Learn to speak the language of analytics to make your strategy better and more profitable in the Data Strategy for Business Leaders program. This course will help you assess and modify your company’s data strategy as well as design new business models around data collection and analytics.
Learn to leverage the data collected from your consumers to get valuable insights and make effective marketing decisions in the Data Driven Marketing for Business Leaders program. This course will help you to understand practical aspects of working with data, drive marketing objectives based on data analysis and acquire and retain consumers
Discounts available! 10% off for people from the same company. 30% off for Haas Alumni (using code IBI2017). 30% off if you register for both courses. Learn more.
The Haas School of Business has incorporated recent developments of data science and business analytics in many of its activities, including research done by the faculty, teaching, conferences, and roundtables. Housed at the Institute for Business Innovation, the Program in Data Science and Strategy is an expanding initiative that seeks to disseminate pioneering research, train business students, and form partnerships with the business and financial communities to incorporate data science into business decisions and policy-making. Read more about the program.
For program development opportunities, please contact Maria Carkovic: MCarkovic@Berkeley.edu
LECTURE SERIES: La Blanc debuted Data Science/Data Strategy in spring 2015 for a full house of 60 full-time and 60 part-time MBA students. La Blanc’s newest course, Analytics for Workforce, Workplace and Wellness, showed how data can be collected and used to solve traditional management problems such as hiring, retention, and productivity. La Blanc has worked with Haas Data Science Club members to organize a speaker series featuring industry practitioners. See the lecture series page.
ANALYTICS COURSE: Applied Data Analytics, co-taught by Dave Rochlin, executive director of Haas@Work, and Visiting Asst. Prof. Thomas Lee gave students hands-on experience with big-data projects for Accenture last spring. Student teams from Haas and the UC School of Information worked with Accenture’s big data group on data-driven projects, solving challenging real-world issues for Accenture’s clients.
Read more. View a 5-minute video about the course.
Research in data science and strategy covers a wide array of topics such as the effect of information on business decisions and market outcomes; the analysis of financial data; the effects of high-frequency trading; demand and supply analysis; and the use of data in behavioral targeting and marketing. Several business school faculty members who are active scholars and thought leaders in this area are listed here, together with a brief description of their work in data analytics:
Ned Augenblick: Research that uses large sets of choice data to empirically test predictions of different behavioral theories. Examples include bidding decisions in online auctions, voting behavior in California elections, and employment decisions in large online marketplaces. [More about Ned Augenblick]
Lucas Davis: Empirical studies of electricity and natural gas regulation, pricing in competitive and non-competitive markets, and the economic and business impacts of environmental policy. Large datasets ranging from microdata on electricity consumption and bills, to hourly measures of performance by U.S. nuclear power plants, to high-frequency measures of air quality. [More about Lucas Davis]
Paul Gertler: Research on energy efficiency in Mexico using data from the electricity bills on the universe of households (25 million). Performance incentives in maternal and child health care in Argentina using birth medial and death records. Analysis of the effect of coupons on generic drug use with billing records from 4 California Insurance companies. [More about Paul Gertler]
Jose Guajardo: Empirical analysis of markets with interactions between products and services. Drivers and effects of business model innovation in different domains (manufacturing, energy, mobile). Matching supply and demand through Operations. [More about Jose Guajardo]
Terry Hendershott: Analysis of electronic, algorithmic, and high-frequency trading in financial markets. Modeling asset price and trading dynamics. Measuring liquidity, price discovery, and efficiency. [More about Terry Hendershott]
Ganesh Iyer: Customization and the internet. Referral infomediaries. Targeting of advertising. Information acquisition and information sharing. [More about Ganesh Iyer]
Przemyslaw Jeziorski: Empirical analysis of consumer demand for search advertising. Effects of branding in sponsored search advertising. Empirical dynamic analysis of market interactions. Dynamic auctions. [More about Przemyslaw Jezioski]
Zsolt Katona: Analysis of large social and communication networks: Applications in marketing. Co-location effects in mobile marketing. Analysis of bidding for sponsored search and the role of search engine optimization. [More about Zsolt Katona]
