R Programming: Data Analysis and Modeling

R Programming: Data Analysis and Modeling

This course is part of R Programming for Data Science Specialization

Instructor: Bill Rosenthal

Access provided by American Open University Global

5 modules

Gain insight into a topic and learn the fundamentals.

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Load, save, and manipulate datasets using base R utilities, high-performance data.table syntax, and expressive dplyr grammar-driven data pipelines.
Conduct exploratory data analysis by evaluating statistical distributions, calculating central tendency metrics, and handling missing data.
Generate advanced visualizations with base graphics and `ggplot2` layers, refining layouts using custom themes, aesthetics, and multi-plot facets.
Data files for this course are provided in the first course of this specialization, "R Programming: Setup and Data Processing".

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the R Programming for Data Science Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

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.

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.

What's included

1 reading7 plugins

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.

What's included

5 plugins

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.

What's included

6 plugins

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.