Data Science

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

1 Students 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

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.

Probability

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

Modeling, Learning, and Decision-Making

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

Human Contexts and Ethics

S tudents 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.

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.

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.

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.

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.

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

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

Economics

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.

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.

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.

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.

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.

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.

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.

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.

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?

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?

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?

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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

If completing the 1-core course pathway, choose TWO from the Approved Elective List.

2-core course PATHWAY

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

  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 C140 (or other approved Probability)	4	DATA C100	4
  Domain Emphasis Upper-division #1	4	Computational & Inferential Depth #1	3-4
  Breadth/Elective	3-4	Breadth/Elective	3-4
  11-12	10-12
  Senior
  Fall	Units	Spring	Units
  Computational & Inferential Depth #2	3-4	DATA C102 (or other approved MLDM)	4
  Domain Emphasis Upper-division #2	3-4	DATA C104 (or other approved HCE)	4
  Breadth/Elective	3-4	Breadth/Elective	3-4
  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
  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.

  Related Courses

  Expand all course descriptions [+] Collapse all course descriptions [-]

  DATA 4AC Data and Justice 4 Units

  Terms offered: Spring 2023, Spring 2022, Spring 2021
  This course engages students with fundamental questions of justice in relation to data and computing in American society. Data collection, visualization, and analysis have been entangled in the struggle for racial and social justice because they can make injustice visible, imaginable, and thus actionable. Data has also been used to oppress minoritized communities and institutionalize, rationalize, and naturalize systems of racial violence. The course examines key sites of justice involving data (such as citizenship, policing, prisons, environment, and health). Along with critical social science tools, students gain introductory experience and do collaborative and creative projects with data science using real-world data.
  Data and Justice: Read More [+]

  Hours & Format

  Fall and/or spring: 15 weeks - 3 hours of lecture and 1.5 hours of discussion per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Alternative to final exam.

  DATA 6 Introduction to Computational Thinking with Data 3 Units

  Terms offered: Fall 2024
  An introduction to computational thinking and quantitative reasoning, preparing students for further coursework (especially Foundations of Data Science, Data 8). Emphasizes the use of computation to gain insight about quantitative problems. Uses data from various domains in the social sciences in order to develop an understanding of the societal implications of data science. Expressions, data types, collections, and tables in Python. Programming practices, abstraction , and iteration. The data science lifecycle. Visualizing univariate and bivariate data with bar charts, histograms, plots, and maps. Introduction to statistical concepts including averages and distributions, prediction, causality, probability, sampling, and inference.
  Introduction to Computational Thinking with Data: Read More [+]

  Objectives & Outcomes

  Course Objectives: C6 also includes quantitative reasoning concepts that aren’t covered in Data 8. These include certain topics in: principles of data visualization; simulation of random processes; and understanding numerical functions through their graphs. This will help prepare students for computational and quantitative courses other than Data 8.
  C6 takes advantage of the complementarity of computing and quantitative reasoning to enliven abstract ideas and build students’ confidence in their ability to solve real problems with quantitative tools. Students learn computer science concepts and immediately apply them to plot functions, visualize data, and simulate random events.

  Foundations of Data Science (CS/Info/Stat C8, a.k.a. Data 8) is an increasingly popular class for entering students at Berkeley. Data 8 builds students’ computing skills in the first month of the semester, and students rely on these skills as the course progresses. For some students, particularly those with little prior exposure to computing, developing these skills benefits from further time and practice. C6 is a rapid introduction to Python programming, visualization, and data analysis, which will prepare students for success in Data 8.

  Student Learning Outcomes: Appreciate the interdisciplinary nature of data science.
  Create and use visualizations to understand univariate data and to identify associations or causal relationships in bivariate data.
  Formulate questions about data and perform exploratory data analysis.
  Perform basic computations in Python, and be able to work with tabular data.
  Run and understand basic probabilistic simulations.
  Understand the syntactic structure of Python code.
  Use good practices in Python programming.

  Rules & Requirements

  Credit Restrictions: Students will receive no credit for DATA 6 after completing DATA C8.

  Hours & Format

  Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 0 hours of laboratory per week

  Summer: 6 weeks - 4 hours of lecture, 2 hours of discussion, and 4 hours of laboratory per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required.

