Applied Multivariate Analysis with Python & R
Applied Multivariate Analysis with Python & R
About the Bundle
In today's world, Data is everywhere and it is getting easier to produce it , collect it and perform multiple analysis.
This bundle is designed as a step by step guide on how to perform multivariate analysis with Python and R. It focuses on PCA (Principal Components Analysis) and LDA (Linear Discriminant Analysis).
The bundle's main idea is to focus on the step by step implementation. It is not necessary to have an advanced knowledge of Python or R but it is recommended to be familiar with the basics of programming, basics of Python and R, Statistics, Math and some Multivariate Methods.
The two books included in this fantastic bundle are:
Check out other books from the author:
Data Science Workflow for Beginners
Front End Developer Interview Questions
About the Books
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
Data Science Workflow for Beginners
Front End Developer Interview Questions
Applied Multivariate Analysis with R
Step by Step Guide to Perform Multivariate Analysis with R
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 R. 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 R but it is recommended to be familiar with the basics of programming, basics of R, Statistics, Math and some Multivariate Methods.
The chapters in the book cover the following:
- Setting Up the R environment.
- A brief introduction to R.
- Using R for Multivariate Analysis.
- Plotting Multivariate Data.
- Calculating Summary Statistics for Multivariate Data.
- Calculating Correlations for Multivariate Data.
- Standardising Variables.
- Principal Component Analysis.
- Linear Discriminant Analysis.
Check out other books from the author:
Applied Multivariate Analysis with Python
Data Science Workflow for Beginners
Front End Developer Interview Questions
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