Course details

R for Data Science: Regression Methods

R for Data Science: Regression Methods


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

The programming language has become an essential skill for statistical computing and graphics, the tool of choice for data science professionals in every industry and field. R creates reproducible high-quality analyses, and allows users to take advantage of its great graphic and charting capabilities. In this 8-video Skillsoft Aspire course, you will discover how to apply regression methods to data science problems by using R. Key concepts covered in this course include preparing a data set before creating a linear regression model how to create a linear regression model with the lm method in R; and extracting statistical results of a linear regression problem. You will also learn how to test the predict method on perform the preparatory steps needed to create a logistic model; and how to apply the generalized linear model (glm) method on a logistic regression problem. Finally, learners see how to create a linear regression model and use the predict method on a linear model.



Expected Duration (hours)
0.6

Lesson Objectives

R for Data Science: Regression Methods

  • Course Overview
  • perform the preparatory steps needed to create a linear model
  • create a linear regression model using the lm method in R
  • extract the results of a linear regression
  • test the predict method on a linear model
  • perform the preparatory steps needed to create a logistic model
  • apply the glm method on a logistic regression problem
  • create a linear regression model and use the predict method
  • Course Number:
    it_dsrfdsdj_04_enus

    Expertise Level
    Intermediate