environmental Science for Sustainable Development4
Upper Division (select two)
ARCH 140Energy and Environment4
CIV ENG 107Climate Change Mitigation3
CIV ENG 110Water Systems of the Future3
CIV ENG 111Environmental Engineering3
CIV ENG 155Transportation Systems Engineering3
CIV ENG 191Civil and Environmental Engineering Systems Analysis3
ENE,RES 131Data, Environment and Society4
ESPM C133/GEOG C135Water Resources and the Environment3
ESPM/LD ARCH C177GIS and Environmental Spatial Data Analysis4
LD ARCH 122Hydrology for Planners4
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

Lower Division (select one)
CIV ENG C88Data Science for Smart Cities2
ENV DES 4BGlobal Cities3
GEOG 70ACThe Urban Experience: Race, Class, Gender & The American City4
Upper Division (select two)
ARCH 110ACThe Social and Cultural Processes in Architecture & Urban Design3
CY PLAN 110Introduction to City Planning4
CY PLAN 113AEconomic Analysis for Planning3
CY PLAN 114Introduction to Urban and Regional Transportation3
CY PLAN 119Planning for Sustainability4
CY PLAN 140Urban Design: City-Building and Place-Making3
ENE,RES 131Data, Environment and Society4
ENV DES 100The City: Theories and Methods in Urban Studies4
ENV DES 102Climate Change and City Planning: Adaptation and Resilience3
GEOG 181Urban Field Study4
GEOG 182Field Study of Buildings and Cities3
LD ARCH 130Sustainable Landscapes and Cities4
LD ARCH/GEOG C188Geographic Information Science4
LD ARCH 187Representation as Research: Contemporary Topics in Landscape Visualization3
SOCIOL 136Urban Sociology4

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

  1. 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.

  2. All courses for the minor must be taken for a letter grade.

  3. Students must earn a C- or better in each course, and have a minimum 2.0 GPA in all courses towards the minor.

  4. 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).

  5. 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).

  6. 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.

  7. There is no restriction on overlap with another minor.

  8. Courses used to fulfill the minor requirements may be applied toward the Seven-Course Breadth requirement.

  9. 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

DATA/COMPSCI/STAT/INFO C8Foundations of Data Science 14
or STAT 20 Introduction to Probability and Statistics
DATA/COMPSCI C88CComputational Structures in Data Science3-4
or COMPSCI 61A The Structure and Interpretation of Computer Programs
or ENGIN 7 Introduction to Computer Programming for Scientists and Engineers
Choose one of the following: 2
DATA/STAT C88SProbability and Mathematical Statistics in Data Science3-4
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
DATA/COMPSCI/STAT C100Principles & Techniques of Data Science4
Choose one of the following:
Information Technology and Society [4]
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
DATA/STAT C131AStatistical Methods for Data Science4
STAT 133Concepts in Computing with Data3
Choose one of the following:
Information Technology and Society [4]
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 (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
FallUnitsSpringUnits
DATA C84COMPSCI 61A or DATA C88C3-4
MATH 1A (10A or 16A acceptable)4MATH 1B4
Reading & Composition A4Reading & Composition B4
Elective2Non-major Elective1-2
 14 12-14
Sophomore
FallUnitsSpringUnits
COMPSCI 61B4MATH 54 or 564
Breadth/Elective3-4Lower-division Domain Emphasis3-4
Breadth/Elective3-4Breadth/Elective3-4
 10-12 10-12
Junior
FallUnitsSpringUnits
DATA C140 (or other approved Probability)4DATA C1004
Domain Emphasis Upper-division #14Computational & Inferential Depth #13-4
Breadth/Elective3-4Breadth/Elective3-4
 11-12 10-12
Senior
FallUnitsSpringUnits
Computational & Inferential Depth #23-4DATA C102 (or other approved MLDM)4
Domain Emphasis Upper-division #23-4DATA C104 (or other approved HCE)4
Breadth/Elective3-4Breadth/Elective3-4
 9-12 11-12
Total Units: 87-100
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
FallUnitsSpringUnits
DATA C84DATA C1004
Lower-division Domain Emphasis2-4DATA C140 (or other approved Probability)4
DATA C88C or COMPSCI 61A3-4American Cultures/Upper-division Elective3-4
Non-major Elective1-2Non-major Elective1-2
 10-14 12-14
Second Year
FallUnitsSpringUnits
Computational & Inferential Depth #13-4DATA C102 (or other approved MLDM)4
Domain Emphasis Upper-division #13-4DATA C104 (or other approved HCE)4
Domain Emphasis Upper-division #23-4Computational & Inferential Depth #23-4
Non-major Elective1-2Non-major Elective1-2
 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:

View the Data Science Major Map.

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.eduLearn 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.

 

Related Courses

Contact Information

Data Science Undergraduate Studies; College of Computing, Data Science, and Society

VISIT PROGRAM WEBSITE

Faculty Director

John DeNero

Faculty Director of Pedagogy

Ani Adhikari

Director of Advising

Laura Imai

130 Warren Hall

ds-advising@berkeley.edu

Undergraduate Major Advisor

Marjorie Ensor

130 Warren Hall

ds-advising@berkeley.edu

Undergraduate Major Advisor

Aaron Giacosa

130 Warren Hall

ds-advising@berkeley.edu

Undergraduate Major Advisor

Silvia Guzman

130 Warren Hall

ds-advising@berkeley.edu

Undergraduate Major Advisor

Miguel Rios

130 Warren Hall

ds-advising@berkeley.edu

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