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Data Analytics Symposium | Keynote | Education 

Keynote Presenter

Ben Wellington Web.jpg

Ben Wellington, I Quant NY

Ben Wellington is the creator of I Quant NY, a data science and policy blog that focuses on insights drawn from New York City's public data, and advocates for the expansion and improvement of that data. His data science has influenced local government policy including changes in NYC street infrastructure, the way New Yorkers pay for cabs and the design of NYC subway vending machines, and his talk on urban data was featured on TED. 

Ben is a contributor to the New Yorker, and a visiting assistant professor in the City & Regional Planning program at the Pratt Institute in Brooklyn where he teaches statistics using ubran open data. He also works as a quantitative analyst at the investment management firm Two Sigma, where he helped start the firm's Data Clinic, a pro bono data analysis program that works with non-profits to harness the power of their own data.  Ben holds a Ph.D. in Computer Science (Natural Language Processing) from New York University.

Keynote Address
Gaining Insights (and Good Stories) from Data - Examples from Urban Data Science

Data is quickly transforming many industries, and non-profits are no exception. In this talk, I’ll explore how I’ve used my blog, I Quant NY, and Data Storytelling to make real changes in the city I live in: New York City. From parking ticket geography, to restaurant inspection scores to subway and taxi pricing, I will share tips on finding insights in your data, explore how storytelling is an important aspect of data science and highlight how these same techniques can be useful for non-profits.  

Along the way, I will point out that data science need not use complicated math and complex programs. I will show examples of the power of simple arithmetic, and show how often it is more about your curiosity and the questions you ask than the complexity of the equations you use. 

 

Claudia Perlich currently acts as chief scientist at Dstillery (previously m6d) and in this role designs, develops, analyzes, and optimizes the machine learning that drives digital advertising. She has published more than 50 scientific article and holds multiple patents in machine learning. She has won many data mining competitions and best paper awards at KDD and is acting as General Chair for KDD 2014. Before joining m6d in February 2010, Perlich worked in the Predictive Modeling Group at IBM’s T. J. Watson Research Center, concentrating on data analytics and machine learning for complex real-world domains and applications. She holds a PhD in information systems from NYU and an MA in computer science from Colorado University and teaches in the Stern MBA program at NYU.