Data modeling has become a pervasive need in today’s business environment. Often the volume of data you need to process goes beyond the capabilities of spreadsheet modeling. When this is the case, the statistical programming language R offers a powerful alternative. With R, you can avoid the cost of standalone statistical packages. Likewise, you don’t need a huge investment in learning the structures required to use a more fully featured programming language.
In this course, you will work through the basic methods of predictive analytics, including generating descriptives, visualization, single and multiple regression, and logistic regression. The benefits of using R for logistic regression are significant, and these are explored in detail. When you have completed this course, you will have gained experience developing R code to solve novel problems in which basic predictive methods are required.
Faculty AuthorChris Anderson
Benefits to the Learner
- Bring data into working memory within an R integrated development environment (IDE)
- Create and manipulate basic data structures
- Generate statistical descriptives and visualizations of a dataset using R
- Use regression to quantify relationships between variables
- Quantify relationships between variables when the dependent variable is categorical
- Data scientists
- Functional managers
- Any professional that uses data to make business decisions
Applies Towards the Following Certificates
- Data Analytics in R : Required Courses