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.
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
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
- Data Visualization
- Data Import/Export
- Plot (Graphics)
- R Programming
- Machine Learning
- Statistical Machine Learning
- Software Development
- Machine Learning Algorithms
- Data Structures
- Statistical Analysis
- Computer Programming
- Regression Analysis
- Statistical Modeling
- R (Software)
- Data Analysis
- Decision Tree Learning
- Machine Learning Methods
- Statistical Visualization
- Data Science
- Computer Programming Tools
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
January 2026
Assessments
1 assignment
Taught in English
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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.
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There are 5 modules in this course
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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