Embark on a journey into the world of data science at the University of California, Berkeley, a leading institution renowned for its pioneering programs and cutting-edge research. This Course Guide Berkeley provides a comprehensive overview of the Data Science programs offered, designed to equip you with the skills and knowledge to thrive in the data-driven era. Whether you’re aiming for a Bachelor of Arts degree or a Minor in Data Science, Berkeley offers a robust curriculum and diverse opportunities to tailor your education to your specific interests and career aspirations.
Data Science Major at Berkeley: Bachelor of Arts (BA) Program
The Data Science Major at UC Berkeley culminates in a Bachelor of Arts (BA) degree, a program meticulously crafted to blend computational and inferential thinking. This interdisciplinary approach empowers students to extract meaningful conclusions from data, addressing real-world challenges across various sectors. Data scientists at Berkeley come from diverse academic backgrounds, united by a shared passion for leveraging mathematical rigor, scientific methodologies, and computational power to solve intricate problems in business, research, and society.
The curriculum is designed to cultivate your ability to draw valid, context-aware conclusions from data. You will gain expertise in statistical inference, computational processes, data management strategies, and domain-specific knowledge. The program emphasizes hands-on data analysis across the entire investigative cycle, preparing you for both scientific and practical applications. Furthermore, a critical component of the major is understanding the ethical and societal implications of data analytics, ensuring responsible and human-centered data science practices.
The core of the Data Science major is anchored by DATA C8 (Foundations of Data Science) and DATA C100 (Principles & Techniques of Data Science). Beyond these foundational courses, the major is structured around several key requirement groups:
- Foundations in Mathematics and Computing: Establishing the fundamental quantitative and computational skills.
- Computational and Inferential Depth: Delving deeper into advanced computational and statistical methodologies.
- Modeling, Learning and Decision Making: Exploring techniques for predictive modeling and informed decision-making.
- Probability: Building a strong theoretical understanding of probability and its applications in data science.
- Human Contexts and Ethics: Examining the societal and ethical dimensions of data science.
- Domain Emphasis: Specializing in a chosen field to apply data science principles within a specific context.
A distinctive feature of the Berkeley Data Science major is the Domain Emphasis. This specialization involves selecting a cluster of courses—one lower division and two upper division—that allows you to integrate data science with another field of study, building valuable interdisciplinary bridges and tailoring your expertise to your passions.
Collaborative learning is a key component of the Data Science program at Berkeley, fostering teamwork and problem-solving skills.
Major Requirements: In Detail
In addition to meeting the general University, campus, and college requirements, Data Science majors must fulfill specific requirements outlined by the program. Always refer to the official Data Science program website for the most up-to-date information and any program updates.
General Guidelines:
- All courses intended to satisfy major requirements must be taken for a letter grade. Pass/Fail grading is not accepted.
- Students are allowed to have a maximum of two upper-division courses overlapping between two majors.
- A minimum GPA of 2.0 is required in all courses contributing to the major, and specifically in all upper-division courses within the major.
Lower Division Prerequisites:
These foundational courses build the necessary groundwork for upper-division data science studies.
| 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 Processingand 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 |
Note: STAT 20 can substitute DATA C8 if combined with CS 61A or CS 88/Data C88C, but not if ENGIN 7 is taken.
In addition to these, you will need to complete one lower division course as part of your chosen Domain Emphasis.
Upper Division Requirements:
The upper-division curriculum requires completion of 8 unique courses, totaling 28 or more units, distributed across the following categories.
Principles and Techniques of Data Science
This core requirement ensures a solid grasp of the fundamental principles underlying data science practices.
| Code | Title | Units |
|---|---|---|
| DATA/COMPSCI/STAT C100 | Principles & Techniques of Data Science | 4 |
Computational and Inferential Depth
Two upper division courses (7+ units total) are required to provide in-depth knowledge beyond the foundational Data 100 course, enhancing both computational and inferential skills.
| 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 | Course Not Available | |
| COMPSCI 170 | Efficient Algorithms and Intractable Problems | 4 |
| COMPSCI 186 | Introduction to Database Systems | 4 |
| or COMPSCI W186 | Course Not Available | |
| 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 (Quantitative) | |
| 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
A dedicated upper-division course in probability is essential to build a strong theoretical foundation for data science.
| 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
This requirement focuses on the practical application of data science through modeling, machine learning, and informed decision-making processes.
| 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
Understanding the ethical and societal implications of data science is crucial. This requirement ensures consideration of the human, social, and ethical contexts of data analytics.
| 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
The Domain Emphasis is a cornerstone of the Data Science BA, allowing for specialization and application of data science within a chosen field. You will select one lower-division course and two upper-division courses from one of the following areas:
- Applied Mathematics and Modeling: Explore mathematical techniques and modeling essential for data science.
- Business and Industrial Analytics: Focus on data-driven decision-making in business and industry.
- Cognition: Investigate the human mind through cognitive science, neuroscience, and computational models.
- Computational Methods in Molecular and Genomic Biology: Prepare for bioinformatics and computational biology careers.
- Data Arts and Humanities: Engage data science practices within humanities and arts disciplines.
- Ecology and the Environment: Analyze diverse data sources related to living organisms and ecosystems.
- Economics: Apply data science to economic analysis and decision-making.
- Environment, Resource Management, and Society: Explore the intersection of economics, policy, and environmental science.
- Evolution and Biodiversity: Study the evolution of life using diverse data sources.
- Geospatial Information and Technology: Utilize geospatial approaches for geophysical and ecological understanding.
- Human and Population Health: Focus on data and methods in epidemiology, health, and related fields.
- Human Behavior and Psychology: Study individual and group behavior using data-driven approaches.
