SciPy and Statistical Analysis: Top Techniques for Statistics in Python

Learn how to use SciPy for strong statistical analysis. Master key techniques to boost your data skills and make smart decisions in 2024.

Last time, I covered the basics of statistical analysis and why it's crucial for making decisions based on data. Now, we’re going to take things a step further by showing you how to use Python and the SciPy library to bring those concepts to life with practical examples.

My aim is to move from theory to practice, using real-world situations to help you apply what you've learned. This guide will walk you through using Python's powerful tools for statistical analysis with SciPy and NumPy.

In this article I will show you the code behind the scenes for Hypothesis testing writing code for tests such as t-tests, chi-square tests and ANOVA. From here we will further expand into Confidence Intervals and different types of Correlation.

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Last week I wrote about Statistical Analysis 101, or at least that is what I called the article. I promised you guys I would get into the code this week and that is exactly what this article is.

When I first started writing last weeks article I was so overwhelmed with how much information I was finding, that it only felt right to split it into two well deserved articles. This article will build heavily on last week so be sure that you read up on that first.

This is all apart of my current series on Data Analytics where I started with an introduction into Data Analytics and have worked through stages of Exploratory Data Analytics into Data Visualization. This series is written for you guys my readers!

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Grab some coffee or tea, and get ready to get coding!

Getting Started with SciPy for Statistical Analysis