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.
The primary objective of the first three sections is to equip students with a well-developed set of skills, enabling them to create a wide range of visualizations in R. The final section focuses on completing a final project, where students apply the skills, theory, and experiences gained from the previous sections. The project entails developing a data visualization that effectively communicates a compelling story to the audience and readers.
In the first module, we will learn what is data visualization, why data visualization is necessary in data science field, what data visualization will do and what skills data visualization need. We will first get started with R by learning R basic and R Markdown to prepare the data visualization in the course.
涵盖的内容
20个视频8篇阅读材料4个作业1个讨论话题2个非评分实验室
显示有关单元内容的信息
20个视频•总计87分钟
Welcome to Data Visualization•5分钟
Meet Your Instructor•2分钟
Module 1 Overview•2分钟
Introduction to Data Visualization•6分钟
Data Visualization techniques•7分钟
Some examples of data visualization•5分钟
Introduction to R•4分钟
RStudio Panes•2分钟
Data types in R•3分钟
Data structures in R•8分钟
Introduction to objects in R•2分钟
Create objects/variables in R•4分钟
Create objects with different data structures in R•8分钟
Remove and save objects•5分钟
A Simple Tutorial to Get You Started with R•6分钟
Introduction to R Markdown file•2分钟
The Structure of an R Markdown File•3分钟
Create Your .Rmd File and Use Knitr to Convert .Rmd to .html or PDF•2分钟
Format the text in R Markdown•7分钟
Code Chunks-Hide code and information•5分钟
8篇阅读材料•总计89分钟
Meet Your Course Staff•5分钟
Read the Course Syllabus•10分钟
Install R and RStudio•30分钟
RStudio Lab (In-Browser Option)•10分钟
Materials for Understanding Basic R•1分钟
RMarkdown Cheat Sheet•30分钟
Install the "knitr" and "rmarkdown" Packages•2分钟
Module 1 Summary•1分钟
4个作业•总计12分钟
Activities and Skills in Data Visualization•3分钟
Basic R information•3分钟
Create Objects in R•3分钟
R Markdown Basic Information.•3分钟
1个讨论话题•总计15分钟
Introduce Yourself•15分钟
2个非评分实验室•总计120分钟
Practice Lab: Getting Started with RStudio/R•60分钟
Create your first R Markdown file by default and name it "My first R Markdown file"•60分钟
Graphics Components for Data Visualization
第 2 单元•小时 后完成
单元详情
Understanding the elements and components of data visualization is essential for data visualization because it provides a systematic framework for creating effective and meaningful visual representations of data.In this module, we will explore the grammar of graphics, explain some rational, and introduce principles in data visualization, as well as describe the common Exploratory Data Analysis (EDA) idioms' features and applications.
涵盖的内容
8个视频2篇阅读材料3个作业1个讨论话题
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8个视频•总计34分钟
Module 2 Overview•2分钟
Introduction to data visualization•4分钟
Introduction to the Grammar of Graphics•3分钟
Marks and Channels•2分钟
Color models•5分钟
Exploratory Data Analysis (EDA)•8分钟
Some examples•3分钟
Data Visualization Principles•7分钟
2篇阅读材料•总计26分钟
Principles of Effective Data Visualization•25分钟
Module 2 Summary•1分钟
3个作业•总计11分钟
Components of Data Visualization•3分钟
Color Systems•3分钟
Best Practices in Data Visualization •5分钟
1个讨论话题•总计30分钟
Why is Rainbow Color Not Suggested in Data Visualization?•30分钟
ggplot2
第 3 单元•小时 后完成
单元详情
Let's get our hands wet with real data visualization-producing a graph. In this module, we will explore the powerful data visualization package ggplot2. In this module, you will learn basic usages of ggplot() function, the fill and color aesthetics, and learn to create a histogram using ggplot() and setting suitable bin numbers or bin width.
