Using Data Science To Predict The Future




Using Data Science to Predict the Future

24 October 2018

New York

Added 01-Jan-1970

Join The Data Incubator and Kirk Borne, Principal Data Scientist at Booz Allen Hamilton for the next installment of our free online webinar series, Data Science in 30 Minutes: Using Data Science to Predict the Future

Abstract: Predictive Analytics is currently one of the most significant and ubiquitous applications of Machine Learning in organizations. It is a major topic in business Data Strategy, Analytics Strategy, and Machine Learning Strategy discussions. This presentation will focus on new approaches to forecasting outcomes (predictive analytics) and to go even further: optimization of outcomes (prescriptive analytics). Specifically, we will invoke some common techniques and exploit them in new ways to "see around corners" with data.

Kirk Borne

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton based in McLean, Virginia USA. In those roles, he focuses on applications of data science, data analytics, machine learning, and artificial intelligence across a wide variety of disciplines. He also provides leadership and mentoring to multi-disciplinary teams of data scientists. He previously was Professor of Astrophysics and Computational Science at George Mason University for 12 years, where he taught, advised students, and performed research in the Data Science Bachelors program and the Computational Informatics Doctoral program. Prior to that, he worked 18 years on large data systems for NASA space science programs, including roles as Data Archive Project Scientist for NASA's Hubble Space Telescope and as the contract Program Manager in NASA's Space Science Data Operations Office. Kirk has a B.S. in Physics from LSU, and a Ph.D. in Astronomy from Caltech. He is a sought-after public speaker globally on data strategy topics. Since 2013, he has been identified as one of the top worldwide social influencers in Big Data and Data Science.

Michael Li founded The Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to Fellows, employers engage with the Incubator as hiring partners.

Previously, he worked as a data scientist (Foursquare), Wall Street quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He completed his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall Scholar. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup to focus on what he really loves.

Michael lives in New York, where he enjoys the Opera, rock climbing, and attending geeky data science events.