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### Applied Multivariate Analysis with Python

###### Step by Step Guide to Perform Multivariate Analysis with Python

In today's world, Data is everywhere and it is getting easier to produce it , collect it and perform multiple analysis.

This book is designed as a step by step guide on how to perform multivariate analysis with Python. It focuses on PCA (Principal Components Analysis) and LDA (Linear Discriminant Analysis).

The book's main idea is to focus on the step by step implementation. It is not necessary to have an advanced knowledge of Python but it is recommended to be familiar with the basics of programming, basics of Python, Statistics, Math and some Multivariate Methods.

The chapters in the book cover the following:

• Setting up the python environment.
• Reading and Plotting Multivariate Data.
• Calculating Summary Statistics for Multivariate Data
• Principal Component Analysis.
• Linear Discriminant Analysis.

Check out other books from the author:

Applied Multivariate Analysis with R

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DevOPs

Javascript Snippets

Appwrite Up and Running

Front End Developer Interview Questions

ReactJS Documentation

Backend Developer Interview Questions

VueJS Documentation

A.J. García

I started my coding career back in 2003. Lately I've been involved a lot in Javascript for frontend and the backend.

I would love passing along to you some of the experiences and challenges I've faced over the years.

#### Bundles that include this book

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• Other Books by Alejandro
• Recommended Resources
• Introduction
• Who is this book for ?
• What this book covers ?
• Chapter 1: Setting up the python environment
• Install Python
• Libraries
• Importing the libraries
• Python console
• Chapter 2: Reading and Plotting Multivariate Data
• Plotting Multivariate Data
• Chapter 3: Calculating Summary Statistics for Multivariate Data
• Means and Variances Per Group
• Between-groups Variance and Within-groups Variance for a Variable
• Between-groups Covariance and Within-groups Covariance for Two Variables
• Calculating Correlations for Multivariate Data
• Standardising Variables
• Chapter 4: Principal Component Analysis
• Deciding How Many Principal Components to Retain
• Scatterplots of the Principal Components
• Chapter 5: Linear Discriminant Analysis
• Separation Achieved by the Discriminant Functions
• A Stacked Histogram of the LDA Values
• Scatterplots of the Discriminant Functions
• Allocation Rules and Misclassification Rate
• Keep developing your Data Science skills

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