Data Visualization
- Offered byCoursera
Data Visualization at Coursera Overview
Duration | 16 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Visualization at Coursera Highlights
- Earn a certificate from Ball State University
- Add to your LinkedIn profile
- 9 quizzes
Data Visualization at Coursera Course details
- In the era of big data, acquiring the ability to analyze and visually represent Big Data in a compelling manner is crucial
- Therefore, it is essential for data scientists to develop the skills in producing and critically interpreting digital maps, charts, and graphs
- Data visualization is an increasingly important topic in our globalized and digital society
- It involves graphically representing data or information, enabling decision-makers across various industries to comprehend complex concepts and processes that may otherwise be challenging to grasp
- DSCI 605 Data visualization serves as the foundation for understanding principles, concepts, techniques, and tools used to visualize information in large, intricate data sets
- It also provides hands-on experience in visualizing big data using the open-source software R
- Through the course, students will learn to evaluate the effectiveness of visualization designs and think critically about decisions, such as color choice and visual encoding
- Additionally, students will create their own data visualizations and become proficient in using R
- The course comprises four sections
- The first section caters to learners with minimal or no experience in R, establishing the groundwork for data visualization with R
- The second section introduces preliminary data visualization techniques, allowing students to gain hands-on experience with common visualization practices for Exploratory Data Analysis (EDA) using ggplot2
- This section emphasizes data exploration before delving into advanced data mining
- The third section builds upon existing data visualization skills by delving into advanced data visualization topics, including interactive data visualization, time series plotting, and spatial mapping
Data Visualization at Coursera Curriculum
Introduction to Data Visualization and Getting Started with R
Welcome to Data Visualization
Meet Your Instructor
Module 1 Overview
Introduction to Data Visualization
Data Visualization techniques
Some examples of data visualization
Introduction to R
RStudio Panes
Data types in R
Data structures in R
Introduction to objects in R
Create objects/variables in R
Create objects with different data structures in R
Remove and save objects
A Simple Tutorial to Get You Started with R
Introduction to R Markdown file
The structure of an R Markdown File
Create Your .Rmd File and Use Knitr to Convert .Rmd to .html or PDF
Format the text in R Markdown
Code Chunks-Hide code and information
Meet Your Course Staff
Read the Course Syllabus
Install R and RStudio
RStudio Lab (In-Browser Option)
Materials for Understanding Basic R
RMarkdown Cheat Sheet
Install "knitr" and "rmarkdown" Packages
Module 1 Summary
Activities and Skills in Data Visualization
Basic R information
Create Objects in R
R Markdown Basic Information
Introduce Yourself
Getting Started with RStudio/R
Create your first R Markdown file by default and name it "My first R Markdown file"
Graphics Components for Data Visualization
Module 2 Overview
Introduction to data visualization
Introduction to the Grammar of Graphics
Marks and Channels
Color models
Exploratory Data Analysis (EDA)
Some examples
Data Visualization Principles
Principles of Effective Data Visualization
Module 2 Summary
Components of Data Visualization
Color Systems
Best Practices in Data Visualization
Why is Rainbow Color Not Suggested in Data Visualization?
ggplot2
Module 3 Overview
Introduction to ggplot2
Basic usage of ggplot() function
Colors in ggplot()
Introduction to Histogram
Bins in Histogram
Plot a single histogram
Grouped Histogram
Installation of ggplot2
ggplot2 Cheatsheet
Change Histogram Outline and Fill Colors
Legends in ggplot2
Module 3 Summary
ggplot() Usage
Step 1: Grouped Histogram in R
Step 2: Grouped histogram in R
Single histogram
Embed Images and Tables in R Markdown Files
Module 4 Overview
Save graphs as png and jpeg
Output graphs into a pdf file
Embed images in R Markdown files
Refer to images in R Markdown files
Create tables in R Markdown
Index tables in R Markdown files
About scatter plots and bubble plots
A scatter plot with ggplot2
A scatter plot with ggplot2
Embed Images and Tables in R Markdown
Module 4 Summary
Does a Scatter Plot Prove the Causation?
A HTML report in R Markdown file with images and tables, and refer and index the tables
Boxplot and Multiple-view Layout
Module 5 Overview
Introduction to Boxplots
Basic Box plot in R
Boxplot in R_Change outline colors and fill colors
Arranging multiple plots on a page
Use facets in ggplot2
grid.arrange() function
Interpretation of Boxplots
Laying Out Multiple Plots on a Page
Module 5 Summary
Step 2: Self-Check Mutiple-view plots including histogram, boxplots and scatter plot with data provided
Step 1: Multiple-view plots including histogram, boxplots and scatter plot with data provided
Why Boxplot Could be Used to Detect Outliers
Plot multiple group boxplots with data provided
Data Visualization at Coursera Admission Process
Important Dates
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