- Introduction
- How to download and install R
- How to install a package and import a library
- How to import a data (Formats : csv, txt) and how to set a working directory
- Eliminate duplicate rows
- Missing values detection and treatment
- Data visualization (Detection of Strongly correlated variables)
- select a subset of the data based on specified criteria
- Operation on columns, Variables and standardization , how to use the apply() f
- Selecting the number of principal components
- Computation of the correlation matrix, eigenvalues and vectors
- Computation of components

### 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 *

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### About the instructors

**3.76**Calificación**8764**Estudiantes**4**Cursos

#### Modeste Atsague

##### Data Scientist, Statistician

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 !!!!!

### Student feedback

##### Course Rating

##### Reviews

Thanks for the content of this course. I am using multispectral remote sensing data and at the same time, I am developing a machine learning tool based on them. Therefore, it was better for me to reduce the dimension of my data prior to run my prediction model. Obviously, the PCA is the integrated part of dimension reduction that widely used for satellite data and this course clearly learned me to calculate my PCAs for further analysis. I do appreciate the instructor for building this course

Something more on PCA plots should be included in the course.

Yes the content was good.

I was frustrated by the lack of explanations of the core principles and I felt the lectures did little more than state what each code line does but I am grateful that the teacher has put his time into making this R course available.

Express and fast expression of the technique. Clears the basic concept to support further reading