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Data Science in R: From Basics to Machine Learning
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Explore R for data science, from basics to machine learning. Learn data manipulation, visualization, version control, and workflow optimization for real-world challenges.

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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.

Embark on an exciting journey with R as your trusted ally for data science. This comprehensive course will equip you with essent...Show More

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

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

TAKEAWAY SKILLS

Machine Learning

Data Science

Data Manipulation

Data Cleaning

Data Plotting

Git

Data types

Strings

Functions

Data Visualization

Neural Networks

Content

53 Lessons3 Projects9 Quizzes

Why R?

4 Lessons

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

7 Lessons

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 Overview

Special Data Type—tibbles

Compounding Commands with the Pipe Operator

Joins and Lookups

Filtering Datasets

Stacking and Unstacking Data

Applying Complex Row-by-Row Operations

Important Tidyverse Functions for Data Science

Quiz: Tidyverse

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.

Why ggplot2?

Basic Plots in ggplot2

Customizing ggplot2 Plots

Faceting and Saving Plots

Quiz: The ggplot2 Package

Best Practices for Data Scientists

3 Lessons

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

Variable Naming Conventions

Creating Functions

Project Structure

Statistical Analysis and Machine Learning with tidymodels

6 Lessons

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

Getting Started with tidymodels

Linear Regression

Random Forest

Hyperparameter Tuning with Random Forest

Neural Networks

Quiz: The tidymodels Package

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.

Date and Time—Lubridate

String Manipulations—The stringr Package

Mapping—The ggmap Package

Checking Data—The skimr Package

Quiz: Useful Libraries

Git Integration

5 Lessons

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

Git and GitHub Overview

R Projects

Using Git with R

Next Steps with R and GitHub

Quiz: Git Integration

10.

Getting The Most Out of R

3 Lessons

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

Efficient Coding Practices

Analytical Best Practices

Conclusion

11.

Appendix

2 Lessons

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

RStudio Orientation

Getting Support in R

Credit Card Fraud Detection using the R Language

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Chris Walsh

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