Course Overview

This course is an opportunity to better equip your data science toolkit by expanding it into R. We’ll cover everything from the basics of R to building complex machine learning models. We’ll do so in the context of data science, discussing applicable best practices and challenges specific to data science along the way. We’ll cover everything you need to be capable and confident in leveraging R to do data science!

All techniques will include interactive examples. Throughout the course, you’ll build machine learning models directly in your browser!

The course starts with the fundamental parts of R, creating a foundation on which most data science analyses can be conducted independently. Afterward, we’ll layer in more complex machine learning tools using the tidymodels package.

In particular, we’ll cover:

• Building R script from scratch, including project components, data types, syntax, and other R programming fundamentals.

• Creating readable code in a data science context using tidyverse.

• Data management and manipulation techniques.

• Data visualization using ggplot2.

• Best practices in R in a data science context.

• Building analytical models in R using tidymodels, including basic analytical models and machine learning techniques.

• Significant packages for data scientists.

• Project management in R using Git integrations.

This course is for anyone interested in learning R for the purpose of carrying out data science tasks. It’s for those with little or no programming background, as well as those who already know another programming language. Foundational programming constructs will be covered within the context of R.

However, this course will assume prior knowledge about statistics and the theories behind data science. We won’t delve deep into standard logic operations (Boolean statements), statistical theory and properties (e.g., different types of distributions), or machine learning theory (e.g., the mechanics behind a neural network model). However, the application of these topics in R will be covered in detail.

By the end of the course, you’ll walk away feeling confident in your R skills and how we can apply them to data science. You’ll be able to create clean, readable code that follows industry best practices and ranges in complexity from the most basic regression to advanced machine learning techniques. If we don’t cover a specific tool here, you’ll still gain the necessary building blocks to learn it—knowing where to research it and the fundamentals on which it’s built.

We’re excited to help you take this next step in your data science career!