  Instructors: Yan, Hug, Harding

  Formerly known as: Data Science, Undergraduate C6/Computer Science C6/Statistics C6

  DATA C8 Foundations of Data Science 4 Units

  Terms offered: Fall 2024, Summer 2024 8 Week Session, Spring 2024, Fall 2023, Spring 2023, Fall 2022, Spring 2022, Fall 2021, Summer 2021 8 Week Session, Fall 2020
  Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.
  Foundations of Data Science: Read More [+]

  Rules & Requirements

  Prerequisites: This course may be taken on its own, but students are encouraged to take it concurrently with a data science connector course (numbered 88 in a range of departments)

  Credit Restrictions: Students will receive no credit for DATA C8\COMPSCI C8\INFO C8\STAT C8 after completing COMPSCI 8, or DATA 8. A deficient grade in DATA C8\COMPSCI C8\INFO C8\STAT C8 may be removed by taking COMPSCI 8, COMPSCI 8, or DATA 8.

  Hours & Format

  Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

  Summer: 8 weeks - 6 hours of lecture and 4 hours of laboratory per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required.

  Formerly known as: Computer Science C8/Statistics C8/Information C8

  Also listed as: COMPSCI C8/INFO C8/STAT C8

  DATA 88 Data Science Connector 2 - 4 Units

  Terms offered: Spring 2022, Spring 2021, Fall 2020
  Designed to be taken in conjunction with the Foundations of Data Science (COMPSCI/INFO/STAT C8) course, each connector course will flesh out data science ideas in the context of one particular field. Blending inferential thinking and computational thinking, the course relies on the increasing availability of datasets across a wide range of human endeavor, and students' natural interest in such data, to teach students to work actively with data in a field of their interest and to interpret and critique their analyses of data. Topics vary by field, and several topics will be offered each term.
  Data Science Connector: Read More [+]

  Objectives & Outcomes

  Course Objectives: Discuss how to formulate and substantiate an argument with evidence
  Explain a variety of analytic and visualization techniques
  Explore approaches to effective communication
  Explore the challenges with working with primary and secondary data

  Student Learning Outcomes: Apply data analysis to evaluate everyday problems
  Communicate effectively in written, spoken, and graphical form about specific issues
  Interpret statistical results
  Know how to locate and use primary data sources
  Obtain and/or collect relevant data using specific qualitative and/or quantitative research methods
  Understand how to use empirical evidence to evaluate an argument

  Rules & Requirements

  Prerequisites: Instructors may require students to enroll concurrently or have completed Data 8 (COMPSCI/STAT/INFO C8)

  Repeat rules: Course may be repeated for credit without restriction. Students may enroll in multiple sections of this course within the same semester.

  Hours & Format

  Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Alternative to final exam.

  DATA 88E Economic Models 2 Units

  Terms offered: Fall 2024, Fall 2023, Fall 2022
  This class aims to motivate and illustrate key concepts in economics through a series of exercises and examples that use Python Jupyter notebooks. The class covers concepts from introductory economics, microeconomic theory, econometrics, development economics, environmental economics and public economics. The course provides data science students a pathway to apply Python programming and data science concepts within the discipline of economics. The course will also gives economics students a pathway to apply programming to reinforce fundamental concepts and to advance the level of study in upper division coursework and possible thesis work.
  Economic Models: Read More [+]

  Objectives & Outcomes

  Course Objectives: Demonstrate how to construct understanding of concepts in economics by developing and coding examples
  Illustrate topics in economics through coding applications
  Motivate basics of econometrics from a data science perspective
  

  Student Learning Outcomes: Programmatically create and interpret graphs of simple equations used in microeconomics
  Reason about and solve simple equations used in microeconomics through coding
  Understand basic concepts in economics

  Rules & Requirements

  Prerequisites: You must have taken Data C8 or be concurrently enrolled in Data C8 to take this course. That being said, we are able to make exceptions if you have prior programming or data science experience; please email the course staff if you have any questions. Prior economics knowledge may be helpful but is not necessary

  Hours & Format

  Fall and/or spring: 15 weeks - 2 hours of lecture per week

  Summer:
  6 weeks - 5 hours of lecture per week
  8 weeks - 4 hours of lecture per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Alternative to final exam.

