Questions about enrolling in a Data course?
- Start by reviewing our Spring 2022 Enrollment FAQs.
- Check for updates on the Data 001 Piazza page.
- Read the Class Notes for each class on the Schedule of Classes.
If you have checked the resources above and cannot find the answer to your question:
- For questions about enrolling in Data courses, please contact us at email@example.com.
- For questions about enrolling in other courses, please contact the department that manages the course (for example: for IND ENG 135, please contact IEOR; for COMPSCI 61B, please contact EECS).
Enrollment Permission Requests
Enrollment requests are reviewed as soon as possible, usually within 1-2 days.
Please note that enrollment permission will only be granted for the specific cases listed below:
- If you have satisfied one or more of the pre/co-requisites with an approved substitute course or outside of UC Berkeley, please submit an enrollment request for Data C100.
- If you have satisfied one or more of the prerequisites for Data C102 outside of UC Berkeley OR you are a graduating senior in Spring 2022 who will be declared in Data Science and you have not yet satisfied the Modeling, Learning & Decision Making requirement, please submit an enrollment request for Data C102. (Graduating seniors who need to meet MLDM may request permission to enroll in either Data/Stat C102 or CompSci 189 but not both).
- If you completed Stat 21/W21 instead of Stat 20 plus CompSci 61A, or an approved linear algebra course outside of UC Berkeley, please submit an enrollment request for Data C140.
CompSci 189, Lecture 2
- If you are a graduating senior in Spring 2022 who will be declared in the Data Science major and you have not yet satisfied the Modeling, Learning, & Decision-Making requirement, please submit an enrollment request for CompSci 189, Lecture 2. (Graduating seniors who need to meet MLDM may request permission to enroll in either Data/Stat C102 or CompSci 189 but not both).
Please check the Schedule of Classes for the most up-to-date information on times, locations and remote availability.
|Data Science for Smart Cities||Cities become more dependent on the data flows that connect infrastructures between themselves, and users to infrastructures. Design and operation of smart, efficient, and resilient cities nowadays require data science skills. This course provides an introduction to working with data generated within transportation systems, power grids, communication networks, as well as collected via crowd-sensing and remote sensing technologies, to build demand- and supply-side urban services based on data analytics.||Gonzalez, M.|
|Computational Structures in Data Science||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.||Friedland, G. & Ball, M.|
|Environmental Data Analysis||Explore the intersection of data science and environmental policy analysis through applications in environmental justice, carbon emissions, pollution regulation, and remote sensing.||Hammer, D.|
This Data Science connector course will motivate and illustrate key concepts in Economics with examples in Python Jupyter notebooks. The course will give data science students a pathway to apply python programming and data science concepts within the discipline of economics. The course will also give 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.
|Van Dusen, E.|
|Crime and Punishment: Taking the Measure of the US Justice System||
We will explore how data are used in the criminal justice system by exploring the debates surrounding mass incarceration and evaluating a number of different data sources that bear on police practices, incarceration, and criminal justice reform. Students will be required to think critically about the debates regarding criminal justice in the US and to work with various public data sets to assess the extent to which these data confirm or deny specific policy narratives. Building on skills from Foundations of Data Science, students will be required to use basic data management skills working in Python: data cleaning, aggregation, merging and appending data sets, collapsing variables, summarizing findings, and presenting data visualizations.
|Data Science Applications in Physics||Introduction to data science with applications to physics. Topics include: statistics and probability in physics, modeling of the physical systems and data, numerical integration and differentiation, function approximation. Connector course for Data Science 8, room-shared with Physics 77. Recommended for freshmen intended to major in physics or engineering with emphasis on data science.|
|Data Science for Social Impact||Sociol 88||This course explores the role of social research in policymaking and public decisions and develops skills for the communication of research findings and their implications in writing and through data visualization. Students will develop an understanding of various perspectives on the role that data and data analysts play in policymaking, learn how to write for a public audience about data, results, and implications, and learn how to create effective and engaging data visualizations.
|Probability and Mathematical Statistics in Data Science||Stat 88||In this connector course we will state precisely and prove results discovered while exploring data in Data 8. 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.||Stoyanov, S.|
|Data and Decisions||UGBA 88||The goal of this connector course is to provide an understanding of how data and statistical analysis can improve managerial decision-making. We will explore statistical methods for gleaning insights from economic and social data, with an emphasis on approaches to identifying causal relationships. We will discuss how to design and analyze randomized experiments and introduce econometric methods for estimating causal effects in non-experimental data. The course draws on a variety of business and social science applications, including advertising, management, online marketplaces, labor markets, and education. This course, in combination with the Data 8 Foundations course, satisfies the statistics prerequisite for admission to Haas.||Huntsinger, R.|