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Data Science in R: From Basics to Machine Learning

Explore R for data science, from basics to machine learning. Learn data manipulation, visualization, version control, and workflow optimization for real-world challenges.

4.9

53 Lessons

4 Projects

15h

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LEARNING OBJECTIVES

Familiarity with R fundamentals: syntax, key operations, and data types/structures
The ability to create engaging data visualizations in R using various techniques
A deep knowledge of the tidyverse (dplyr, tidymodels, ggplot2) for data analysis
Hands-on experience developing machine learning models in R
Proficiency with data wrangling in R, including filtering, joining, and reshaping
Familiarity with Git and GitHub integration for version control in R

Learning Roadmap

53 Lessons3 Projects9 Quizzes

Why R?

Get familiar with R's importance for data science, statistical analysis, and visualizations.

Course Overview

What is R?

Why Data Scientists Need R

Quiz: R Overview

R Fundamentals

Discover the logic behind R syntax, flow control, data frames, matrices, libraries, and data loading.

Getting Organized—Libraries and Working Directories

Loading External Data

Quiz: R Fundamentals

R Fundamentals Exercises

Project

Readable Coding with tidyverse

9 Lessons

Go hands-on with tidyverse for enhanced data manipulation, visualization, and code readability in R.

Tidyverse Exercises

Project

Importing More Data Sources

4 Lessons

Build a foundation in importing data from databases, web sources, and Excel into R.

Data Visualization with ggplot2

5 Lessons

Dig deeper into data visualization techniques and customization with ggplot2 in R.

Best Practices for Data Scientists

3 Lessons

Tackle effective naming conventions, function creation, and systematic project structure for better data science practices in R.

Statistical Analysis and Machine Learning with tidymodels

6 Lessons

Build on tidymodels for statistical analysis, machine learning, and model optimization.

Exploring tidymodels through Exercises

Project

Useful Libraries for Data Science

5 Lessons

Learn how to use essential R libraries for date handling, string manipulation, mapping, and data checking.

Git Integration

5 Lessons

Get started with integrating Git and GitHub for effective R project management.

10.

Getting The Most Out of R

3 Lessons

Work your way through efficient coding, analytical practices, and advanced R applications.

11.

Appendix

2 Lessons

Grasp the fundamentals of using RStudio and finding support in the R community.

Credit Card Fraud Detection using the R Language

Project

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

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.

ABOUT THE AUTHOR

Chris Walsh

Data Science Leader, Runner, Maple Syrup Artisan

Learn more about Chris

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