- 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
- 3.99 Calificación
- 11062 Estudiantes
- 2 Cursos
Modeste Atsague
Data Scientist, Statistician
Ph.D. Student in Computer Science, My education includes a BS in Mathematics and an MS in Mathematical Statistics. I invest a lot of time in 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
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For me i appreciated the way of teaching of professeur so he start from simple to explain much more the notion we need to at all step