The Fisher Center for Business Analytics coordinates a range of educational programs that applies UC Berkeley’s leadership in Data Science to business and reflects the distinct brand of the Berkeley Haas Defining Leadership Principles:


The business analytics curriculum at Berkeley-Haas is framed not as a distinct subject but as fundamental to every discipline. Analytics is thus diffused throughout the general, MBA curriculum.

All students, regardless of degree program or concentration, enroll in Data and Decisions, a core course that introduces exploratory statistics and causal analysis.

As depicted in the figure, three primary courses extend Data and Decisions. The syllabi are coordinated to maximize complementarities among methods and problem-domains. A student interested in the Business Analytics program of study takes at least one of the primary courses.

Beyond the primary courses are a number of secondary, domain-specific courses. Each includes additional quantitative, analytics modules.  A student pursuing the Business Analytics program of study is expected to complete at least two secondary courses.


Berkeley-Haas trains collaborative decision-makers to lead interdisciplinary teams that integrate domain experts and data scientists. This training proceeds in two ways:

Experiential Learning

Students from across the University enroll in experiential learning courses at Haas. Students from graduate programs throughout the University bring their data science and/or domain expertise. Berkeley-Haas students contribute business strategy and leadership. Together, interdisciplinary student teams address corporate, non-profit and entrepreneurial challenges.  

The University of California at Berkeley

UC Berkeley’s programs in Statistics, Computer Science, Mathematics, and Information are defining the leading edge of Data Science. Haas students have the opportunity to pursue advanced studies from global experts across the University.  


Haas programs in Business Analytics extend beyond degree programs to support continuing education and industry best practices in at least two ways:

Executive and Professional Education

Executive and Professional Education Programs in Business Analytics bring business leaders to campus to continue their lifelong education.  Courses in analytics include open-enrollment programs as well as opportunities for custom programs through Berkeley’s Center for Executive Education.

CIO Leadership

The Center for Business Analytics facilities idea exchange and thought leadership between industry leaders in a peer-to-peer setting through its CIO Leadership Program.

  • The program helps coordinate three regional networks of strategic information technology leaders who meet monthly to educate and support one another (Silicon Valley, San Francisco, East Bay).
  • With the Gartner Group, the Center established the Fisher-Hopper Prize for Lifetime Achievement in CIO Leadership.  Each year, the prize ceremony is part of an annual, day-long seminar on strategic information technology


Through the support of the Ryoichi Sasakawa Foundation, the Center administers Fellowships to PhD students applying analytics in their doctoral research. Current Fellows in Business Analytics are:

Oren Reshef


Oren Reshef’s research is entitled “Information Simplification and Strategic Reaction: Evidence from the Israeli Food Market.”

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In a world of complex choices, consumers may make uninformed decisions even when all relevant information is readily available. A common policy suggestion is to aid consumers by making information more accessible, simple, and standardized. The goal of this project is to study the general equilibrium effects of the provision of simple information. Specifically, the focus is on a nationwide, mandatory labeling policy, advocated by the Israeli Ministry of Health, to promote healthy nutrition. Oren Reshef develops a simple conceptual framework to formalize the mechanisms governing consumers’ and firms’ behavior examining consumers’ understanding and usage of the simplified labels and whether demand for healthier products changes as a result. Oren focuses on the unintended consequences of the policy, especially those affecting individual information acquisition and demand for nutritional dimensions not explicitly included in the labeling. Additionally, he studies the supply side response by examining how the shift in incentives drives product innovation, reformulation, and readjustment of pricing strategies. Finally, he derives the aggregated welfare implications.

Xin Chen


Xin Chen is working on a project entitled “What is a Share worth” where she studies, a fast-growing Chinese online grocery platform with a founding mission of helping
solve China’s food security issue.

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The study aims to leverage the power of data analytics to help Benlai improve its social advertising ROI and therefore to serve more Chinese people with high quality food. The project includes a multi-level randomization design to evaluate’s Wechat social advertising effectiveness. Ex ante, it is unclear whether Benlai’s current “must share” social promotion benefits or hurts its bottom line. Through a series of field experiments, the project quantifies the aggregate cost or benefit, as well as decouples the consumer sharing cost from the word-of- mouth benefits for Benlai both in the short and long run. By examining the heterogeneous treatment effects using machine leaning tools, such as causal trees and forests, the study helps Benlai identify potential customer segments with high lifetime value to target for improved ROI. The study will also help Benlai explore other innovative social advertising design to broaden its impact, such as through red packet social promotion.

Richard Lu


Richard Lu is working on a project with Jenny Chatman, Amir Goldberg, and Sameer Srivastava that is focused on understanding how cognition and behavior relate to one another.

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Using survey data and email data from a mid-sized organization, he is establishing the linkages between the cognitive manifestations of cultural fit– that is, how someone thinks about how they fit into an organization based on self-reports- -and the behavioral manifestations– based on linguistic similarity between a person and her interlocutors in internal email communications. He is using the tools of computational linguistics and machine learning to identify the “linguistic signature” of cognitive cultural fit so that we can impute self-reported cultural fit for people and time periods where these data are not available. This will enable him to transform a one-time survey into a longitudinal assessment, which will in turn enable him to examine the dynamic interplay between cognitive and behavioral cultural fit and its consequences for individual performance.

Santiago Truffa


Santiago Truffa is working on an ambitious project about the agglomeration of talent and the economics of inequality across and within cities.

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This is a project that aims to account for a complex empirical pattern within a unified framework that can speak to the data. The pattern involves cross-city differentials: vast inequality in wages and property prices, and differences in the human capital that on average flocks to different cities. But in addition, individual human capital and wages differ within cities. The understanding of these patterns require a general equilibrium framework, which Santiago has developed, and one that is estimable. In order to estimate the role of the key forces at play in driving the various inequalities in the U.S., Santiago has launched into fascinating data work. On the one hand, he has collected data on wages, city amenities, and property values in 50 metropolitan areas in the U.S., and analyzed those data to first describe the overall patterns, and then used the data to estimate his model. In addition, Santiago has worked with a leading online brain training company (Lumosity) to obtain from them their user data. Santiago has succeeded and is now in the process of analyzing it. These data are exciting because they include millions of observations of users all over the country, that among other things allow us to produce an “IQ map” that helps us better understand the distribution of talent over space. These data complement his analysis of the human capital distribution across cities, but they have great additional potential. An obvious challenge in using these data is the fact that they reflect self-selection into using a brain-training service. Santiago has deployed state-of- the-art selection techniques to undo these selection effects, which tells me there is truly fantastic potential for the use of these data.

Jean Zeng


Jean Zeng plans to work on a novel and very comprehensive database with industry-level analytics, including long-term forecasts of value drivers (e.g., employment growth, operating performance etc.).