Statistics for Data Science
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Examples of K-Means Clustering Problems
K-Means clustering is one of the simplest and most widely used unsupervised machine learning algorithms. Its goal is to group data points into clusters based on similarity. In this article, we’ll explore different types of K-Means clustering problems, challenges related to choosing the right number…
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Making Sense of Unstructured Data: K-means Clustering
Data is everywhere. From social media posts to customer reviews, much of the data we generate and collect is unstructured. Unlike structured data (like spreadsheets), unstructured data doesn’t follow a predefined format, making it harder to analyze. That’s where machine learning techniques like K-means clustering…
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Making Sense of Unstructured Data: Distance and Scaling Measures
In the age of big data, unstructured data like text, images, audio, and videos make up the bulk of the information we generate. Unlike structured data (like spreadsheets), unstructured data doesn’t come in neat rows and columns. To analyze it effectively, especially for machine learning…
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Making Sense of Unstructure Data: Dimensionality Reduction (PCA & tSNE)
What is Dimensionality? In data science, dimensionality refers to the number of features (also called variables or attributes) in a dataset. For example, an image that’s 28×28 pixels has 784 dimensions (28 multiplied by 28), because each pixel represents a feature. High-dimensional data presents challenges:…
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Making Sense of Unstructured Data: Unsupervised Learning
In today’s data-rich environment, most of the information we encounter is unstructured. From social media posts and support tickets to satellite imagery and audio recordings, unstructured data surrounds us. Making sense of this kind of data is critical for businesses, scientists, and engineers alike. One…
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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…