  DATA C88C Computational Structures in Data Science 3 Units

  Terms offered: Fall 2024, Summer 2024 8 Week Session, Spring 2024, Spring 2023, Fall 2022
  Development of Computer Science topics appearing in Foundations of Data Science (C8); expands computational concepts and techniques of abstraction. Understanding the structures that underlie the programs, algorithms, and languages used in data science and elsewhere. Mastery of a particular programming language while studying general techniques for managing program complexity, e.g., functional, object-oriented , and declarative programming. Provides practical experience with composing larger systems through several significant programming projects.
  Computational Structures in Data Science: Read More [+]

  Objectives & Outcomes

  Course Objectives: Develop a foundation of computer science concepts that arise in the context of data analytics, including algorithm, representation, interpretation, abstraction, sequencing, conditional, function, iteration, recursion, types, objects, and testing, and develop proficiency in the application of these concepts in the context of a modern programming language at a scale of whole programs on par with a traditional CS introduction course.

  Student Learning Outcomes: Students will be able to demonstrate a working knowledge of these concepts and a proficiency of programming based upon them sufficient to construct substantial stand-alone programs.

  Rules & Requirements

  Prerequisites: This course is a Data Science connector course and may only be taken concurrently with or after COMPSCI C8/DATA C8/INFO C8/STAT C8. Students may take more than one Data Science connector (88) course if they wish, concurrent with or after having taken the C8 course

  Credit Restrictions: Students will receive no credit for DATA C88C after completing COMPSCI 61A.

  Hours & Format

  Fall and/or spring: 15 weeks - 2-2 hours of lecture, 2-2 hours of laboratory, and 0-1 hours of supplement per week

  Summer: 8 weeks - 4-4 hours of lecture, 4-4 hours of laboratory, and 0-2 hours of supplement per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required.

  Instructors: Ball, Culler

  Formerly known as: Computer Science 88

  Also listed as: COMPSCI C88C

  DATA C88S Probability and Mathematical Statistics in Data Science 3 Units

  Terms offered: Spring 2024, Summer 2023 8 Week Session, Spring 2023, Fall 2022
  In this connector course we will state precisely and prove results discovered while exploring data in Data C8. Topics include: probability, conditioning, and independence; random variables; distributions and joint distributions; expectation, variance, tail bounds; Central Limit Theorem; symmetries in random permutations; prior and posterior distributions; probabilistic models; bias-variance tradeoff; testing hypotheses; correlation and the regression model.
  Probability and Mathematical Statistics in Data Science: Read More [+]

  Rules & Requirements

  Prerequisites: Prerequisite: one semester of calculus at the level of Math 16A, Math 10A, or Math 1A. Corequisite or Prerequisite: Foundations of Data Science (COMPSCI C8 / DATA C8 / INFO C8 / STAT C8)

  Credit Restrictions: Students will receive no credit for DATA C88S after completing STAT 134, STAT 140, STAT 135, or DATA C102.

  Hours & Format

  Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week

  Summer: 8 weeks - 6 hours of lecture and 4 hours of discussion per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required.

  Formerly known as: Statistics 88

  Also listed as: STAT C88S

  DATA 94 Special Topics in Data Science 1 - 4 Units

  Terms offered: Fall 2024, Spring 2021
  Topics will vary semester to semester.
  Special Topics in Data Science: Read More [+]

  Hours & Format

  Fall and/or spring: 15 weeks - 1-3 hours of lecture, 0-2 hours of discussion, and 0-2 hours of laboratory per week

  Summer: 8 weeks - 2-6 hours of lecture, 0-4 hours of discussion, and 0-4 hours of laboratory per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required, with common exam group.

  DATA C100 Principles & Techniques of Data Science 4 Units

  Terms offered: Fall 2024, Summer 2024 8 Week Session, Spring 2024, Summer 2023 8 Week Session, Fall 2022, Fall 2021, Fall 2020
  In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction​, and decision-making.​ This class will focus on quantitative critical thinking​ and key principles and techniques needed to carry out this cycle. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.
  Principles & Techniques of Data Science: Read More [+]

  Rules & Requirements

  Prerequisites: COMPSCI C8 / DATA C8 / INFO C8 / STAT C8 with a C- or better, or Pass; and COMPSCI 61A, COMPSCI/DATA C88C, or ENGIN 7 with a C- or better, or Pass; Corequisite: MATH 54, 56 or EECS 16A (C- or better, or Pass, required if completed prior to Data C100)

  Credit Restrictions: Students will receive no credit for DATA C100\STAT C100\COMPSCI C100 after completing DATA 100. A deficient grade in DATA C100\STAT C100\COMPSCI C100 may be removed by taking DATA 100.

