Data Manipulation and PCA (Principal Component Analysis )

Data Manipulation and PCA (Principal Component Analysis ) , Data Manipulation and PCA.

What you”ll learn:

• By the end of this course , a student will be able to do the following:
• Stet a working directory , Import a txt or csv file, eliminate duplicate rows in the data, detect rows containing missing values, eliminate rows containing missing values, replace missing values by the mean, replace missing values by a specified information, use the apply function , do some arithmetic on columns , detect strongly correlated variable (some nice plots for visualization ), compute the correlation matrix , the eigenvalue and eigenvector vector, select the number of components the compute the components

Description 

In this course, we learn the following:

How to Stet a working directory

How to  Import a txt or  csv file

How to eliminate duplicate rows in the data

How to  detect rows containing missing values

How to eliminate rows containing missing values

How to  replace missing values

How to select a subset of the data based on specifics criteria 

How to do arithmetic on columns 

How detect strongly correlated variable (some nice plots for visualization )

How to compute the correlation matrix , the eigenvalue and eigenvector

How  select the number of components  

How to compute the components