R Trainings in LinkedIn Learning

The “Getting Started with R for Data Science” learning path (15 h) and the "Advance your Skills in R" learning path (26 h) have all the commands that a beginner needs to know to continue by his or her own. These trainings courses provide clear-to-understand content and exercise files for practice.

After watching the Getting Started with R for Data Science learning path, users will have a very good understanding about how R and R Studio work, and you will be able to:

• Import data from several sources
• Explore variables: frequencies, descriptive, correlations, and contingency tables
• Create visualizations for one or more variables and for categorical and scale variables
• Wrangle data and recode different types of variables
• Make inference about the mean and the proportion for one or more variables
• Carry out ANOVA, correlation, and several regression analyses such as linear, multiple linear, lasso, quantile, log-linear, and logistic.
• If you are familiar with multivariate statistical analyses and machine learning, you could watch videos about cluster analysis, principal component analysis, decision trees, and random forest analysis.

Once you complete this learning path, you can improve your knowledge and skills with the “Advance your Skills in R” learning path. This path is designed for people who already have some basic understanding of R and want to take their skills to the next level. It walks through intermediate to advanced topics in R, especially focused on data science, visualization, modeling, and working heavily with the tidyverse package. Learners will apply concepts via real-projects, challenges, and practical tasks. Below are the major components of the learning path:

• Data wrangling, visualization, and modeling using R
• Real-world project work to build portfolio-ready experience.
• Deep dive into the tidyverse package (e.g. tools for importing, filtering, reshaping, modeling, and visualizing data).
• Advanced visualizations: building complex charts, mapping, interactive graphics.
• Code challenges to test knowledge and boost problem-solving of R programming.

The path has its own sequence, but we suggest following the sequence below because it flows logically from broad foundations to a practical project:

• Complete Guide to R: Wrangling, Visualizing, and Modeling Data
• R for Data Science: Analysis and Visualization
• Learning the R Tidyverse
• R Tidyverse Applications
• R Code Challenges: Data Science
• Build Advanced Charts in R
• Creating Maps with R
• Complete Your First Project in R

You are ready to go. Once you complete each learning path, you will receive a certificate of completion.

Follow the steps below to access LinkedIn Learning:

1. Go to eLearning; http://elearning.ufl.edu
2. Click on LinkedIn
3. You will be prompted to authenticate with your Gatorlink credentials.
4. If this is your first time using LinkedIn Learning, you must create your account.
5. Otherwise, use your password
6. The first page will be displayed and type the name of the learning path in the searching box.

R Programming Training Offered by UFIT

If you are interested in R programming specifically designed for basic statistical analyses at the graduate level, we offer two online and self-paced training courses: Data Preparation for Analysis and Data Analysis.” Click here for registration.

Time Commitment

Getting Started with R for Data Science = 15 h

Advanced your Skills in R = 26 h

Related Training

R Programming Training Courses: Data Preparation for Analysis and Data Analysis