R Programming: Data Analysis and Modeling

Overview

To round out your R programming skills, you'll dive into its data science capabilities by loading and saving data and manipulating data frames using base R and the dplyr package. You'll also analyze data by exploring its underlying distribution and identifying missing values. Then, you'll visualize data by using base R and ggplot2 to plot that data in various ways. Lastly, you'll create statistical and machine learning models in R that can make predictions and other estimations about data. This is the third and final course in a multi-course Specialization. All of the courses in this Specialization require that you have R and R Studio installed on a Windows PC. The course setup instructions provided in the first course go into more detail about the hardware and software requirements.

Syllabus

Managing Data in R

Up until now, you've mostly been applying the fundamentals of R as a general programming language. But, as you know, data science is where R really shines. In this lesson, you'll begin using R in a more data-driven context, particularly by managing data in various ways. This data-driven approach will continue throughout the rest of the course as you work toward building statistical and machine learning models.

Analyzing Data in R

Now that you've loaded and shaped your data, you can begin analyzing it in earnest. In this lesson, you'll use R to apply various techniques—both statistical and otherwise—that will reveal useful insights about your data.

Visualizing Data in R

Data analysis is not just about looking at raw numbers or text. Transforming your data into graphs and plots can greatly enhance your ability to interpret the data, as well as present that data to an audience. In this lesson, you'll use R to analyze your data from a visual perspective in order to reveal insights that raw numbers alone may not provide.

Modeling Data in R

In many data science projects, the ultimate goal is to create a model of the data. The model can be used to estimate some aspect of the data and the larger domain that the data is about. It can even be used to make predictions from the data, which is particularly attractive to businesses. In this lesson you'll get a crash course on modeling data, as well as how to implement those concepts in R.