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What is a Probability Distribution ? Determine its Type for Your Data


Probability Distribution is an important topic that each data scientist should know for the analysis of the data. It defines all the related possibility outcomes of a variable. In this, the article you will understand all the Probability Distribution types that help you to determine the distribution for the dataset. There are two types of distribution. In the discrete Distribution, the sum of the probabilities of all the individuals is equal to one.

A Gentle Introduction to Probability Distributions


Probability can be used for more than calculating the likelihood of one event; it can summarize the likelihood of all possible outcomes. A thing of interest in probability is called a random variable, and the relationship between each possible outcome for a random variable and their probabilities is called a probability distribution. Probability distributions are an important foundational concept in probability and the names and shapes of common probability distributions will be familiar. The structure and type of the probability distribution varies based on the properties of the random variable, such as continuous or discrete, and this, in turn, impacts how the distribution might be summarized or how to calculate the most likely outcome and its probability. In this post, you will discover a gentle introduction to probability distributions.

A Comprehensive guide to Parametric Survival Analysis


Survival analysis is one of the less understood and highly applied algorithm by business analysts. That is a dangerous combination! Not many analysts understand the science and application of survival analysis, but because of its natural use cases in multiple scenarios, it is difficult to avoid!

Probability Distributions in Data Science


Having a sound statistical background can be greatly beneficial in the daily life of a Data Scientist. Every time we start exploring a new dataset, we need to first do an Exploratory Data Analysis (EDA) in order to get a feeling of what are the main characteristics of certain features. If we are able to understand if it's present any pattern in the data distribution, we can then tailor-made our Machine Learning models to best fit our case study. In this way, we will be able to get a better result in less time (reducing the optimisation steps). In fact, some Machine Learning models are designed to work best under some distribution assumptions.