This blog aims to explain everything about the types of Business Analytics with their core & real-life examples. In addition to that, how much statistics knowledge is required for each analytics to perform is also been listed in this blog.

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This blog aims to explain the difference between one of the most encountered distributions in the Data Science World, i.e., Binomial Distribution & Bernoulli Distributions with real-life examples.

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This blog aims to explain the problem associated with the Dummy Variables, i.e., Dummy Variable Trap. Everything related to the Dummy Variable Trap will be covered starting from the source/origin of this problem to the solution of the problem.

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Source: Aditya Chinchure via Unsplash

While doing Data Preprocessing, every time “Feature Engineering” has to be done. “Dummy Variable Trap” is the problem that occurs in Feature Engineering.

Origin/Source of Dummy Variable Trap!


This blog aims to explain the MultiCollinearity concept which is very much important in Data Preprocessing, which is, in turn, a part of Data Science or Machine Learning/Deep Learning.

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This blog aims to explain the process of creating the multi-node cluster setup of Hadoop using Ansible which is very rarely available. Hadoop version 1.2.1 is used in this blog, you can choose your own version based on your choice.

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This blog aims to explain the process of creating an architecture involving HAProxy & Apache Webserver for Load Balancer & Webserver usage respectively using Ansible!

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This blog aims to explain the process of launching a webserver using containerization technology(Docker) and the DevOps tool(Ansible) for automation!

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This blog aims to explain the difference between the Probability & the Likelihood. This topic is very important to understand, but the problem here is that both the topics are very confusing to understand. That is why, I am writing this blog to remove the confusion, & I will explain the topics in a simple manner as possible.

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Important note!


This blog aims to explain an effective way to calculate the correlation between the features of a dataset which in turn will help to not only select specific features to improve the model training(remove the curse of dimensionality), but it will also help in improving the model performance.

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For Feature Selection, there are various techniques, among those techniques, finding correlation is very famous & widely adopted. Finding a correlation between the features of the dataset is a very interesting and important aspect.


This blog aims to explain the Covariance which is a very important topic in Feature Engineering in Data Science. In addition to that, this blog will also cover its use-cases, advantages & disadvantages.

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Harshit Dawar

Big Data Enthusiast, have a demonstrated history of delivering large and complex projects. Interested in working in the field of AI and Data Science.

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