Principles of Beautiful Data Visualization (2022)

Name: Principles of Beautiful Data Visualization (2022)
Rating: 4.7 (92 reviews)

Fundamental Principles, Cool Graphs & Charts in Python

Created byPhenil Buch

Last updated 1/2022

English

What you'll learn

Understand the core concepts of Data Visualization
Level-up Data Viz skills regardless of the programming language or visualization tool you use
What makes Data Visualization "Beautiful"?
Visualizing quantity of variables, their distributions and relationships
Appropriate charts and plots to use for appropriate data
Choosing colors, scales, fonts and symbols for elegant Data Visualization
Advanced Data Visualization with Python using Plotly, Seaborn and Matplotlib

Course content

8 sections • 35 lectures • 2h 33m total length

Introduction and Course Outline1:28
Explore the core principles and elements of data representation, learn to visualize different data types, and practice loading and plotting data in Python to tell compelling stories with charts.
Principles of Beautiful Data Visualization1:23
Elements of Data Representation2:13
Types of Data and Scales3:33

Bar Plots Part 19:30
Visualize penguin species counts with a seaborn bar plot in Python, customizing colors, order, and axis labels for a clear, well-labeled data visualization.
Bar Plots Part 28:19
Dot Plots4:40
Explore dot plots as an alternative to bar charts, noting how axis ranges affect interpretation, and learn to create vertical or horizontal dot plots that emphasize counts and order.
Bar Plots Part 33:53
Heatmaps6:26

Histograms7:26
Explore how histograms visualize distributions in the penguins dataset by adjusting bins and coloring by species. Incorporate kernel density estimates and consider log scale to compare multiple numerical distributions clearly.
Density Plots8:16
Explore density plots as an alternative to histograms using seaborn to visualize distributions, compare bandwidth settings, and display multiple distributions side by side with density estimates.
Box Plots6:54
Violin Plots3:30

Pie Charts Part 13:36
Explore how to visualize proportions with pie charts, noting there is no perfect chart and when tree maps may help for many slices.
Pie Charts Part 23:44
Continue refining pie charts by converting to donut charts, adjusting inner and outer radii, removing explosions, and displaying percentages outside the donut for clearer data with improved title placement.
Side by side bars and Stacked bars11:46
Sankey Diagrams5:29
Tree Maps4:15

Scatterplots3:31
Learn to visualize associations with scatterplots in Seaborn using the penguins dataset, customize markers, colors, and sizes, place legends outside the plot, and overlay kernel density estimates for richer insights.
Bubble Charts and Scales & Redundancy3:26
Learn how to use scales and redundancy to improve data visualizations, encoding penguin species with color and shape in a scatter plot and in a bubble chart.
3D Scatterplots6:49
Explore 3d scatterplots to visualize relationships among three features using the penguins dataset, color-coding by species and enabling hover to read x, y, z values.
Highlighting in Scatterplots2:10
Pair Plots2:48

Line Graphs5:36
Emphasis Using Trendlines3:05
Visualizing Uncertainty in Trendlines1:53
Visualize trendline uncertainty by adjusting the confidence interval and polynomial order, using a compact 300-point data subset; observe how higher orders cause overfitting while lower orders often generalize better.
Animated Charts5:36
Animate data visualizations by importing celluloid, creating a figure and camera, and progressively plotting date versus closing price to build a gif from snapshot frames.
Course Summary and Thank You0:35
Celebrate completing this course and apply principles like simplicity, redundancy, and highlighting figures to craft beautiful data visualizations.

Requirements

Beginner Level Python Programming Skills

Description

This course will enhance a student's understanding of charts, plots and graphs and bring it to a whole new level. Whether a student already knows something about Data Visualizations or not, they will definitely take something away after completing this course.

Although this course uses the Python Programming Language to create data visualizations, any other tool or programming language could be used to apply the principles that have been taught in this course.

- Achieve the main goal of data visualization which is to communicate data or information clearly and effectively to readers.

- Learn various do's and don'ts of data visualizations, color scales and plot animation regardless of programming language or visualization tool.

- Visualize amounts, distributions, proportions, associations and time-series data.

- Learn how to visualize data like a pro.

- Make use of 3 different Python plotting libraries including Plotly, Seaborn, Matplotlib.

- Make use of your learnings to create beautiful data visualizations that could be used in print media, reports and social media.

- Master visual storytelling to communicate a message supported by the data.

- Identify trends and make data engaging and easily digestible

- Evoke an emotional response from whoever takes a look at your data visualized through various types of charts and plots.

Who this course is for:

Data Science Enthusiasts
Machine Learning Enthusiasts
Graphic Designers
Digital Marketers

Principles of Beautiful Data Visualization (2022)

What you'll learn

Explore related topics

Course content

Principles of Data Visualization4 lectures • 9min

Introduction to Data Visualization in Python3 lectures • 10min

Position and Color Scales4 lectures • 12min

Visualizing Amounts5 lectures • 33min

Visualizing Distributions4 lectures • 26min

Visualizing Proportions5 lectures • 29min

Visualizing Associations5 lectures • 19min

Visualizing Time-series Data5 lectures • 17min

Requirements

Description

Who this course is for: