Python Data Visualization: Matplotlib & Seaborn Masterclass

Python Data Visualization: Matplotlib & Seaborn Masterclass

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 94 lectures (7h 30m) | 2.67 GB

Bring your data to LIFE and master Python’s most popular data analytics & visualization libraries: Matplotlib & Seaborn

This is a hands-on, project-based course designed to help you learn two of the most popular Python packages for data visualization: Matplotlib & Seaborn.

We’ll start with a quick introduction to data visualization frameworks and best practices, and review essential visuals, common errors, and tips for effective communication and storytelling.

From there we’ll dive into Matplotlib fundamentals, and practice building and customizing line charts, bar charts, pies & donuts, scatterplots, histograms and more. We’ll break down the components of a Matplotlib figure and introduce common chart formatting techniques, then explore advanced customization options like subplots, GridSpec, style sheets and parameters.

Finally we’ll introduce Python’s Seaborn library. We’ll start by building some basic charts, then dive into more advanced visuals like box & violin plots, PairPlots, heat maps, FacetGrids, and more.

Throughout the course you’ll play the role of a Consultant at Maven Consulting Group, a firm that provides strategic advice to companies around the world. You’ll practice applying your skills to a range of real-world projects and case studies, from hotel customer demographics to diamond ratings, coffee prices and automotive sales.


Intro to Data Visualization

Learn data visualization frameworks and best practices for choosing the right charts, applying effective formatting, and communicating clear, data-driven stories and insights

Matplotlib Fundamentals

Explore Python’s Matplotlib library and use it to build and customize several essential chart types, including line charts, bar charts, pie/donut charts, scatterplots and histograms

PROJECT #1: Analyzing the Global Coffee Market

Read data into Python from CSV files provided by a major global coffee trader, and use Matplotlib to visualize volume and price data by country

Advanced Customization

Apply advanced customization techniques in Matplotlib, including multi-chart figures, custom layout and colors, style sheets, gridspec, parameters and more

PROJECT #2: Visualizing Global Coffee Production

Continue your analysis of the global coffee market, and leverage advanced data visualization and formatting techniques to build a comprehensive report to communicate key insights

Data Visualization with Seaborn

Visualize data with Python’s Seaborn library, and build custom visuals using additional chart types like box plots, violin plots, joint plots, pair plots, heatmaps and more

PROJECT #3: Analyzing Used Car Sales

Use Seaborn and Matplotlib to explore, analyze and visualize automotive auction data to help your client identify the best deals on used service vehicles for the business

What you’ll learn

  • Master the essentials of Matplotlib & Seaborn, two of Python’s most powerful data visualization packages
  • Design and format 20+ chart types using Matplotlib & Seaborn, including line charts, bar charts, scatter plots, histograms, violin plots, heatmaps and more
  • Learn advanced customization options like subplots, gridspec, style sheets and parameters
  • Apply best practices for data visualization, storytelling, formatting and visual design
  • Build powerful, practical skills for modern analytics and business intelligence
Table of Contents

Getting Started
1 Course Structure & Outline
2 READ ME Important Notes for New Students
3 DOWNLOAD Course Resources
4 Introducing the Course Project
5 Setting Expectations
6 Jupyter Installation & Launch

Intro to Data Visualization
7 Why Visualize Data
8 Key Questions
9 Essential Visuals
10 Chart Formatting & Storytelling
11 Common Visualization Mistakes
12 Key Takeaways

Matplotlib Fundamentals
13 Intro to Matplotlib
14 Plotting Methods
15 Plotting DataFrames
16 ASSIGNMENT Plotting DataFrames
17 SOLUTION Plotting DataFrames
18 Anatomy of a Matplotlib Figure
19 Chart Titles & Font Sizes
20 Chart Legends
21 Line Styles
22 Axis Limits
23 Figure Sizes
24 Custom Axis Ticks
25 Vertical Lines
26 Adding Text
27 PRO TIP Text Annotations
28 Removing Borders
29 ASSIGNMENT Formatting Charts
30 SOLUTION Formatting Charts
31 Line Charts
32 Stacked Line Charts
33 Dual Axis Charts
34 ASSIGNMENT Dual Axis Line Charts
35 SOLUTION Dual Axis Line Charts
36 Bar Charts
37 ASSIGNMENT Bar Charts
38 SOLUTION Bar Charts
39 Stacked Bar Charts
40 Grouped Bar Charts
41 Combo Charts
42 ASSIGNMENT Advanced Bar Charts
43 SOLUTION Advanced Bar Charts
44 Pie & Donut Charts
45 ASSIGNMENT Pie & Donut Charts
46 SOLUTION Pie & Donut Charts
47 Scatterplots & Bubble Charts
48 Histograms
49 ASSIGNMENT Scatterplots & Histograms
50 SOLUTION Scatterplots & Histograms
51 Key Takeaways

PROJECT #1 Analyzing the Global Coffee Market
52 Project #1 Introduction
53 Project #1 Solution Walkthrough

Advanced Customization
54 Intro to Advanced Customization
55 Subplots
56 ASSIGNMENT Subplots
57 SOLUTION Subplots
58 GridSpec
60 SOLUTION GridSpec
61 Color Options
62 Color Palettes
64 SOLUTION Colors
65 Style Sheets
66 ASSIGNMENT Style Sheets
67 SOLUTION Style Sheets
68 rcParameters
69 Saving Figures & Images
70 Key Takeaways

PROJECT #2 Visualizing Global Coffee Production
71 Project #2 Introduction
72 Project #2 Solution Walkthrough

Visualization with Seaborn
73 Intro to Seaborn
74 Basic Formatting Options
75 Bar Charts & Histograms
76 ASSIGNMENT Bar Charts & Histograms
77 SOLUTION Bar Charts & Histograms
78 Box & Violin Plots
79 ASSIGNMENT Box & Violin Plots
80 SOLUTION Box & Violin Plots
81 Linear Relationship Charts
82 Jointplots
83 PairPlots
84 ASSIGNMENT Linear Relationship Charts
85 SOLUTION Linear Relationship Charts
86 Heatmaps
87 ASSIGNMENT Heatmaps
88 SOLUTION Heatmaps
89 FacetGrid
90 Matplotlib Integration
91 Key Takeaways

PROJECT #3 Analyzing Used Car Sales
92 Project #3 Introduction
93 Project #3 Solution Walkthrough