Gain insights into R for data science, from basics to machine learning. Delve into data manipulation, visualization, and version control, and discover how to optimize R workflows for real-world challenges.

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Tidyverse

Embark on an exciting journey with R as your trusted ally for data science. This comprehensive course will equip you with essential skills to leverage R's power for data manipulation and analysis. The course is suitable even if you have limited R experience, empowering you to perform data science tasks effectively.

The journey starts with R fundamentals to lay a strong foundation. You’ll master the tidyverse to create powerful and readable code. Then, you will explore the import of various data sources, visualization, and best practices before gaining hands-on machine learning experience. Also, you’ll learn version control with Git and GitHub and to optimize your R code for efficient data science workflows.

At course completion, you'll emerge as a confident R data scientist, ready to tackle real-world challenges. You'll be well-equipped to advance your career in data science with R's extensive capabilities in your toolbelt.

Data Science in R: From Basics to Machine Learning

In this lesson, we walk through the steps of setting up Git with R and provide guidance for getting started with version control for R projects. It's possible and common to set up and manage GitHub with R projects with the user interfaces provided through RStudio and GitHub software. However, we'll leverage the `git2r` package to keep our coding mindset while performing project maintenance tasks.

Learn to create, push to, and clone Git repositories from R.

Why R?

R Fundamentals

R Fundamentals Exercises

Readable Coding with tidyverse

Tidyverse Exercises

Importing More Data Sources

Data Visualization with ggplot2

Best Practices for Data Scientists

Statistical Analysis and Machine Learning with tidymodels

Exploring tidymodels through Exercises

Useful Libraries for Data Science

Git Integration

Getting The Most Out of R

Appendix

Using Git with R