Learn the foundations of data science by exploring, transforming, and visualizing data with R. In this course, you’ll develop core skills in exploratory data analysis and statistical thinking including: using visualizations to uncover patterns, identifying trends, and generating insights.
Data Visualization and Transformation with R
Data Visualization and Transformation with R
This course is part of Data Science with R Specialization
Instructors: Mine Çetinkaya-Rundel
Access provided by Toyota North America
1,610 already enrolled
Gain insight into a topic and learn the fundamentals.
4.8
(16 reviews)
Beginner level
No prior experience required
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Transform, visualize, summarize, and analyze data in R, with packages from the Tidyverse, using RStudio
Carry out analyses in a reproducible and shareable manner with Quarto
Learn to effectively communicate results through an optional written project version controlled with Git and hosted on GitHub
Skills you'll gain
- Data Wrangling
- Data Science
- Statistics
- Exploratory Data Analysis
- Ggplot2
- Data Analysis
- Statistical Programming
- Data Transformation
- R Programming
- Descriptive Statistics
- Data Manipulation
- GitHub
- Statistical Visualization
- Data Visualization
- Probability & Statistics
- Statistical Analysis
- Data-Driven Decision-Making
- R (Software)
- Data Visualization Software
- Tidyverse (R Package)
Details to know
Shareable certificate
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Assessments
3 assignments
Taught in English
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This course is part of the Data Science with R 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 3 modules in this course
Hello World! In the first module, you will learn about what data science is and how data science techniques are used to make meaning from data and inform data-driven decisions. There is also discussion around the importance of reproducibility in science and the techniques used to achieve this. Next, you will learn the technology languages of R, RStudio, Quarto, and GitHub, as well as their role in data science and reproducibility.
What's included
4 videos11 readings1 assignment2 discussion prompts1 plugin
In our second module, we'll advance our understanding of R to set the stage for creating data visualizations using tidyverse’s data visualization package: ggplot2. We'll learn all about different data types and the appropriate data visualization techniques that can be used to plot these data. The majority of this module is to help best understand ggplot2 syntax and how it relates to the Grammar of Graphics. By the end of this module, you will have started building up the foundation of your statistical tool-kit needed to create basic data visualizations in R.
What's included
4 videos5 readings1 assignment1 discussion prompt1 plugin
In this module, we will take a step back and learn about tools for transforming data that might not yet be ready for visualization as well as for summarizing data with tidyverse’s data wrangling package: dplyr. In addition to describing distributions of single variables, you will also learn to explore relationships between two or more variables. Finally, you will continue to hone your data visualization skills with plots for various data types.
What's included
8 videos15 readings1 assignment2 discussion prompts1 plugin
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