Predictions in Excel through Estimating Missing Values

Abstract: In this workshop, we introduce a new data analysis tool that enables predictions in Excel-like environment **without** any prior knowledge of Machine Learning, Statistics or Data Science. This, seemingly magical, ability is direct consequence of viewing the question of prediction as estimating missing values or correcting errors within observations. More precisely, this boils down to estimating a structured "tensor" from its noisy, missing observations. We will show an intuitive, simple and scalable approach for estimating tensor as well as provide a collection of case-studies using an actual tool.

Bio: Devavrat Shah is a Professor with the department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology. His current research interests are at the interface of Statistical Inference and Social Data Processing. His work has been recognized through prize paper awards in Machine Learning, Operations Research and Computer Science, as well as career prizes including 2010 Erlang prize from the INFORMS Applied Probability Society and 2008 ACM Sigmetrics Rising Star Award. He is a distinguished young alumni of his alma mater IIT Bombay.