causality

Learn Why Do we need causality in Data Science?

Learn Why Do we need Causality in Data Science? Tuesday, January 28 at 9 p.m. IST This is a Webinar explaining why we need causal inference(Causality) in data science and machine learning. Causal inference brings a new fresh set of tools and perspectives that let us deal with old problems. First off, designing and running experiments (typically with A/B testing) is always better than using causal inference techniques: you don’t need to model how data is generated. If you can do that, go for it! There are Three Main Sources of influence in Casual inference: Computer Science, Statistics, and epidemiology…

Getting Started with Linear Regression using a Case Study

Getting Started with Linear Regression using a Case Study

Getting Started with Linear Regression using a Case Study Tuesday, January 14 at 11 a.m. IST In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly,…