Explaining the curse of dimensionality using a relevant example
Linear programming in R along with sensitivity analysis and cool visualizations.
A complete analytical journey of linear regression. From EDA, model building, model diagnostics, residual plots, outlier treatment, co-linearity effects, transformation of variables, model re-building and validation for Boston housing price prediction problem.
Understanding part (semi partial) and partial correlation coefficients in multiple regression model. Deriving the multiple R-Squared and beta coefficients from basics. Inspired from Business Analytics: The Science of Data-Driven Decision Making by Dinesh Kumar.