David Levine: Analysis of large administrative datasets on companies and workers. Analysis of sensor data, with specific attention of turning physical measures such as degrees Celsius on a temperature sensor into hours of cooking on a traditional stove. [More about David Levine]
Minjung Park: Estimating dynamic consumer response to firm actions in financial services. Measuring the effect of advertising. Regression discontinuity design with endogenous variables. Empirical analysis of individual consumer choice in health care markets. [More about Minjung Park]
Carl Shapiro: Author of “Information Rules: A Strategic Guide to the Network Economy” (with Hal Varian). Research on competitive strategies in markets for information goods. [More about Carl Shapiro]
Richard Stanton: Research on empirical analysis of mortgage and lease markets. Estimation of dynamic interest rate term structure models. Testing for regulatory-capital arbitrage and ratings inflation in the commercial mortgage-backed security markets. [More about Richard Stanton]
Steven Tadelis: Research on the effects of information revelation in offline and online auction markets. Uses large-scale experimentation and marketplace data to uncover the effects of online advertising. Studying the roles played by online reputation systems in large anonymous marketplaces. [More about Steven Tadelis]
Miguel Villas-Boas: Research on the use of customer data for behavioral pricing and behavioral targeting. Modeling consumer search in the internet, with many search alternatives and large information sets. Measuring consumer response with market data and endogeneity. [More about Miguel Villas-Boas]
Reed Walker: Research using large scale firm and/or individual microdata to understand how environmental policy affects firm operating decisions while also trying to better understand the benefits of environmental policy through its effect on population health. [More about Reed Walker]
Nancy Wallace: Housing price indices and models to monitor residential real estate price movements over the business cycle. Mortgage prepayment and pricing models, mortgage contract design, and the market microstructure of the mortgage market. [More about Nancy Wallace]
Catherine Wolfram: Research on energy and environmental markets using high-frequency data on both production and customer demand. Topics include incentives for and returns to investment in energy efficient technologies, firm response to both existing and planned environmental regulations and optimal policy design. [More about Catherine Wolfram]
The Haas School offers also several courses across different programs on the intensive use of data in business. Some of these courses are:
Data and Decisions: (MBA program) This core course teaches students to be critical consumers of statistical analysis using available software packages. Key concepts include interpretation of regression analysis, model formation and testing, and diagnostic checking.
Marketing Analytics: (MBA program) This course presents a scientific approach to marketing with hands-on use of technologies such as databases, analytics and computing systems to collect, analyze, and act on customer information. While students employ quantitative methods in the course, the goal is not to produce experts in statistics; rather, students gain the competency to interact with and manage a marketing analytics team. The course uses a combination of lectures, cases, and exercises to learn the material. This course takes a very hands-on approach with real-world databases and equips students with tools that can be used immediately on the job.
Social Media Marketing: (MBA program) This course covers several topics in social media marketing. In particular the course covers the differences and interaction between traditional and social media, two-sided markets and social media platforms (including verticals such as gaming, shopping and entertainment), basic theory of social networks online and offline (graph theory, sociology, information diffusion), consumer behavior and digital media, best marketing strategies for paid and unpaid social media, and implementation, analytics and measurement of ROI.
High Frequency Finance: (MFE program) This course links theoretical insights of the economic forces that shape investing and trading decisions in financial markets to the data that financial markets produce. Students develop skills for understanding and processing market data to develop models for investing and trading. Risk measurement and challenges in building and testing models on historical data are emphasized.
Empirical Methods: (MFE program) plus data emphasis and data lab at MFE program.
Choice Models: (PhD program) Analysis of individual choice data. static and dynamic. Modeling consumer choice. Identification and causality with market data. Field experiments.
Conferences and roundtables
SICS, Marketing article, and pictures
Leading the Way on Big Data Conference http://www.datalead2014.com/
Fisher CIO roundtable this Fall:
November 4 Fisher Silicon Valley Roundtable
Topic: How enterprises can take advantage of Big Data, Speaker: Sreeni Garlapati, Meru Networks