Mastering Data Visualization with Python using Seaborn Library

Build your Data Analysis and Visualization skills with Python’s Seaborn Library

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Last Updated: 2022-11-18 06:11:53 English Course Type: Self Learning

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!

WHAT YOU WILL LEARN

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.

Section 1 : Importing the Data 1 Lectures 00:06:29
Lecture 1 :
Importing the Data Preview
Section 2 : Distribution Plot 2 Lectures 00:09:33
Lecture 1 :
DISPLOT
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 00:00:00
Lecture 1 :
Bar Plot
Section 9 : Count Plot 1 Lectures 00:02:00
Lecture 1 :
Count Plot
Section 10 : Box Plot 1 Lectures 00:00:00
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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