  Hours & Format

  Fall and/or spring: 15 weeks - 3-3 hours of lecture, 1-1 hours of discussion, and 0-1 hours of laboratory per week

  Summer: 8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required.

  Instructors: Gonzalez, Nourozi, Perez, Yan

  Formerly known as: Statistics C100/Computer Science C100

  Also listed as: COMPSCI C100/STAT C100

  DATA C101 Data Engineering 4 Units

  Terms offered: Not yet offered
  This course will cover the principles and practices of managing data at scale, with a focus on use cases in data analysis and machine learning. We will cover the entire life cycle of data management and science, ranging from data preparation to exploration, visualization and analysis, to machine learning and collaboration, with a focus on ensuring reliable, scalable operationalization.
  
  Data Engineering: Read More [+]

  Rules & Requirements

  Prerequisites: COMPSCI 61B, or INFO 206B, or equivalent courses in programming with a C- or better, or Pass; and COMPSCI C100 / DATA C100 / STAT C100, or COMPSCI 189, or INFO 251, or DATA 144, or equivalent upper-division course in data science with a C- or better, or Pass

  Hours & Format

  Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required.

  Instructors: Hellerstein, Jain, Parameswaran, Yan

  Formerly known as: Data Science, Undergraduate 101

  Also listed as: COMPSCI C187

  DATA C102 Data, Inference, and Decisions 4 Units

  Terms offered: Fall 2024, Spring 2024, Fall 2023
  This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals , causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.
  Data, Inference, and Decisions: Read More [+]

  Rules & Requirements

  Prerequisites: Math 54 or 56 or 110 or Stat 89A or Physics 89 or both of EECS 16A and 16B with a C- or better, or Pass; Data/Stat/CompSci C100 with a C- or better, or Pass; and any of EECS 126, Data/Stat C140, Stat 134, IndEng 172, Math 106 with a C- or better, or Pass. Data/Stat C140 or EECS 126 are preferred

  Credit Restrictions: Students will receive no credit for DATA C102 after completing STAT 102, or DATA 102. A deficient grade in DATA C102 may be removed by taking STAT 102, STAT 102, or DATA 102.

  Hours & Format

  Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required.

  Formerly known as: Statistics 102

  Also listed as: STAT C102

  DATA C104 Human Contexts and Ethics of Data - DATA/History/STS 4 Units

  Terms offered: Fall 2024, Summer 2024 8 Week Session, Spring 2024, Spring 2022, Fall 2020, Spring 2020
  This course teaches you to use the tools of applied historical thinking and Science, Technology, and Society (STS) to recognize, analyze, and shape the human contexts and ethics of data. It addresses key topics such as doing ethical data science amid shifting definitions of human subjects, consent, and privacy; the changing relationship between data, democracy, and law; the role of data analytics in how corporations and governments provide public goods such as health and security to citizens; sensors, machine learning and artificial intelligence and changing landscapes of labor, industry, and city life. It prepares you to engage as a knowledgeable and responsible citizen and professional in the varied arenas of our datafied world.
  Human Contexts and Ethics of Data - DATA/History/STS: Read More [+]

  Rules & Requirements

  Credit Restrictions: Students will receive no credit for DATA C104\HISTORY C184D\STS C104D after completing DATA 104. A deficient grade in DATA C104\HISTORY C184D\STS C104D may be removed by taking DATA 104.

  Hours & Format

  Fall and/or spring: 15 weeks - 3-3 hours of lecture and 0-1.5 hours of discussion per week

  Summer:
  6 weeks - 7.5-7.5 hours of lecture and 0-3.5 hours of discussion per week
  8 weeks - 6-6 hours of lecture and 0-3 hours of discussion per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required.

  Formerly known as: History C184D/Science and Technology Studies C104D

  Also listed as: HISTORY C184D/STS C104D

  DATA C131A Statistical Methods for Data Science 4 Units

  Terms offered: Fall 2024, Fall 2023, Spring 2023
  This course teaches a broad range of statistical methods that are used to solve data problems. Topics include group comparisons and ANOVA, standard parametric statistical models, multivariate data visualization, multiple linear regression, logistic regression and classification, regression trees and random forests. An important focus of the course is on statistical computing and reproducible statistical analysis. The course and lab include hands-on experience in analyzing real world data from the social, life, and physical sciences. The R statistical language is used.
  
