The Data Preparation Cycle from Exploration to Acquisition to Feature Engineering (panel)

Big Data is proving its value in many application areas. But what can you do when you don’t have a large data set? Or when you have a large amount of data, but not much labeled data in the domain of the prediction you need to model? This panel considers the potential of data exploration, feature selection/creation, and non-supervised machine learning to deliver value when relevant labeled data is scarce. Moderator: Henry Morris, PhD, Principal, Henry Morris Analytics; Panelists: Suresh Kumar, Head of AI Solutions, PARC; Max Kirby, Director of Cloud Platform Solutions, Publicis.Sapient/Google