How to create Parametric Survival model that gets right distribution?
Survival analysis refers to analyzing a set of data in a defined time duration before another event occurs. The number of years in which a human can get affected by diabetes/heart attack is a quintessential of survival analysis. Survival analysis is one of the most used algorithms, especially in Pharmaceutical industry.

Consider This: The Big Data Workout
The biggest disadvantage of Big Data is that there is so much of it, and one of the biggest problems with Big Data is that few people can agree on what it is. Overcoming the disadvantage of size is possible; overcoming the problem of understanding may take some time.

Guiding Principles to Build a Demand Forecast
Demand forecasting is key for many industries, including finance, healthcare, and retails, and it is one of the most challenging tasks for predictive analytics. We review challenges and guiding principles of demand forecasting.

Cohort Analysis with Heatmap
Previously I shared the data visualization approach for descriptive analysis of progress of cohorts with the ‘layer-cake’ chart (part I and part II). In this post, I want to share another interesting visualization that not only can be used for descriptive analysis as well but would be more helpful for analyzing a large number of cohorts. For instance, if you need to form and analyze weekly cohorts, you would have 52 cohorts within a year.

A causal-inference version of a statistics problem: If you fit a regression model with interactions, and the underlying process has an interaction, your coefficients won’t be directly interpretable
Linear regression, controlling for pre-treatment variables, is the standard method for causal inference in experiments and observational studies. The idea is that the regression on background variables serves to adjust for differences between the treatment and control group so that comparable groups are effectively being compared in the causal analysis.