  Statistical Methods for Data Science: Read More [+]

  Rules & Requirements

  Prerequisites: Statistics/Computer Science/Information C8 or Statistics 20; and Mathematics 1A, Mathematics 16A, or Mathematics 10A/10B. Strongly recommended corequisite: Statistics 33A or Statistics 133

  Hours & Format

  Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required.

  Formerly known as: Statistics 131A

  Also listed as: STAT C131A

  DATA C140 Probability for Data Science 4 Units

  Terms offered: Fall 2024, Spring 2024, Fall 2023, Spring 2023
  An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
  Probability for Data Science: Read More [+]

  Objectives & Outcomes

  Course Objectives: Data/Stat C140 is a probability course for Data C8 graduates who have taken more mathematics and wish to go deeper into data science. The emphasis on simulation and the bootstrap in Data C8 gives students a concrete sense of randomness and sampling variability. Data/Stat C140 capitalizes on this, abstraction and computation complementing each other throughout. Topics in statistical theory are included to allow students to proceed to modeling and statistical learning classes without taking a further semester of mathematical statistics.

  Student Learning Outcomes: Understand the difference between math and simulation, and appreciate the power of both
  Use a variety of approaches to problem solving
  Work with probability concepts algebraically, numerically, and graphically

  Rules & Requirements

  Prerequisites: DATA/COMPSCI/INFO/STAT C8, or both STAT 20 and one of COMPSCI 61A or COMPSCI/DATA C88C with C- or better, or Pass; and one year of calculus at the level of MATH 1A-1B or higher, with C- or better, or Pass. Corequisite: MATH 54, MATH 56, EECS 16B, MATH 110 or equivalent linear algebra (C- or better, or Pass, required if completed prior to enrollment in Data/Stat C140)

  Credit Restrictions: Students will receive no credit for STAT C140 after completing STAT 134, or EECS 126.

  Hours & Format

  Fall and/or spring:
  15 weeks - 3-3 hours of lecture, 1-1 hours of discussion, 1-1 hours of supplement, and 0-1 hours of voluntary per week
  15 weeks - 3-3 hours of lecture, 2-2 hours of discussion, 0-0 hours of supplement, and 0-1 hours of voluntary per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Final exam required.

  Formerly known as: Statistics 140

  Also listed as: STAT C140

  DATA 144 Data Mining and Analytics 3 Units

  Terms offered: Fall 2024, Fall 2023, Fall 2022
  Data Mining and Analytics introduces students to practical fundamentals of data mining and emerging paradigms of data mining and machine learning with enough theory to aid intuition building. The course is project-oriented, with a project beginning in class every week. The in-class portion of the project is meant to be collaborative and a time for the instructor and GSIs to work closely with project groups to understand the objectives, help work through software logistics, and connect project work to lecture. Lectures will introduce theories, concepts, practical contexts, and algorithms. Students should expect to leave the class with hands-on, contemporary data mining skills they can confidently apply in research and industry.
  Data Mining and Analytics: Read More [+]

  Objectives & Outcomes

  Course Objectives: Conduct manual feature engineering (from domain knowledge) vs. machine induced featurization (representation learning)
  Develop intuition in various machine learning classification algorithms (e.g. decision trees, feed-forward neural networks, recurrent neural networks, skip-grams) and clustering techniques (e.g. k-means, spectral)
  Foster critical thinking about real-world actionability from analytics
  Provide an overview of issues in research and practice that will affect the practice of data science in a variety of domains

  Student Learning Outcomes: Develop capabilities in a range of data mining techniques
  Gain the ability to solve problems in data mining research and practice
  Think critically about how to assess analytics
  Use data mining and analytics in a domain of application

  Rules & Requirements

  Prerequisites: Corequisite: Data/CompSci/Stat C100 (C- or better, or Pass, required if completed prior to Data 144)

  Credit Restrictions: Students will receive no credit for DATA 144 after completing INFO 154. A deficient grade in DATA 144 may be removed by taking INFO 154.

  Hours & Format

  Fall and/or spring: 15 weeks - 3 hours of lecture per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Alternative to final exam.

