HomeCoursesData Science in R: From Basics to Machine Learning

Updated 2 months ago

AI-powered learning

Save

Data Science in R: From Basics to Machine Learning

Name: Data Science in R: From Basics to Machine Learning
Price: 59 USD
Author: Chris Walsh

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

53 lessons

4 projects

15h

Created by Chris Walsh

Data Science in R: From Basics to Machine Learning

What's included

53 lessons across 11 modules
9 quizzes with instant feedback
4 projects in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

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

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.

About this course

WHY I WROTE 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

Trusted by 3.1 million developers working at companies

These are high-quality courses. Trust me the price is worth it for the content quality. Educative came at the right time in my career. I'm understanding topics better than with any book or online video tutorial I've done. Truly made for developers. Thanks

Anthony Walker

@_webarchitect_

Just finished my first full #ML course: Machine learning for Software Engineers from Educative, Inc. ... Highly recommend!

Evan Dunbar

ML Engineer

You guys are the gold standard of crash-courses... Narrow enough that it doesn't need years of study or a full blown book to get the gist, but broad enough that an afternoon of Googling doesn't cut it.

Software Developer

Carlos Matias La Borde

I spend my days and nights on Educative. It is indispensable. It is such a unique and reader-friendly site

Souvik Kundu

Front-end Developer

Your courses are simply awesome, the depth they go into and the breadth of coverage is so good that I don't have to refer to 10 different websites looking for interview topics and content.

Vinay Krishnaiah

Software Developer

Course

Data Science with R: Decision Trees and Random Forests

Gain insights into essential machine learning algorithms in R, including CART, random forest, and XGBoost. Discover model tuning and cross-validation to create accurate, robust data science models.

14 h

intermediate

Course

Data-Centric Statistical Inference Using R and Tidyverse

Gain insights into data visualization with ggplot2, data wrangling, and modeling in R. Delve into statistical inference using the infer package to enhance your data science skills.

17 h

beginner

Course

Data Visualizations with ggplot2 in R

Gain insights into using ggplot2 in R to create compelling visualizations. Learn about scatter plots, bar charts, customizing themes, and building interactive maps to enhance data storytelling.

10 h

beginner

Course

Data Science Handbook

Explore data science fundamentals, including generative AI, time series, and business analytics. Gain insights into practical applications using Python libraries like pandas, seaborn, and TensorFlow.

2 h 30 m

beginner

Course

Data Science for Non-Programmers

Discover how to conduct data analysis in Python without prior programming experience. Discover Python basics, data preprocessing, EDA, and predictive analytics for solving business problems.

13 h

beginner

Course

Data Science with R: Decision Trees and Random Forests

intermediate

14 hour

Course

Data-Centric Statistical Inference Using R and Tidyverse

beginner

17 hour

Course

Data Visualizations with ggplot2 in R