Data Science in R: From Basics to Machine Learning

Data Science in R: From Basics to Machine Learning

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.

Beginner

53 Lessons

15h

Certificate of Completion

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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Explanations

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This course includes

3 Projects
74 Playgrounds
9 Quizzes

This course includes

3 Projects
74 Playgrounds
9 Quizzes

Course Overview

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, vi...Show More

TAKEAWAY SKILLS

Machine Learning

Data Science

Data Manipulation

Data Cleaning

Data Plotting

Git

Data Types

Strings

Functions

Data Visualization

Neural Networks

What You'll Learn

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

What You'll Learn

Familiarity with R fundamentals: syntax, key operations, and data types/structures

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Course Content

1.

Why R?

Get familiar with R's importance for data science, statistical analysis, and visualizations.
2.

R Fundamentals

Discover the logic behind R syntax, flow control, data frames, matrices, libraries, and data loading.
4.

Importing More Data Sources

Build a foundation in importing data from databases, web sources, and Excel into R.
5.

Data Visualization with ggplot2

Dig deeper into data visualization techniques and customization with ggplot2 in R.
6.

Best Practices for Data Scientists

3 Lessons

Tackle effective naming conventions, function creation, and systematic project structure for better data science practices in R.
7.

Statistical Analysis and Machine Learning with tidymodels

6 Lessons

Build on tidymodels for statistical analysis, machine learning, and model optimization.
8.

Useful Libraries for Data Science

5 Lessons

Learn how to use essential R libraries for date handling, string manipulation, mapping, and data checking.
9.

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.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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