  Instructor: Pardos

  DATA C182 Designing, Visualizing and Understanding Deep Neural Networks 4 Units

  Terms offered: Fall 2024, Spring 2008, Spring 2007, Spring 2006
  Deep Networks have revolutionized computer vision, language technology, robotics and control. They have growing impact in many other areas of science and engineering. They do not however, follow a closed or compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling,
  practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground.
  Designing, Visualizing and Understanding Deep Neural Networks: Read More [+]

  Objectives & Outcomes

  Student Learning Outcomes: Students will come to understand visualizing deep networks. Exploring the training and use of deep networks with visualization tools.
  Students will learn design principles and best practices: design motifs that work well in particular domains, structure optimization and parameter optimization.
  Understanding deep networks. Methods with formal guarantees: generative and adversarial models, tensor factorization.

  Rules & Requirements

  Prerequisites: MATH 53, MATH 54, and COMPSCI 61B; COMPSCI 70 or STAT 134; COMPSCI 189 is recommended

  Credit Restrictions: Students will receive no credit for COMPSCI 182 after completing COMPSCI W182, or COMPSCI L182. A deficient grade in COMPSCI 182 may be removed by taking COMPSCI L182, COMPSCI W182, COMPSCI W182, or COMPSCI L182.

  Hours & Format

  Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Alternative to final exam.

  Instructor: Gonzalez

  Formerly known as: Computer Science 182

  Also listed as: COMPSCI C182

  DATA 188 Advanced Data Science Connector 2 - 4 Units

  Terms offered: Spring 2024
  Designed to be taken concurrently with or after Principles and Techniques of Data Science (Data C100) or Probability for Data Science (Data C140) or both, each connector course consists of an intensive study of data science ideas in a particular field. Topics include the development of the theory of data science, the application of data science in a variety of domains, its human and social contexts, and anything else. Topics vary by field, and more than one topic may be offered in a semester.
  Advanced Data Science Connector: Read More [+]

  Objectives & Outcomes

  Course Objectives: Develop theoretical mastery in data science topics, address the challenges of gathering data and converting it to usable formats, develop skills in selecting appropriate data science methods, explore approaches to decision-making and effective communication.

  Student Learning Outcomes: Understand and apply theory in an area of data science, or follow the data science life cycle in a domain of application from question formulation to the use of advanced data science methods and the communication of results.

  Rules & Requirements

  Prerequisites: Prerequisites or corequisites may vary depending on topic. Consult the Schedule of Classes or department website for details

  Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.

  Hours & Format

  Fall and/or spring: 15 weeks - 2-3 hours of seminar and 1-1 hours of discussion per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Alternate method of final assessment during regularly scheduled final exam group (e.g., presentation, final project, etc.).

  DATA H195A Data Science Honors Thesis Seminar 2 Units

  Terms offered: Prior to 2007
  The senior honors thesis seminar gives students an opportunity to experience firsthand what it means to do data science research. Over two semesters, students will learn to formulate a research problem, design a research strategy, collect evidence, and write up the findings and analysis. The first semester focuses primarily on the preparation and implementation of a research proposal, as well as data management strategies. During the second semester, we will emphasize analysis and writing. The final result will be a hybrid product with a 20-25 page research paper, with data visualizations and analysis tables, along with a documented data source, annotated code, well documented Github repository, and open science posting of the project.
  Data Science Honors Thesis Seminar: Read More [+]

  Objectives & Outcomes

  Course Objectives: Assist students with project organization and management.
  Convey approaches to effective writing and visual communication.
  Discuss how to formulate and substantiate an argument with evidence.
  Explain approaches to designing a research question and project.
  Explore a variety of analytic and visualization techniques and discuss their appropriateness to different research questions.
  Identify the challenges in data acquisition and management.

  Student Learning Outcomes: Communicate effectively in written, spoken, and graphical form.
  Develop an understanding of data availability, constraints, and ethics.
  Develop data management skills.
  Develop reproducible research and interpret results.
  Formulate a proposal for a research project.
  Learn how to develop a research question and project.
  Understand how to organize empirical work into a written document.
  Understand how to use empirical evidence to construct an argument.

  Rules & Requirements

  Prerequisites: There are no specific prerequisites. Students must be accepted into the data science honors program in order to take this course. Students must complete H195A in order to enroll in H195B

  Hours & Format

  Fall and/or spring: 15 weeks - 2 hours of seminar per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Alternative to final exam.

