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  • What is Data Visualization?
  • Why Should you use Data Visualization in Analytics and Business Intelligence projects?
  • Data Visualization in Python
  • Seaborn Library
  • Distribution Plot, Histograms, KDE Plots, Scatter Plot, Rug Plot, Joint Plot, Pair Plot, Bar Plot, Count Plot, Box Plot, Violin Plot, Strip Plot, Swarm Plot
  • Heat Map, Pair Plot, Sub Plot

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.

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.

This course is designed to teach analysts, students interested in data science, statisticians, and data scientists how to analyze real-world data by creating professional-looking charts and using numerical descriptive statistics techniques in Python 3.

We'll teach you how to program with Python, how to analyze and create amazing data visualizations with Python! You can use this course as your ready-to-go reference for your own project.


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!



  • Describe what makes a good or bad visualization

  • Understand best practices for creating basic charts

  • Identify the functions that are best for particular problems

  • Create a visualization seaborn

  • Distribution Plot

  • Histograms

  • KDE Plots

  • Scatter Plot

  • Rug Plot

  • Joint Plot

  • Pair Plot

  • Bar Plot

  • Count Plot

  • Box Plot

  • Violin Plot

  • Strip Plot

  • Swarm Plot

  • Heat Map

  • Pair Plot

  • Sub Plot

  • No introductory skill level of Python programming required
  • Desire to learn!
  • Data Scientists, Data Analysts.
  • Who wants to start their data visualization journey
  • Who Is curious about how to build effective and impactful graphs
  • Who Is looking to break into the business intelligence, analytics or data visualization field
  • Anyone who want to express thoughts, findings, insights from any kind of data using data Visualization.
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  • Section 1 : Importing the Data 1 Lectures 00:06:29

    • Lecture 1 :
  • Section 2 : Distribution Plot 2 Lectures 00:09:33

    • Lecture 1 :
    • Lecture 2 :
    • Uses of distribution plot
  • Section 3 : KDE PLOT 3 Lectures 00:21:51

    • Lecture 1 :
    • KDE Introduction
    • Lecture 2 :
    • KDE Plot Part 1
    • Lecture 3 :
    • KDE Plot Part 2
  • Section 4 : Scatter Plot 1 Lectures 00:11:40

    • Lecture 1 :
    • Scatter Plot
  • Section 5 : Rug Plot 1 Lectures 00:03:45

    • Lecture 1 :
    • Rug Plot
  • Section 6 : Joint Plot 1 Lectures 00:07:55

    • Lecture 1 :
    • Joint Plot
  • Section 7 : Pair Plot 1 Lectures 00:05:55

    • Lecture 1 :
    • Pair Plot
  • Section 8 : Bar Plot 1 Lectures

    • Lecture 1 :
    • Bar Plot
  • Section 9 : Count Plot 1 Lectures 00:02:00

    • Lecture 1 :
    • Count Plot
  • Section 10 : Box Plot 1 Lectures

    • Lecture 1 :
    • Box Plot
  • Section 11 : Violin Plot 1 Lectures 00:04:50

    • Lecture 1 :
    • Violin Plot
  • Section 12 : Strip Plot 1 Lectures 00:04:16

    • Lecture 1 :
    • Strip Plot
  • Section 13 : Swarm Plot 1 Lectures 00:03:37

    • Lecture 1 :
    • Swarm Plot
  • Section 14 : Heat Map 1 Lectures 00:09:25

    • Lecture 1 :
    • Heat Map
  • Section 15 : Pair Grid 1 Lectures 00:08:45

    • Lecture 1 :
    • Pair Grid
  • Section 16 : Sub Plots 1 Lectures 00:05:16

    • Lecture 1 :
    • Sub Plots
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