涵盖的内容
8个视频5篇阅读材料1个作业1个编程作业1次同伴评审1个非评分实验室
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8个视频•总计32分钟
Module 3 Overview•2分钟
Introduction to ggplot2•3分钟
Basic usage of ggplot() function•8分钟
Colors in ggplot()•7分钟
Introduction to Histogram•1分钟
Bins in Histogram•2分钟
Plot a Single Histogram•1分钟
Grouped Histogram•8分钟
5篇阅读材料•总计7分钟
Installation of ggplot2•1分钟
ggplot2 Cheatsheet•1分钟
Change Histogram Outline and Fill Colors•2分钟
Legends in ggplot2•2分钟
Module 3 Summary•1分钟
1个作业•总计3分钟
ggplot() Usage•3分钟
1个编程作业•总计60分钟
Step 1: Grouped Histogram in R•60分钟
1次同伴评审•总计60分钟
Step 2: Grouped histogram in R•60分钟
1个非评分实验室•总计60分钟
Single histogram•60分钟
Embed Images and Tables in R Markdown Files
第 4 单元•小时 后完成
单元详情
Now you have conducted the basic data wrangling, documented your work in R Markdown, and created your first data visualization in previous modules. In this module, you will learn to embed, create and refer to images and tables in R Markdown. In addition, you will learn to produce scatter plots, which further enrich your visualization experience and enhance your visualization skills.
涵盖的内容
10个视频2篇阅读材料1个讨论话题1个非评分实验室
显示有关单元内容的信息
10个视频•总计32分钟
Module 4 Overview•2分钟
Save graphs as png and jpeg•4分钟
Output graphs into a pdf file•2分钟
Embed images in R Markdown files•6分钟
Refer to images in R Markdown files•2分钟
Create tables in R Markdown•2分钟
Index tables in R Markdown files•2分钟
About scatter plots and bubble plots•3分钟
A scatter plot with ggplot2•6分钟
A scatter plot with ggplot2•4分钟
2篇阅读材料•总计31分钟
Embed Images and Tables in R Markdown•30分钟
Module 4 Summary•1分钟
1个讨论话题•总计40分钟
Does a Scatter Plot Prove the Causation?•40分钟
1个非评分实验室•总计60分钟
Ungraded Lab: A HTML report in R Markdown file with images and tables, and refer and index the tables•60分钟
Boxplot and Multiple-view Layout
第 5 单元•小时 后完成
单元详情
This module will continue for one of the common EDA idioms-box plots to enrich your data visualization experience and will explore new technique-layout multiple plots on one page. In this module, you will learn to produce boxplots using ggplot(), interpret boxplots and arrange multiple plots on one page.
涵盖的内容
7个视频3篇阅读材料1个作业1个编程作业1个讨论话题1个非评分实验室
显示有关单元内容的信息
7个视频•总计34分钟
Module 5 Overview•2分钟
Introduction to Boxplots•3分钟
Basic Box plot in R•6分钟
Boxplot in R_Change outline colors and fill colors•4分钟
Arranging multiple plots on a page•5分钟
Use facets in ggplot2•6分钟
grid.arrange() function•7分钟
3篇阅读材料•总计16分钟
Interpretation of Boxplots•7分钟
Laying out multiple plots on a page•8分钟
Module 5 Summary•1分钟
1个作业•总计30分钟
Step 2: Self-Check Mutiple-view plots including histogram, boxplots and scatter plot with data provided•30分钟
1个编程作业•总计30分钟
Step 1: Multiple-view plots including histogram, boxplots and scatter plot with data provided •30分钟
1个讨论话题•总计10分钟
Why Boxplot Could be Used to Detect Outliers•10分钟
1个非评分实验室•总计60分钟
Plot multiple group boxplots with data provided•60分钟
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攻读学位
课程 是 Ball State University提供的以下学位课程的一部分。如果您被录取并注册,您已完成的课程可计入您的学位学习,您的学习进度也可随之转移。
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