Michael Li and Rich Ott offer a nontechnical overview of AI and data science. Learn common techniques, how to apply them in your organization, and common pitfalls to avoid. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.
Introduction to AI and data science
Algorithms and techniques
Industry use cases
AI within the organization
Common pitfalls and fallacies in AI and data science
Tianhui Michael Li is the founder and president of the Data Incubator, a data science training and placement firm. Michael bootstrapped the company and navigated it to a successful sale to the Pragmatic Institute. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, J.P. Morgan, and D.E. Shaw. He’s a regular contributor to the Wall Street Journal, Tech Crunch, Wired, Fast Company, Harvard Business Review, MIT Sloan Management Review, Entrepreneur, Venture Beat, Tech Target, and O’Reilly. Michael was a postdoc at Cornell Tech, a PhD at Princeton, and a Marshall Scholar in Cambridge.
Richard Ott is a data scientist in residence at the Data Incubator, where he combines his interest in data with his love of teaching. Previously, he was a data scientist and software engineer at Verizon. Rich holds a PhD in particle physics from the Massachusetts Institute of Technology, which he followed with postdoctoral research at the University of California, Davis.
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