# Data Science with R

###### Course info

###### Course info

###### Description

Data science is the practice of transforming data into knowledge, and R is one of the most popular programming language used by data scientists. In a data-driven economy, this combination of skills is in extremely high demand, commanding significant increases in salary, as it is revolutionizing the world. In this course, Data Science with R, you'll learn first learn about the practice of data science, the R programming language, and how they can be used to transform data into actionable insight. Next, you'll learn how to transform and clean your data, create and interpret descriptive statistics, data visualizations, and statistical models. Finally, you'll learn how to handle Big Data, make predictions using machine learning algorithms, and deploy R to production. By the end of this course, you'll have the skills necessary to use R and the principles of data science to transform your data into actionable insight.

###### Section Introduction Transcripts

Course Overview

Hi, I'm Matthew Renze with Pluralsight, and welcome to Data Science with R. Data science is the practice of transforming data into knowledge, and R is the most popular open-source programming language used for data science. In our data-driven economy, this combination of skills is in extremely high demand, commanding significant increases in salary as it's revolutionizing the world around us. In this course, you'll learn how to use the principles of data science and the R programming language to answer day-to-day questions about your data. As an overview of this course, first, we'll learn about the practice of data science and the R programming language. Then, we'll learn how to work with data to create descriptive statistics, data visualizations, and statistical models. Finally, we'll learn how to handle big data, make predictions with machine learning, and deploy our applications into production. By the end of this course, you'll have the skills necessary to use R and the principles of data science to transform your data into actionable insight. In addition, by the end of this course you'll have built and deployed a web-based interactive machine learning application that allows users to make predictions using data. As an introductory course, there are no prerequisites for this course. However, having basic experience with at least one programming language and basic knowledge of statistics will be beneficial. So please join us today at Pluralsight and learn how to transform your data into actionable insight using data science with R.

Introduction to R

Hello, and welcome back to Data Science with R. I'm Matthew Renze with Pluralsight, and in this module, we'll learn about the programming language R. As an overview of this module, first, we'll learn about the R programming language, what it is, and why it has become so popular. We'll also learn about the RStudio integrated development environment, or IDE. Then, we'll see a demo of the basic syntax of the R language, we'll learn about data types, data structures, and how to slice and dice data in R. So let's get started.

Working with Data

Hello again, and welcome to our next module on Data Science with R. I'm Matthew Renze with Pluralsight, and in this module we'll learn how to work with data using R. As an overview of this module, first, we'll learn how to work with data using R. That is, we'll learn how to import, clean, transform, and export data. We'll also learn how R makes this process easier and more efficient than by using other tools. Then, we'll see a demonstration where we'll import, transform, and export data. The data transformation steps will be done using a very popular extension package in R called dplyr. So let's get started.

Creating Descriptive Statistics

Hi there, and welcome back to Data Science with R. I'm Matthew Renze with Pluralsight, and in this module we'll learn how to create descriptive statistics using R. As an overview of this module, first, we'll learn about descriptive statistics and how we can use R to describe our data in numerical ways. We'll cover the standard statistical measures for both a single categorical variable and a single numeric variable. Then, we'll see a demo where we'll learn how to create these descriptive statistics using R. We'll create frequency tables, measures of central tendency, measures of spread, and we'll create correlation coefficients as well. So let's get started.

Creating Data Visualizations

Hello again, and welcome back to Data Science with R. I'm Matthew Renze with Pluralsight, and in this module we'll learn how to create data visualizations using R. As an overview of this module, first, we'll learn about data visualization and the three main plotting systems in R. Then we'll look at a series of standard data visualizations and explore some more advanced data visualizations as well. Then, we'll see a demo where we'll learn how to create a few of these data visualizations using R. So let's get started.

Creating Statistical Models

Hello again, and welcome to the next module in our introduction to Data Science with R. I'm Matthew Renze with Pluralsight, and in this module we'll learn how to create statistical models using R. As an overview of this module, first, we'll learn about statistical modeling and how we can use statistical models to create inferences and make predictions. In addition, we'll learn about simple linear regression models. Then, we'll see a demo where we'll learn how to create a simple linear regression model using R.

Handling Big Data

Hi, I'm Matthew Renze with Pluralsight, and welcome back to Data Science with R. In this module, we'll learn how to handle big data using R. As an overview of this module, first, we'll learn about big data, what it is, and why it's important that we learn how to handle it in R. Then, we'll see a demo where we'll learn how to use a big data extension package called FF, which is used to handle data sets that are too large to fit into memory. We'll also use another big data extension package called Big LM, which is used to create linear regression models for large data sets. This will prepare us to use R to train machine-learning models using very large data sets. So let's get started.

Predicting with Machine Learning

Hello again, and welcome to our next module on Data Science with R. I'm Matthew Renze with Pluralsight, and in this module we'll learn how to make predictions using machine learning with R. As an overview of this module, first, we'll learn about machine learning and how it can be used to make predictions about our data. We'll cover the various types of machine-learning algorithms and how to train and evaluate our machine-learning models. Then, we'll see a demo where we'll apply what we've learned by creating a decision-tree classifier and make predictions using this classifier. So let's get started.

Deploying to Production

Hello again, and welcome to the final module in our introduction to Data Science with R. I'm Matthew Renze with Pluralsight, and in this module we'll learn how to deploy R into production. As an overview of this module, first, we'll learn how to deploy R into production. We'll learn about the various ways we can share data, code, and analyses with others. In addition, we'll learn about a web-based, interactive application framework for R called Shiny. Then, we'll see a demo where we'll learn how to convert our machine-learning model from our last demo into a web-based application in R using Shiny. Finally, we'll wrap things up for the course as a whole. In addition, I'll show you where to go to learn more about all the topics we discussed in this course.