Data Manipulation and PCA (Principal Component Analysis )

Data Manipulation and PCA
Rating: 4.0 out of 5 (78 ratings)
4,955 students
English
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

Requirements

  • No Prior programing knowledge is required.

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  

Who this course is for:

  • If you are working on a large data set or trying to get some informations about your data , then this course is a right fit for you.

Course content

1 section • 12 lectures • 1h 9m total length
  • Introduction
    00:46
  • How to download and install R
    07:54
  • How to install a package and import a library
    03:44
  • How to import a data (Formats : csv, txt) and how to set a working directory
    10:47
  • Eliminate duplicate rows
    02:44
  • Missing values detection and treatment
    09:20
  • Data visualization (Detection of Strongly correlated variables)
    06:15
  • select a subset of the data based on specified criteria
    07:06
  • Operation on columns, Variables and standardization , how to use the apply() f
    11:16
  • Selecting the number of principal components
    01:45
  • Computation of the correlation matrix, eigenvalues and vectors
    03:03
  • Computation of components
    05:06

Instructor

Data Scientist, Statistician
Modeste Atsague
  • 3.6 Instructor Rating
  • 128 Reviews
  • 9,139 Students
  • 4 Courses

Phd Student in Computer Science, My education Include a BS in Mathematics and a MS in Mathematical Statistics. I invest a lot of time on learning and teaching. Covering a wide range of topics in Mathematics, Statistics and Computer Science , Some of my main interests include machine learning, data reduction techniques, Statistical Computing, regression analysis and a wide range of mathematical Statistics topics including parameter estimate.

Join my courses and learn !!!!!