In this video, Dipayan introduces a framework data analysts should use to solve analytics problems. The topics covered include concepts in data hygiene and ways to figure out if something is systematically wrong with data. He goes on to elaborate what to look for in exploratory data analysis and how to build a predictive model. Finally, he discusses how to present and extract insights after the model has been built.
In short, you will get an exposure to the thinking process employed while trying to build a predictive model in the real world from start to finish.
By: Dipayan Maity
Dipayan is the co-founder at Insight Jedi and was formerly a senior manager with Mu Sigma. He specialties in credit risk, high dimensional modeling, sequential modeling, bayesian modeling, bayesian visual analytics, data mining, R, C and Python. He completed his PhD in statistics from Virginia Polytech