Connect with experienced professionals from around the world.
Learn BigML, Kaggle, Decision Trees, Regressions in Excel, and more.
Engage in live, synchronous discussions with faculty and classmates.
Participate in Q&A with managers from leading Fortune 500 tech companies.
ABOUT THIS COURSE
This course provides an understanding of the role of artificial intelligence in managerial decision-making. It surveys different methods in unsupervised and supervised machine learning with a focus on exploiting firm data for strategic advantage. Learners will acquire the language and framework to talk to both technical experts and executives in order to better oversee the practical application of artificial intelligence and data science in their respective organizations.
Throughout the course, you will examine problems in marketing, operations, workforce management, and finance through the lens of data science and machine intelligence. You will focus on the role of managers as both consumers and producers of information, illustrating how finding and developing the right data and applying appropriate statistical methods can help solve business problems.
This cohort-based course covers a new module each week. In addition to the video lectures, case studies, and interviews with industry experts on the platform, you will have opportunities to interact with the faculty. Teaching assistants also will host weekly video conferences to field questions and facilitate discussions. The course culminates with a practical capstone project applying what you learned to your own business context.
Exclusive video lectures, interviews, case studies, short assessments, and discussion boards
Continuous project work alongside facilitators and faculty with live video office hours each week
Live, faculty-led case discussions and lectures
This course is expected to launch in the spring of 2020. Join the mailing list to receive updates about the dates for the first cohort.
INTRODUCTION TO DATA SCIENCE AND DATA STRATEGY
DESCRIPTIVE AND PREDICTIVE ANALYTICS
DATA UNDERSTANDING AND DATA PREPARATION
CLUSTERING AND NEAREST NEIGHBOR
VISUALIZING MODEL EVALUATION
NETWORKS AND ENSEMBLES
Gregory LaBlanc has been teaching at the UC Berkeley School of Law and Haas School of Business since 2005. His research interests lie at the intersection of law, finance, and psychology in the areas of business strategy and risk management. He has been the recipient of numerous teaching awards at UC Berkeley, including the Earl F. Cheit Award for Outstanding Teaching (2009) and the Haas EWMBA Core Graduate Instructor of the Year (2004-2005). Gregory has been instrumental in developing and teaching innovative courses, such as Blockchain and the Future of Technology, Business, and Law, to keep Berkeley students on the cutting edge of the increasing complex and ever-changing business environment.
Prior to his tenure at UC Berkeley, he held teaching positions at Wharton, Duke, and the University of Virginia. He has also worked outside of academia in the areas of competitive intelligence, litigation consulting, and advising. He has consulted numerous Fortune 500 companies in finance, marketing, and strategy. Gregory received B.A. and B.S. degrees from the University of Pennsylvania before pursuing further graduate studies there as a University Scholar and graduate fellow. He later earned a J.D. from George Mason University and an LLM from Berkeley Law.
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