- Inequalities in Society: Analyze social inequalities using data science methodologies.
- Linguistic Sciences: Explore data-driven analysis of language and linguistic structures.
- Neurosciences: Gain expertise in computational neuroscience and neural data analysis.
- Organizations and the Economy: Study the social construction of markets and organizational roles.
- Philosophical Foundations: Evidence and Inference: Delve into the philosophical underpinnings of data and reasoning.
- Philosophical Foundations: Minds, Morals, and Machines: Explore ethical and philosophical questions arising from AI and machine learning.
- Physical Science Analytics: Apply data analytics in physical science and engineering domains.
- Quantitative Social Science: Develop expertise in quantitative methodologies for social science research.
- Robotics: Explore the design, control, and applications of robots.
- Science, Technology, and Society: Critically engage with the interplay of science, technology, and society.
- Social Welfare, Health, and Poverty: Apply data science to social welfare, health, and poverty-related issues.
- Social Policy and Law: Study the foundations of legal institutions and social policy analysis.
- Sustainable Development and Engineering: Focus on data-driven approaches to sustainability and environmental engineering.
- Urban Science: Explore the use of data science to understand and shape urban environments.
Note: Course availability and prerequisites for Domain Emphases can vary. Students should plan carefully and consider alternative options.
UC Berkeley’s iconic campus, a hub for innovation and academic excellence in Data Science.
Data Science Minor at Berkeley
The Minor in Data Science at UC Berkeley is designed to provide students from diverse disciplines with practical data analysis skills and critical thinking abilities regarding data and models. This minor empowers you to participate effectively in data science projects and apply rigorous computational and inferential analysis within your primary field of study. For detailed information, visit the Data Science Minor program website.
Minor Requirements: Essential Skills
General Guidelines for the Minor:
- Minors must be declared before the start of their Expected Graduation Term (EGT).
- All minor courses must be taken for a letter grade.
- A minimum grade of C- and a 2.0 GPA in minor courses are required.
- Up to one upper-division course can overlap with a major.
- Maximum one course from the student’s major department can count towards the minor’s upper-division requirements.
- Upper-division courses used for lower-division requirements do not count towards the upper-division course count or major overlap limit.
- No restrictions on overlap with other minors.
- Minor courses can fulfill Seven-Course Breadth requirements.
- All minor requirements must be completed before the final exam day of your graduating semester.
Lower-division Requirements:
These courses provide the foundational knowledge necessary for the Data Science Minor.
| 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 |
Note: STAT 20 can substitute DATA C8 if combined with CS 61A or CS 88/Data C88C, but not if ENGIN 7 is taken.
Note 2: STAT 134, DATA C140, IND ENG 172, EECS 126, or MATH 106 can substitute for the probability requirement.
Upper-division Requirements:
Complete 4 upper-division courses following one of these pathways:
1-Core Course Pathway
This pathway centers around the core Data C100 course, complemented by courses focusing on ethics and electives.
| Code | Title | Units |
|---|---|---|
| DATA/COMPSCI/STAT C100 | Principles & Techniques of Data Science | 4 |
| Choose one of the following for Human Contexts and Ethics: | ||
| AMERSTD/AFRICAM C134 | Information Technology and Society [4] | |
| or AFRICAM 134 | 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] | |
| 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] |
Choose TWO additional electives from the Approved Elective List.
2-Core Course Pathway
This option offers a deeper dive into statistical methods and computing with data, along with an ethics course and elective.
| 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 for Human Contexts and Ethics: | ||
| AMERSTD/AFRICAM C134 | Information Technology and Society [4] | |
| or AFRICAM 134 | 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] | |
| 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] |
Choose ONE additional elective from the Approved Elective List.
Academic and Career Opportunities
Berkeley Data Science programs extend beyond the classroom, offering numerous opportunities for personal and professional growth.
Student Teams: Join a vibrant community by participating in student teams focused on Communications, Operations, External Relations, and Curriculum Development. These teams offer internships and volunteer positions, with pathways to leadership roles. For inquiries, contact [email protected]. Learn more here.
Data Scholars: The Data Scholars program is dedicated to fostering inclusivity in data science, creating a supportive environment for underrepresented and nontraditional students. Benefit from specialized tutoring, advising, mentorship, and workshops designed to empower diverse perspectives in the field. Learn more here.
Data Science Peer Advising: Get guidance from fellow students! Data Science Peer Advisors offer drop-in services to assist with course selection, academic exploration, and major/minor declaration processes. Contact them at [email protected]. Learn more here.
Data Science Course Staff: Contribute to the educational mission by becoming a Data Science Course Staff member. Both graduate and undergraduate students are appointed to support instructional programs, playing a vital role in student learning experiences. Learn more here.
Group projects and discussions are integral to the Data Science curriculum, promoting collaborative problem-solving and diverse perspectives.
Resources and Support
For further information and guidance, explore these valuable resources:
- Program Website: Data Science Undergraduate Studies
- Faculty Director: John DeNero
- Faculty Director of Pedagogy: Ani Adhikari
- Director of Advising: Laura Imai, 130 Warren Hall, [email protected]
- Undergraduate Major Advisors:
- Marjorie Ensor, 130 Warren Hall, [email protected]
- Aaron Giacosa, 130 Warren Hall, [email protected]
- Silvia Guzman, 130 Warren Hall, [email protected]
- Miguel Rios, 130 Warren Hall, [email protected]
Conclusion
The Data Science programs at UC Berkeley offer an unparalleled educational experience, combining rigorous academics with diverse specializations and abundant opportunities for growth. Whether you choose the comprehensive BA program or the focused Minor, you will be well-prepared to become a leader in the data-driven world. Visit the Data Science Program Website to learn more and begin your journey today.
