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Data Visualization
  • Level Foundation
  • المدة 16 ساعات hours
  • الطبع بواسطة Ball State University
  • Offered by

عن

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

الوحدات

Start Here! -- Introductions

    • 2 Videos
    • 2 Readings
    • 1 Discussion
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1 Discussions

  • Introduce Yourself

2 Videos

  • Welcome to Data Visualization
  • Meet Your Instructor

2 Readings

  • Meet Your Course Staff
  • Read the Course Syllabus

Introduction to Data Visualization

    • 4 Videos
    • 1 Assignment
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1 Assignment

  • Activities and Skills in Data Visualization

4 Videos

  • Module 1 Overview
  • Introduction to Data Visualization
  • Data Visualization techniques
  • Some examples of data visualization

Introduction to R

    • 2 Videos
    • 3 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Basic R information

1 Labs

  • Getting Started with RStudio/R

2 Videos

  • Introduction to R
  • RStudio Panes

3 Readings

  • Install R and RStudio
  • RStudio Lab (In-Browser Option)
  • Materials for Understanding Basic R

Basic R

    • 7 Videos
    • 1 Assignment
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1 Assignment

  • Create Objects in R

7 Videos

  • 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

    • 5 Videos
    • 3 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • R Markdown Basic Information.

1 Labs

  • Create your first R Markdown file by default and name it "My first R Markdown file"

5 Videos

  • 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

3 Readings

  • RMarkdown Cheat Sheet
  • Install the "knitr" and "rmarkdown" Packages
  • Module 1 Summary

Design Data Visualization

    • 4 Videos
    • 1 Assignment
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1 Assignment

  • Components of Data Visualization

4 Videos

  • Module 2 Overview
  • Introduction to data visualization
  • Introduction to the Grammar of Graphics
  • Marks and Channels

Color Systems

    • 1 Videos
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Color Systems

1 Discussions

  • Why is Rainbow Color Not Suggested in Data Visualization?

1 Videos

  • Color models

Best Practices in Data Visualization

    • 3 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Best Practices in Data Visualization

3 Videos

  • Exploratory Data Analysis (EDA)
  • Some examples
  • Data Visualization Principles

2 Readings

  • Principles of Effective Data Visualization
  • Module 2 Summary

Introduction to ggplot2

    • 4 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • ggplot() Usage

4 Videos

  • Module 3 Overview
  • Introduction to ggplot2
  • Basic usage of ggplot() function
  • Colors in ggplot()

2 Readings

  • Installation of ggplot2
  • ggplot2 Cheatsheet

Basic Histogram

    • 3 Videos
    • 1 Readings
    • 1 Lab
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1 Labs

  • Single histogram

3 Videos

  • Introduction to Histogram
  • Bins in Histogram
  • Plot a Single Histogram

1 Readings

  • Change Histogram Outline and Fill Colors

Grouped Histogram

    • 1 Videos
    • 2 Readings
    • 1 PeerReview
    • 1 Programming
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1 Programming

  • Step 1: Grouped Histogram in R

1 Peer Review

  • Step 2: Grouped histogram in R

1 Videos

  • Grouped Histogram

2 Readings

  • Legends in ggplot2
  • Module 3 Summary

Embed Images in R Markdown Files

    • 5 Videos
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5 Videos

  • 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

Embed Tables in R Markdown Files

    • 2 Videos
    • 1 Readings
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2 Videos

  • Create tables in R Markdown
  • Index tables in R Markdown files

1 Readings

  • Embed Images and Tables in R Markdown

A Scatter Plot with ggplot2

    • 3 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
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1 Discussions

  • Does a Scatter Plot Prove the Causation?

1 Labs

  • A HTML report in R Markdown file with images and tables, and refer and index the tables

3 Videos

  • About scatter plots and bubble plots
  • A scatter plot with ggplot2
  • A scatter plot with ggplot2

1 Readings

  • Module 4 Summary

Boxplot

    • 2 Videos
    • 1 Readings
    • 1 Discussion
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1 Discussions

  • Why Boxplot Could be Used to Detect Outliers

2 Videos

  • Module 5 Overview
  • Introduction to Boxplots

1 Readings

  • Interpretation of Boxplots

Group Boxplots

    • 2 Videos
    • 1 Lab
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1 Labs

  • Plot multiple group boxplots with data provided

2 Videos

  • Basic Box plot in R
  • Boxplot in R_Change outline colors and fill colors

Multiple-view Layout

    • 3 Videos
    • 2 Readings
    • 1 Programming
    • 1 Assignment
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1 Programming

  • Step 1: Multiple-view plots including histogram, boxplots and scatter plot with data provided

1 Assignment

  • Step 2: Self-Check Mutiple-view plots including histogram, boxplots and scatter plot with data provided

3 Videos

  • Arranging multiple plots on a page
  • Use facets in ggplot2
  • grid.arrange() function

2 Readings

  • Laying out multiple plots on a page
  • Module 5 Summary

Auto Summary

"Data Visualization" is an essential course within the Data Science & AI domain, offered by Coursera and instructed by expert faculty. This foundational course spans 960 minutes and delves into creating and critically evaluating digital maps, charts, and graphs using R. It is structured into four sections, beginning with basic R skills and advancing to interactive data visualization and a final project. Ideal for beginners and professionals, it offers Starter, Professional, and Paid subscription options. This course is perfect for those aiming to harness the power of big data through compelling visual representations.

Dr. Aihua Li
Instructor

Dr. Aihua Li