Abstract: The workshop will present how to
combine tools to quickly query, transform and model data using
command line tools.
The goal is to show that command line tools are efficient at handling reasonable sizes of data and can accelerate the data science
process. We will show that in many instances, command line processing ends up being much faster than ‘big-data’ solutions. The content
of the workshop is derived from the book of the same name (http://datascienceatthecommandline.com/). In addition, we will cover
vowpal-wabbit (https://github.com/JohnLangford/vowpal_wabbit) as a versatile command line tool for modeling large datasets.
Biography: Sharat Chikkerur is a principal data scientist at Nanigans working on ads related modeling and optimization He has previously worked as a senior software engineer at Google and Microsoft working on large scale machine learning. He holds a PhD from Massachusetts Institute of Technology in Electrical Engineering and Computer Science and a M.S from University at Buffalo in Electrical Engineering (affiliated with CUBS). He several patents related to biometrics and computational advertising most of which were granted based on work at University at Buffalo with Prof. Govindaraju.