Mastering Data Visualization with Python

Mastering Data Visualization with Python

Visualize data using pandas, matplotlib and seaborn libraries for data analysis and data science

What you’ll learn

  • Understand what plots are suitable for a type of data you have
  • Visualize data by creating various graphs using pandas, matplotlib and seaborn libraries

Requirements

Description

This course will help you draw meaningful knowledge from the data you have.

Three systems of data visualization in R are covered in this course:

A. Pandas    B. Matplotlib  C. Seaborn

 

A. Types of graphs covered in the course using the pandas package:

Time-series: Line Plot

Single Discrete Variable: Bar Plot, Pie Plot

Single Continuous Variable:  Histogram, Density or KDE Plot, Box-Whisker Plot

Two Continuous Variable: Scatter Plot

Two Variable: One Continuous, One Discrete: Box-Whisker Plot

B. Types of graphs using Matplotlib library:

Time-series: Line Plot

Single Discrete Variable: Bar Plot, Pie Plot

Single Continuous Variable:  Histogram, Density or KDE Plot, Box-Whisker Plot

Two Continuous Variable: Scatter Plot

In addition, we will cover subplots as well, where multiple axes can be plotted on a single figure.

C. Types of graphs using Seaborn library:

In this we will cover three broad categories of plots:

relplot (Relational Plots): Scatter Plot and Line Plot

displot (Distribution Plots): Histogram, KDE, ECDF and Rug Plots

catplot (Categorical Plots): Strip Plot, Swarm Plot, Box Plot, Violin Plot, Point Plot and Bar plot

In addition to these three categories, we will cover these three special kinds of plots: Joint Plot, Pair Plot and Linear Model Plot

In the end, we will discuss the customization of plots by creating themes based on the style, context, colour palette and font.

Who this course is for:

  • Data Science, Six Sigma and other professionals interested in data visualization
  • Professionals interested in creating publication quality plots
  • Professionals who are not happy with the plots created in MS Excel, and see them as dull and boring

Course content

5 sections • 73 lectures • 9h 26m total length
  • Introduction
  • Getting Data and Using the Pandas Package to Plot
  • Matplotlib Library for Plots
  • Seaborn Library for Plots
  • Python for Absolute Beginners
Last updated 3/2021
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https://www.udemy.com/course/mastering-data-visualization-with-python/

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