R Programming: Data Analysis and Modeling

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R Programming: Data Analysis and Modeling

This course is part of R Programming for Data Science Specialization

Instructor: Bill Rosenthal

Included with

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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

In this course, you'll manage, analyze, and visualize data in R; and create statistical and machine learning models from that data.

Skills you'll gain

Tools you'll learn

Details to know

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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.

What's included

4 plugins

You'll wrap things up and then validate what you've learned in this course by taking an assessment.

What's included

1 reading1 assignment

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Instructor

Bill Rosenthal

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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.