  DATA H195B Data Science Honors Thesis Seminar 2 Units

  Terms offered: Spring 2020
  The senior honors thesis seminar gives students an opportunity to experience firsthand what it means to do data science research. Over two semesters, students will learn to formulate a research problem, design a research strategy, collect evidence, and write up the findings and analysis. The first semester focuses primarily on the preparation and implementation of a research proposal, as well as data management strategies. During the second semester, we will emphasize analysis and writing. The final result will be a hybrid product with a 20-25 page research paper, with data visualizations and analysis tables, along with a documented data source, annotated code, well documented Github repository, and open science posting of the project.
  Data Science Honors Thesis Seminar: Read More [+]

  Objectives & Outcomes

  Course Objectives: Assist students with project organization and management.
  Convey approaches to effective writing and visual communication.
  Discuss how to formulate and substantiate an argument with evidence.
  Explain approaches to designing a research question and project.
  Explore a variety of analytic and visualization techniques and discuss their appropriateness to different research questions.
  Identify the challenges in data acquisition and management.

  Student Learning Outcomes: Communicate effectively in written, spoken, and graphical form.
  Develop an understanding of data availability, constraints, and ethics.
  Develop data management skills.
  Develop reproducible research and interpret results.
  Formulate a proposal for a research project.
  Learn how to develop a research question and project.
  Understand how to organize empirical work into a written document.
  Understand how to use empirical evidence to construct an argument.

  Rules & Requirements

  Prerequisites: There are no specific prerequisites. Students must be accepted into the data science honors program in order to take this course. Students must complete H195A in order to enroll in H195B

  Hours & Format

  Fall and/or spring: 15 weeks - 2 hours of seminar per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Letter grade. Alternative to final exam.

  DATA 197 Field Studies in Data Science 1 - 4 Units

  Terms offered: Fall 2019
  Students take part in organized individual field sponsored programs with off-campus organizations or tutoring/mentoring relevant to specific aspects and applications of data science on or off campus. Note Summer CPT or OPT students: written report required. Course may not count toward major requirements but will be counted in the cumulative units toward graduation.
  Field Studies in Data Science: Read More [+]

  Rules & Requirements

  Prerequisites: Consent of instructor (see department advisor). Upper-division standing

  Repeat rules: Course may be repeated for credit with advisor consent.

  Hours & Format

  Fall and/or spring: 15 weeks - 1-4 hours of fieldwork per week

  Summer:
  6 weeks - 2.5-10 hours of fieldwork per week
  8 weeks - 2-7.5 hours of fieldwork per week
  10 weeks - 1.5-6 hours of fieldwork per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Offered for pass/not pass grade only. Alternative to final exam.

  DATA 198 Directed Group Studies for Advanced Undergraduates 1 - 4 Units

  Terms offered: Fall 2024, Spring 2024, Fall 2023
  Written proposal must be approved by a faculty sponsor, who will serve as Instructor of Record. Seminars for the group study of selected topics, which will vary from semester to semester. Topics may be initiated by students.
  Directed Group Studies for Advanced Undergraduates: Read More [+]

  Rules & Requirements

  Prerequisites: Instructors may require students to enroll concurrently or have completed Data 8 (COMPSCI/STAT/INFO C8). Upper-division standing and consent of instructor

  Repeat rules: Course may be repeated for credit without restriction. Students may enroll in multiple sections of this course within the same semester.

  Hours & Format

  Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

  DATA 199 Supervised Independent Study and Research 1 - 4 Units

  Terms offered: Prior to 2007
  Independent study and research by arrangement with faculty or staff. This course allows students to obtain course credit for participation in undergraduate research. Students may opt either to participate in a semester-long series of workshops which provide a guided research experience with project milestone assignments and regular feedback, or they may opt to work independently with supervision from one faculty research mentor.
  Supervised Independent Study and Research: Read More [+]

  Objectives & Outcomes

  Student Learning Outcomes: Develop and refine skills acquired in other courses in a hands-on, self-directed research project.
  Identify how to properly manage data and describe best practices in programming and analytics.
  Integrate feedback from an instructor into research on a regular basis.
  Learn how to structure and complete a research project working independently.

  Rules & Requirements

  Prerequisites: Instructors may require students to enroll concurrently or have completed Data 8 (COMPSCI/STAT/INFO C8). Upper-division standing and consent of instructor

  Repeat rules: Course may be repeated for credit without restriction.

  Hours & Format

  Fall and/or spring: 15 weeks - 3-12 hours of independent study per week

  Summer:
  6 weeks - 7.5-30 hours of independent study per week
  8 weeks - 5.5-22.5 hours of independent study per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Undergraduate

  Grading/Final exam status: Offered for pass/not pass grade only. Alternative to final exam.

