Data Science
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Making Sense of Unstructured Data: Kullback-Leibler (KL) Divergence
In today’s data-driven world, most of the information we encounter is unstructured. From social media posts and emails to images and audio recordings, unstructured data surrounds us. Understanding and making sense of this data is crucial for businesses and researchers alike. One fundamental concept that…
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Making Sense of Unstructured Data – Covariance
In the world of data science, much of the information we encounter is messy, unpredictable, and without a clear structure. This is known as unstructured data, and it includes things like text, images, videos, and audio. Making sense of unstructured data is crucial for extracting…
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Making Sense Of Unstructure Data – Understanding Unsupervised Learning
In the world of data science and machine learning, algorithms are usually grouped into three main categories: In this post, we will focus on unsupervised learning. What is Unsupervised Learning? Unsupervised learning is a type of machine learning where computers learn from data that has…
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Statistics for Data Science – Normal Distribution Example
Understanding statistical concepts is crucial for data science, as they form the foundation for data analysis and decision-making. One of the most important and widely used concepts in statistics is the normal distribution. In this post, we will explore the normal distribution and work through…
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Statistics for Data Science – Normal Distribution Properties
The normal distribution, also known as the Gaussian distribution, is one of the most important concepts in statistics, particularly in the context of data science. It is widely used because many real-world phenomena are approximately normally distributed. Understanding the properties of the normal distribution is…
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Statistics for Data Science – Normal Distribution Intro
In the world of data science, understanding probability distributions is key to making informed decisions based on data. One of the most important probability distributions to grasp is the Normal Distribution, often referred to as the Gaussian distribution. It is the foundation of many statistical…
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Statistics for Data Science – Uniform Distribution Example
In statistics, the uniform distribution is one of the most fundamental probability distributions. It is used to model situations where all outcomes are equally likely. Understanding the uniform distribution is essential for data science, as it provides a simple yet powerful tool for modeling randomness…
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Statistics for Data Science – Uniform Distribution Concept
Understanding probability distributions is a key part of data science, as they form the foundation for statistical analysis. One such distribution is the Uniform Distribution. This distribution is simple yet powerful, and is often used in scenarios where every outcome is equally likely. In this…
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Statistics for Data Science – Binomial Distribution Assumptions
The Binomial Distribution is a commonly used probability distribution in statistics. It is essential to understand the assumptions behind the Binomial Distribution when applying it in Data Science. These assumptions help us determine when this distribution is appropriate for a given dataset. In this post,…
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Statistics for Data Science – Binomial Distribution Formula
In the field of data science, statistics plays a critical role in making sense of data and drawing meaningful conclusions. One important statistical concept is the Binomial Distribution, which models the number of successes in a fixed number of independent trials of a binary experiment.…