Dealing with Missing Data and Data with Outliers

One of the most common problem that a data scientist faces in data cleaning/exploration analysis is the handling of missing (incomplete) data. In general, there is no one good way to deal with missing data. In statistics, an outlier is data point that is distant from other data points. Removing or correcting outliers is one of the difficult decisions during data analysis. In this talk, we will demonstrate different methods for dealing with missing data and how to detect outliers and when to remove or correct them.