This is an archived copy of the 2019-20 guide. To access the most recent version of the guide, please visit http://guide.berkeley.edu.
Courses
Terms offered: Not yet offered
An introduction to computational thinking and quantitative reasoning, preparing students for further coursework, especially Foundations of Data Science (CS/Info/Stat C8). Emphasizes the use of computation to gain insight about quantitative problems with real data. Expressions, data types, collections, and tables in Python. Programming practices, abstraction, and iteration. Visualizing univariate and bivariate data with bar charts, histograms, plots, and maps. Introduction to statistical concepts including averages and distributions, predicting one variable from another, association and causality, probability and probabilistic simulation. Relationship between numerical functions and graphs. Sampling and introduction to 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: Students will be able to perform basic computations in Python, including working with tabular data.
Students will be able to understand basic probabilistic simulations.
Students will be able to understand the syntactic structure of Python code.
Students will be able to use good practices in Python programming.
Students will be able to use visualizations to understand univariate data and to identify associations or causal relationships in bivariate data.
Rules & Requirements
Credit Restrictions: Students will receive no credit for DATA C6\COMPSCI C6\STAT C6 after completing DATA C8, or DATA 6. A deficient grade in DATA C6\COMPSCI C6\STAT C6 may be removed by taking DATA 6.
Hours & Format
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.
Formerly known as: Computer Science C8R/Statistics C8R
Also listed as: COMPSCI C6/STAT C6
Introduction to Computational Thinking with Data: Read Less [-]
Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020, Fall 2019, Spring 2019
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 - 3-3 hours of lecture and 2-2 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
Terms offered: Fall 2020, Spring 2020, Fall 2019
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.
Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020, Fall 2019
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; and COMPSCI 61A, COMPSCI 88, or ENGIN 7; Corequisite: MATH 54 or EECS 16A
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 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week
Summer: 8 weeks - 6 hours of lecture, 2 hours of discussion, 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 C100/Computer Science C100
Also listed as: COMPSCI C100/STAT C100
Terms offered: Fall 2020
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: Mathematics 54 or Mathematics 110 or Statistics 89A or Physics 89 or both of Electrical Engineering and Computer Science 16A and Electrical Engineering and Computer Science 16B; Statistics/Computer Science C100; and any of Electrical Engineering and Computer Science 126, Statistics 140, Statistics 134, Industrial Engineering and Operations Research 172. Statistics 140 or Electrical Engineering and Computer Science 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
Terms offered: Fall 2020, Spring 2020, Fall 2019
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.,Terms offered: Not yet offered
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
Human Contexts and Ethics of Data - DATA/History/STS: Read Less [-]
Terms offered: Fall 2020, Spring 2020, Fall 2019
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.,Terms offered: Not yet offered
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
Human Contexts and Ethics of Data - DATA/History/STS: Read Less [-]
Terms offered: Fall 2020, Fall 2019
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: Data 100 (COMPSCI/STAT C100) recommended
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
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.
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.
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.
Terms offered: Prior to 2007
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.
Directed Group Studies for Advanced Undergraduates: Read Less [-]
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.
Terms offered: Fall 2020, Spring 2020, Fall 2019, Spring 2019
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: Computer Science/Information/Statistics C8 or Engineering 7; and either Computer Science 61A or Computer Science 88. Corequisite: Mathematics 54 or Electrical Engineering 16A
Credit Restrictions: Students will receive no credit for STAT C200C\COMPSCI C200A\DATA C200 after completing DATA C100, or STAT 200C. A deficient grade in STAT C200C\COMPSCI C200A\DATA C200 may be removed by taking STAT 200C.
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/Graduate
Grading: Letter grade.
Formerly known as: Statistics C200C/Computer Science C200A
Also listed as: COMPSCI C200A/STAT C200C