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Explore the basics of data visualization and exploratory data analysis in the [course_title] course.

The course includes three motivating examples and teaches about the ggplot2 which is a data visualisation package for the statistical programming language R, to code. The course illustrates some of the case studies related to world health and economics and infectious disease trends in the United States. The course also discusses how failure to discover problems often leads to flawed analyses and false discoveries.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

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Course Credit:  Harvard University.

Course Curriculum

Introduction to Data Visualization 00:05:00
Lesson 1
Introduction to Distributions 00:02:00
Data Types 00:02:00
Describe Heights to ET 00:02:00
Smooth Density Plots 00:07:00
Normal Distribution 00:02:00
Quantile-Quantile Plots 00:03:00
Percentiles 00:02:00
Boxplots 00:02:00
Distribution of Female Heights 00:02:00
Lesson 2
ggplot 00:02:00
Graph Components 00:02:00
Creating a New Plot 00:02:00
Layers 00:02:00
Tinkering 00:01:00
Scales, Labels, and Colors 00:02:00
Add-on Packages 00:02:00
Other Examples 00:02:00
Lesson 3
dplyr 00:02:00
The Dot Placeholder 00:02:00
Group By 00:02:00
Sorting Data Tables 00:02:00
Lesson 4
Case Study: Trends in World Health and Economics 00:02:00
Gapminder Dataset 00:02:00
Life Expectancy and Fertility Rates 00:02:00
Faceting 00:05:00
Time Series Plots 00:05:00
Transformations 00:08:00
Stratify and Boxplot 00:07:00
Comparing Distributions 00:06:00
Density Plots 00:06:00
Ecological Fallacy 00:05:00
Lesson 5
Introduction to Data Visualization Principles 00:02:00
Encoding Data Using Visual Cues 00:02:00
Know When to Include Zero 00:03:00
Do Not Distort Quantities 00:02:00
Order by a Meaningful Value 00:02:00
Show the Data 00:03:00
Ease Comparisons: Use Common Axes 00:03:00
Consider Transformations 00:02:00
Ease Comparisons: Compared Visual Cues Should Be Adjacent 00:02:00
Slope Charts 00:02:00
Encoding a Third Variable 00:01:00
Case Study: Vaccine 00:02:00
Avoid Pseudo and Gratuitous 3D Plots 00:02:00
Avoid Too Many Significant Digits 00:02:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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