Complete Course On Data Visualization, Matplotlib And Python

Master Matplotlib Anatomy and Learn Seaborn, Altair, Plotly, Streamlit, Dash, Pandas, Suitable for All Purposes

What you'll learn

Review the python visualization landscape

Explore core visualization concepts

Use matplotlib to build and customize visualizations

Build and customize simple plots with pandas

Learn about seaborn and use it for statistical visualizations

Create visualizations using Altair

Generate interactive plots using the Plotly library

Design interactive dashboards using Streamlit

Construct highly custom and flexible dashboards using Plotly’s Dash framework

Data Analyst, data visualizations, Design interactive, developers, framework, libraries

Python, TalkPython, technologies, trainingtalkpy, Matplotlib, plotting

Requirements

Developers and Data Analysts that have some experience with python but have not developed a competency in a python visualization library

This course is also helpful for those that feel restricted by their current plotting tools and wish to explore other options.

All software used during this course, including editors, Python language, etc., are 100% free and open source. You won’t have to buy anything to take the course.

Description

COURSE IN THE NUTSHELLConcise and to the point, as I appreciate your time and don't have the luxury to tell you my storyEasy to understand and tailored for a broad audience, as it only requires a basic knowledge of Python and onlyAboutHave you ever been confused by all the different python plotting libraries? Have you tried to make a “simple” plot and gotten stuck and been unable to move forward? Do you want to make sophisticated, interactive data visualizations in python? If you answer yes, to any of these questions, then this course is for you.What’s this course about and how is it different?The python data visualization landscape has many different libraries. They are all powerful and useful but it can be confusing to determine what works best for you. This course is unique because you will learn about many of the most popular python visualization libraries. You will start by learning how to use each library to build simple visualizations. You will also explore more complex usage and identify the scenarios where each library shines.By the end of this course, you will have a basic working knowledge of how to visualize data in python using multiple libraries. You will also learn which library is best for you and your coding style. Along the way, you’ll learn general visualization concepts to make your plots more effective.In addition to the overview material, we will cover some of the more complex, interactive visualization dashboard technologies.What topics are coveredIn this course, you will:– Review the python visualization landscape– Explore core visualization concepts– Use matplotlib to build and customize visualizations– Build and customize simple plots with pandas– Learn about seaborn and use it for statistical visualizations– Create visualizations using Altair– Generate interactive plots using the Plotly library– Design interactive dashboards using Streamlit– Construct highly custom and flexible dashboards using Plotly’s Dash frameworkWho is this course for?Developers and Data Analysts that have some experience with python but have not developed a competency in a python visualization library. This course is also helpful for those that feel restricted by their current plotting tools and wish to explore other options.Note: All software used during this course, including editors, Python language, etc., are 100% free and open source. You won’t have to buy anything to take the course.TELL ME MORE…After completing this course you will master Matplotlib on an intuition level and feel comfortable visualizing and customizing Matplotlib, Seaborn and Pandas charts of any complexities. More specifically, this course is a great resource if you are interested in:How Matplotlib WorksHow to create charts from simple to scientific ones with Matplotlib, Pandas and SeabornHow to customize charts of any complexities with easeTo achieve the objectives, I split this course into the following sections:Matplotlib AnatomyAs the name implies, in this section you will learn how Matplotlib works and how a variety of charts are generated.It gives you a solid understanding and a lot of aha-moments when it comes to creating and / or customizing charts that you haven't dealt with before.Create 2D ChartsIn this section, you will generate plethora of charts using Matplotlib OOP, and Pandas and mix them together to achieve the maximum efficiency and granular control over graphs.Axes Statistical ChartsHere we will learn how to make statistical charts such as Auto Correlation, Boxplots, Violinplots and KDE plots with Matplotlib OOP and Pandas.SeabornSeaborn, a high-level interface to Matplotlib helps make statistical plots with ease and charm. It is a must-know library for data exploration and super easy to learn. And in this section, we will create Regression plots, Count plots, Barplots, Factorplots, Jointplots, Boxplots, Violin plots and more.Course Summary and ExercisesThis section has dual purposes.For one, it is a good summary of the course and provides you with exercises to test your knowledge and then provide solutions for comparison.Secondly, If you are short-on time, you can start here and then move to other sections if you seek more granular coverage of the topic or when you have more time available.TOOLS USEDDashStreamlitplotlyAltairMatplotlibSeabornPandas

Overview

Section 1: Introduction

Lecture 1 Python Data Visualization

Lecture 2 Statistics aren't enough.

Lecture 3 Why Visualize Data

Lecture 4 Why Python

Lecture 5 Python Visualization Eco System

Lecture 6 Course Objectives

Lecture 7 Topic outlines

Lecture 8 Python Check

Lecture 9 Source Code

Section 2: Visualization Concepts

Lecture 10 Introduction to Visualization Concepts

Lecture 11 Aesthetics

Lecture 12 Data Types

Lecture 13 Visualization Variables

Lecture 14 Colors

Lecture 15 Small Multiple Plots

Lecture 16 Analysis types

Lecture 17 Working with Data

Section 3: Matplotlib

Lecture 18 Introduction to Matplotlib

Lecture 19 Matplotlib History

Lecture 20 Matplotlib landscape

Lecture 21 System Setup

Lecture 22 Data Set

Lecture 23 Figure Overview

Lecture 24 Interface Types

Lecture 25 Launching notebooks

Lecture 26 Reading Data

Lecture 27 Pyplot Example

Lecture 28 Object Oriented API

Lecture 29 Histograms

Lecture 30 Figures And Axes

Lecture 31 Saving Images

Lecture 32 Quick References

Lecture 33 Line Plots

Lecture 34 Bar Charts

Lecture 35 Scatter Plots

Lecture 36 Styles

Lecture 37 Regressions

Lecture 38 Customizing Multiple Plots

Lecture 39 References

Lecture 40 Summary

Section 4: Pandas

Lecture 41 Introduction to Pandas

Lecture 42 Pandas Overview

Lecture 43 API Overview

Lecture 44 Basic API Example

Lecture 45 API Summary

Lecture 46 Specialized hist and Box Plot API

Lecture 47 Advanced Specialized Plots

Lecture 48 Advanced Plot Summary

Lecture 49 Pandas Conclusion

Section 5: Seaborn

Lecture 50 Introduction To Seaborn

Lecture 51 Seaborn Overview

Lecture 52 Getting Started

Lecture 53 Figures and Axes level PLot

Lecture 54 Data Set Changes

Lecture 55 Displot

Lecture 56 Catplot

Lecture 57 Relplot

Lecture 58 Seaborn API Summary

Lecture 59 Displot Replot and Facetting

Lecture 60 Catplot API Summary

Lecture 61 Specialized plots

Lecture 62 Heatmap

Lecture 63 Pair and jointplot

Lecture 64 Customizing Seaborn Summary

Lecture 65 Seaborn Summary

Section 6: Altair

Lecture 66 Introduction to Altair

Lecture 67 Overview

Lecture 68 Vega Lite

Lecture 69 Installing

Lecture 70 Shorthand API

Lecture 71 Basic Shorthand API

Lecture 72 Additional Examples of the Basic API

Lecture 73 Longhand API

Lecture 74 Longhand Overview

Lecture 75 Data Type

Lecture 76 Type Viz Alterations

Lecture 77 Concat Charts

Lecture 78 Faceting

Lecture 79 layers

Lecture 80 Multiple Chart Summary

Lecture 81 Amazon Data Set

Lecture 82 Amazon Authors

Lecture 83 Reference Examples

Lecture 84 Conclusion

Section 7: Plotly

Lecture 85 Introduction To plotly

Lecture 86 OverView

Lecture 87 API Intro

Lecture 88 Installing

Lecture 89 Basic Plotting

Lecture 90 Customizing Map

Lecture 91 Additional Plot Types

Lecture 92 API Overview

Lecture 93 Scatter Plots

Lecture 94 Line Bar Area

Lecture 95 Regression treemap Heatmap

Lecture 96 Facetting

Lecture 97 Annotations

Lecture 98 Annotation Summary

Lecture 99 Conclusion

Section 8: Streamlit

Lecture 100 introduction to Streamlit

Lecture 101 Background

Lecture 102 Installation

Lecture 103 Basic App Concept

Lecture 104 Simple App Example

Lecture 105 Streamlit Running overview

Lecture 106 API Summary

Lecture 107 Widget Summary

Lecture 108 Widget Interactivity

Lecture 109 User input

Lecture 110 Show Charts

Lecture 111 Sidebar Intros

Lecture 112 Sidebar Detail

Lecture 113 Conclusion

Section 9: Dash

Lecture 114 Introduction to Dash

Lecture 115 Overview

Lecture 116 Why Dash

Lecture 117 Getting Started

Lecture 118 Program Structure

Lecture 119 First App

Lecture 120 Running App

Lecture 121 Component Overview

Lecture 122 HTML

Lecture 123 interactive App

Lecture 124 interactive App Demo

Lecture 125 Callback reference

Lecture 126 Final App Overview

Lecture 127 Full app Part

Lecture 128 Full App data filtering

Lecture 129 Full App Demo

Lecture 130 Advance Topics

Lecture 131 Conclusion

Section 10: Whole Course Conclusion

Lecture 132 Course review

Lecture 133 Objectives

Lecture 134 Data Vis Concepts

Lecture 135 Matplotlib

Lecture 136 pandas

Lecture 137 Seaborn

Lecture 138 Altair

Lecture 139 Plotly

Lecture 140 Streamlit

Lecture 141 Dash

Lecture 142 My Workflow

Anyone who wants to gain granular control over Matplotlib Charts,Anyone who wants to gain an intuition behind Matplotlib,Anyone who wants to learn to make a variety of charts with Matplotlib OOP, Seaborn and Pandas,Anyone who wants to learn to make a variety of charts with Altair, Plotly, Streamlit, Dash