Title: The Role of Inference and Prediction in Data Science for Entertainment
Abstract: The emerging field of data science, and the notion that statistical modeling and machine learning can unlock latent value in data held or acquired by businesses, has already had a transformative impact on many industries and disciplines. In particular, many companies have benefited from accurate predictive systems deployed at scale. Hollywood studios leverage predictive modeling to target advertisements, recommend content, and more. Such predictive applications have proven successful enough that 'data science' has in some cases become synonymous with 'prediction.' However, inferential applications of modeling are equally important. Statistical inference provides the basis for people to learn from models and improve decision making, in both scientific and industrial contexts. In entertainment, this includes assessing the impact of a blockbuster film's marketing campaign and inferring the thematic composition of content like TV shows. In this talk, I will introduce applications of both predictive and inferential statistical and machine learning systems in the entertainment industry and highlight the critical importance of investment in interpretation and communication to enable data and models to be leveraged efficiently.
Bio: Nathan Sanders is the Chief Scientist at Warner Media Applied Analytics. He leads a team of social, physical, and computational scientists engaged in deploying data science and human subjects research techniques to better understand and serve consumers of entertainment products including film, TV, and digital video. Prior to that, Nathan built and led the Quantitative Analytics team at Legendary Entertainment's Applied Analytics division, which was acquired by Warner Media in 2018. Nathan is also a co-founder and Leadership Team Chair of ComSciCon (http://comscicon.org/), the international workshop series on science communication for graduate students. Nathan did his undergraduate work at Michigan State University and earned his PhD in Astronomy and Astrophysics from Harvard University.