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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 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,…

Data Visualization with Python Saturday, January 11th at 9 p.m. IST “A picture is worth a thousand words”. We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract…