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.

R for Data Scientists - From Basics to Machine Learning (1).png

tarball.tar.gz

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

Matrices in R are very similar to data frames, but they're less commonly used. In a data science context, they can be helpful in specific scenarios, but the vast majority of the time it's easier to use data frames.

Learn to use matrices in R, how they’re different from data frames, and how to do matrix arithmetic.

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

Matrices