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The Ultimate Drawing Course Beginner to Advanced...
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Description

Welcome to
Mastering Data Visualization
! In this course, you're going to learn about the
Theory and Foundations of Data Visualization
so that you can create
amazing charts that are informative, true to the data, and communicatively effective
.
Have you noticed there are more and more charts generated every day? If you turn on the TV, there's a bar chart telling you the evolution of COVID, if you go on Twitter, boom! a lot of line charts displaying the evolution of the price of gas. In newspapers, lots and lots of infographics telling you about the most recent discovery... The reason for that is that now we have
lots of data
, and the most natural way to communicate data is in visual form: that is, through Data Visualization. But, have you noticed all of the mistakes in those visualizations? I have to tell you,
many of the charts that I see regularly have one problem or another
. Maybe their color choices are confusing, they chose the wrong type of chart, or they are displaying data in a distorted way.
Actually, that happens because
more and more professional roles now require to present data visually, but there's few training on how to do it correctly
. This course aims to solve this gap. If there's one thing I can promise you is that,
after completing this course, you'll be looking at charts at a completely different way
. You will be able to distinguish good and bad visualizations, and, more importantly, you will be able to tell when a graph is lying and how to correct it.
If you need to analyze, present or communicate data professionally at some point, this course is a must.
Actually, even if you don't need to actually draw plots for a living, this course is hugely useful. After all, we are all consumers of data visualizations, and we need to identify when charts are lying to us. (As an example, my mother attended one of my classes and now she's spotting mistakes in a lot of the media she sees everyday!)
I really encourage you to deepen your knowledge on Data Visualization. It's not a difficult topic, and we will start from the basics. You don't need any previous knowledge. I'll teach you everything you need to know along the way and we'll go straight to the point. No rambling.
I really hope to see you in class!
Who this course is for:
Programmers / Researchers / Designers that want to learn how to produce top-quality plots
Anyone who has to present data at some point!
Data Scientists
Academic scientists having to publish in scientific journals
Journalists / Data Journalists
Communication experts
Also the general public: you should know how graphs work because they're everywhere!

What you'll learn

Learn to design effective data communication

Improve your plots up to a professional level

Learn to choose and design the appropriate plot for your purpose

Learn to create compelling graphs that do not lie

Learn to avoid the traps your data can fall into

Learn to distinguish between good, bad and wrong visualization

Learn the golden rules on Graphical Excellence, Integrity and Sophistication

Learn the most common crimes in plotting to be able to avoid them!

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Introduction
4
1.1-Introduction
1.2-About this course: the 5 Ws
1.3-Examples of Data Visualization
1.4-The Problem with Data Visualization
2-Graphical Perception
9
2.1-A very quick interruption...
2.2-Introduction to this Chapter
2.3-The science of human graphical perception
2.4-The Elementary Perceptual Tasks (Part 1)
2.5-The Elementary Perceptual Tasks (Part 2)
2.6-The Ranking of the Elementary Perceptual Tasks
2.7-How good is your Graphical Perception?
2.8-Identify the Elementary Perceptual Tasks
2.9-Redesigning charts
3-The Golden Rules of Data Visualization
12
3.1-Introduction to this Chapter
3.2-Graphical Excellence
3.3-Graphical Distortion
3.4-Graphical Integrity: The Lie Factor
3.5-Exercise: Calculate the Lie Factor (updated!)
3.6-Labeling and Annotation
3.7-Data Variation vs. Design Variation
3.8-The problem with dimensions
3.9-Some advice regarding dimensions
3.10-The Data-Ink Ratio
3.11-Data Density
3.12-Proportion and Scale
4-Statistical Traps: How not to fall in them
5
4.1-Correlation doesn't sell newspapers
4.2-Selection Bias and Data Attrition
4.3-The Importance of Context
4.4-The Incorrect Normalization of the Data
4.5-The Simpson's Paradox
5-Plots: Find the correct plot for your data
7
5.1-Wait, do you really need a plot?
5.2-Types of Plots
5.3-Plotting Distributions
5.4-Plotting Relationships between variables
5.5-Plotting Rankings
5.6-Comparing Part to Whole
5.7-Plotting spatial data: Maps
6-Plot Crimes
7
6.1-Introduction to the chapter
6.2-When is it okay to cut the Y-axis?
6.3-Shading the Area of a Line Plot
6.4-The Spaghetti Chart
6.5-Error bars and the Dynamite Plot
6.6-How to choose the right colors
6.7-Common mistakes with color
7-What Next?
1
7.1-Congratulations! What now?