Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY
Jennifer Shin

Jennifer Shin
Founder and Adjunct Professor, 8 Path Solutions | NYU Stern | IBM

Website

Jennifer Shin is the founder of data science, analytics, and technology company 8 Path Solutions and an adjunct professor at New York University’s Stern School of Business. An experienced data scientist and management consultant, Jennifer has led complex, large-scale, and high-profile projects as a product director at NBCUniversal, director of data science at Comcast, senior principal data scientist at The Nielsen Company, and management consultant at GE Capital, the Carlyle Group, Fortress Investment Group, the City of New York, and Columbia University. Previously, Jennifer taught courses in statistics, data science, and business at UC Berkeley, the Columbia Business School, and the City University of New York. She is internationally recognized as a thought leader, influencer, and expert in data science, business, and technology by governments, corporations, and academic institutions. Jennifer has several patents and trademarks related to data science, machine learning, and AI, has published research in peer-reviewed journals, and has been featured in news publications, press conferences, and on billboards in Times Square and the Vegas Strip. She serves on the data science committee for the Grace Hopper Conference, the advisory board for the data science graduate program at City University of New York, and the advisory board for up-and-coming startups. Jennifer holds an undergraduate degree in economics, mathematics, and creative writing and a graduate degree in statistics, both from Columbia University.

Sessions

11:20am–12:00pm Thursday, 09/13/2018
Location: 1A 01/02
Jennifer Shin (8 Path Solutions | NYU Stern | IBM)
Common wisdom dictates that we should never make assumptions, but assumptions are essential in the creation of statistical models. Jennifer Shin explores how assumptions fit into the creation of a statistical model, the pitfalls of applying a model to data without taking the underlying assumptions into account, and how to identify datasets where the model and its assumptions are applicable. Read more.
4:20pm–5:00pm Thursday, 09/13/2018
Location: 1A 01/02
Jennifer Shin (8 Path Solutions | NYU Stern | IBM)
Common wisdom dictates that we should never make assumptions, but assumptions are essential in the creation of statistical models. Jennifer Shin explores how assumptions fit into the creation of a statistical model, the pitfalls of applying a model to data without taking the underlying assumptions into account, and how to identify datasets where the model and its assumptions are applicable. Read more.