Spring 2021 Courses

Questions about enrolling in a Data course? Start by reviewing our Spring 2021 Enrollment FAQs

Also check for updates on the Data 001 Piazza page and the class notes on the Schedule of Classes.

  • For questions about enrolling in Data courses, please contact us at ds-enrollments@berkeley.edu.
  • 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).

The Data Science Education Program is excited to announce a new class being offered for the first time in Spring 2021:

Data 4AC - Data and Justice (4 units)

Class Number # 33106

TuTh 2:00-3:30pm

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.

This course satisfies American Cultures.


Interested in becoming an Academic Student Employee for Data Science? 

Visit ASE Application portal

**Please be sure to fill out the main form linked above AND the CDSS Supplemental info form linked at the bottom of the main form.**

Priority Deadline to Apply for Spring 2021: October 30, 2020

Applications may be submitted after October 30 but your best chance for being considered is to submit BOTH applications by the priority deadline.

Connectors

Please check the Schedule of Classes for the most up-to-date information on times, locations and remote availability.

Title Course Number Description Instructor
Data Science for Smart Cities

CivEng C88 #32460

CyPlan C88 #21319

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

CompSci 88 #28893 #29286

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.
Economic Models

Data 88E

#33109

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.
Data Science Applications in Physics

Physics 88 #25417

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 #26483 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. Harding, D.
Probability and Mathematical Statistics in Data Science Stat 88 #24180 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. Murali Stoyanov, S.
Data and Decisions UGBA 88 #20737 #20738 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.