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Data Science in R: From Basics to Machine Learning

Data Science in R: From Basics to Machine Learning

Learn everything you need to know to be a confident Data Scientist in R

Beginner

53 Lessons

15h

Certificate of Completion

Learn everything you need to know to be a confident Data Scientist in R

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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
What You'll Learn
Course Content

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?

Course OverviewWhat is R?
Why Data Scientists Need RQuiz: R Overview
2.

R Fundamentals

Basic R SyntaxFlow ControlData FramesMatricesGetting Organized—Libraries and Working DirectoriesLoading External DataQuiz: R Fundamentals

R Fundamentals Exercises

Project

3.

Readable Coding with tidyverse

Tidyverse OverviewSpecial Data Type—tibblesCompounding Commands with the Pipe OperatorJoins and LookupsFiltering DatasetsStacking and Unstacking DataApplying Complex Row-by-Row OperationsImportant Tidyverse Functions for Data ScienceQuiz: Tidyverse

Tidyverse Exercises

Project

4.

Importing More Data Sources

Database ConnectionsWeb DataMicrosoft ExcelQuiz: Data Import
5.

Data Visualization with ggplot2

Why ggplot2?Basic Plots in ggplot2Customizing ggplot2 PlotsFaceting and Saving PlotsQuiz: The ggplot2 Package
6.

Best Practices for Data Scientists

3 Lessons

7.

Statistical Analysis and Machine Learning with tidymodels

6 Lessons

Exploring tidymodels through Exercises

Project

8.

Useful Libraries for Data Science

5 Lessons

9.

Git Integration

5 Lessons

10.

Getting The Most Out of R

3 Lessons

11.

Appendix

2 Lessons

Course Author

Practice and apply your skills

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Anthony Walker

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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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