Let's explore z-score tables, which are used to look up the cumulative probability in a standard normal distribution.
First, I'll show you how to use these tables to find the cumulative probability for any z-score from −4 to 4. Then we'll create them in Python using NumPy, SciPy, and pandas.
Z-score helps us understand the relative position of a specific observation within a distribution.
In this article, we'll dive deep into z-score and its practical applications, such as comparing data points across similar distributions, detecting outliers, etc.
In this article, I’ll explain linear equations step-by-step using simple examples.
You’ll gain an intuitive understanding of this fundamental data analysis technique. By the end, you'll be ready to dive into linear regression, the first ML algorithem most people encounter.
Let me show you how to simulate randomness using NumPy, the most widely used Python library for numerical computation.
You'll learn how to create a Random Number Generator (RNG), generate samples from various statistical distributions, create random subsets, shuffle arrays, and much more.
This tutorial provides an introduction to React's component-driven approach to building UIs. I'll use practical examples to teach you how to define and reuse components.
You'll also learn to specify component props, set the default prop values, and define prop types.
Let's dive deep into JSX, a potent mix of JavaScript and HTML that's used to build interactive React applications.
You'll learn how to do conditional rendering, apply CSS styles, render arrays, handle events, and much more.
This step-by-step tutorial will show you how to create a new GitHub repository and upload your local code to it.
You'll also learn how to set up the essential command line tools locally and how to use them.
New to React? Let me show you how to create a new app from scratch and run it locally.
You'll also get a glimpse into creating and using components to compose a web page, and how to use React Developer Tools to debug your app.
Let's explore different ways of selecting data from a Pandas DataFrame.
First, I'll introduce the two types of DataFrame indexes. Then, I'll show you how to use the methods iloc[] and loc[] to access the DataFrame data.
Let me show you how to use the methods idxmin() and idxmax() to get the index of the minimum or maximum value.
These methods provide additional functionality for DataFrames. We'll cover that as well.