  DATA 200S Principles and Techniques of Data Science 3 Units

  Terms offered: Fall 2024, Spring 2024, Fall 2023
  Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project.
  Principles and Techniques of Data Science: Read More [+]

  Rules & Requirements

  Prerequisites: DATA/COMPSCI/INFO/STAT C8; and COMPSCI 61A or COMPSCI/DATA C88C. Corequisites: MATH 54 or EECS 16A

  Credit Restrictions: Students will receive no credit for DATA 200S after completing DATA C100, or DATA C200. A deficient grade in DATA 200S may be removed by taking DATA C200.

  Hours & Format

  Fall and/or spring: 15 weeks - 3-3 hours of lecture, 1-1 hours of discussion, and 0-1 hours of laboratory per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Graduate

  Grading: Letter grade.

  DATA C200 Principles and Techniques of Data Science 4 Units

  Terms offered: Fall 2024, Spring 2024, Fall 2023, Spring 2023, Spring 2022, Spring 2021, Spring 2020
  Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project.
  Principles and Techniques of Data Science: Read More [+]

  Rules & Requirements

  Prerequisites: COMPSCI C8 / INFO C8 / STAT C8 or ENGIN 7; and either COMPSCI 61A or COMPSCI 88. Corequisites: MATH 54 or EECS 16A

  Credit Restrictions: Students will receive no credit for DATA C200\COMPSCI C200A\STAT C200C after completing DATA C100.

  Hours & Format

  Fall and/or spring:
  8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week
  15 weeks - 3-3 hours of lecture, 1-1 hours of discussion, and 0-1 hours of laboratory per week

  Summer: 8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Graduate

  Grading: Letter grade.

  Formerly known as: Statistics C200C/Computer Science C200A

  Also listed as: COMPSCI C200A/STAT C200C

  DATA C204 Human Contexts and Ethics of Data 4 Units

  Terms offered: Fall 2024, Spring 2024
  This course teaches you to use approaches from the across the humanities and interpretive social sciences and tools of Science,
  Technology, and Society (STS) to recognize, analyze, and shape the human contexts, social implications, and ethics of data and data technologies, including data analytics, algorithmic decision systems, machine learning (ML), and artificial intelligence (AI).
  Human Contexts and Ethics of Data: Read More [+]

  Rules & Requirements

  Prerequisites: Graduate standing or permission of the instructor. Graduate students without previous (undergraduate or graduate-level) preparation in the interpretive social sciences or humanities are encouraged to confer with the instructor before enrolling

  Hours & Format

  Fall and/or spring: 15 weeks - 2 hours of seminar per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Graduate

  Grading: Letter grade.

  Instructor: Carson

  Also listed as: HISTORY C254/STS C204

  DATA 298 Directed Group Studies for Graduates 1 - 4 Units

  Terms offered: Prior to 2007
  DATA 298 provides credit for directed group study by graduate students working closely with an instructor who is a faculty member. Students are responsible for finding an instructor to supervise their work, and they will meet with that instructor weekly or bi-weekly. Faculty members must commit to supervising and evaluating the students’ work and be available to meet regularly as required by the guidelines.
  
  Directed Group Studies for Graduates: Read More [+]

  Rules & Requirements

  Prerequisites: Instructors may require students to enroll concurrently or have completed Data C8 (COMPSCI/STAT/INFO C8) or or Data C200 (COMPSCI C200A/STAT C200C). Graduate standing and consent of instructor

  Repeat rules: Course may be repeated for credit without restriction. Students may enroll in multiple sections of this course within the same semester.

  Hours & Format

  Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Graduate

  Grading: Letter grade.

  DATA 375 Professional Preparation: Teaching of Data Science 2 Units

  Terms offered: Fall 2024, Spring 2024
  Discussion and practice of techniques for effective teaching of student-centered learning, focusing on issues most relevant to teaching assistants in data science courses. Discussion, review and development of formative and summative assessments, guidance of laboratory classes, course development, supervised practice teaching, and culturally relevant pedagogy.
  Professional Preparation: Teaching of Data Science: Read More [+]

  Rules & Requirements

  Prerequisites: Concurrent Teaching Assistant appointment required

  Hours & Format

  Fall and/or spring: 15 weeks - 2 hours of lecture per week

  Additional Details

  Subject/Course Level: Data Science, Undergraduate/Professional course for teachers or prospective teachers

  Grading: Offered for satisfactory/unsatisfactory grade only.