Mastering Data Visualization with Python 2021
MP4 | h264, 1280x720 | Lang: English | Audio: aac, 44100 Hz | 9h 26m | 3.85 GB
Visualize data using pandas, matDescriptionlib ans seaborn libraries for data analysis and data science

What you'll learn
Understand what Descriptions are suitable for a type of data you have
Visualize data by creating various graphs using pandas, matDescriptionlib and seaborn libraries
Requirements
Some basic knowledge of Python is expected. However this course does include a quick overview of Python knowledge required for this course.
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. MatDescriptionlib C. Seaborn

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

Time-series: Line Description

Single Discrete Variable: Bar Description, Pie Description

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

Two Continuous Variable: Scatter Description

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

B. Types of graphs using MatDescriptionlib library:

Time-series: Line Description

Single Discrete Variable: Bar Description, Pie Description

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

Two Continuous Variable: Scatter Description

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

C. Types of graphs using Seaborn library:

In this we will cover three broad categories of Descriptions:

relDescription (Relational Descriptions): Scatter Description and Line Description

disDescription (Distribution Descriptions): Histogram, KDE, ECDF and Rug Descriptions

catDescription (Categorical Descriptions): Strip Description, Swarm Description, Box Description, Violin Description, Point Description and Bar Description

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

In the end, we will discuss the customization of Descriptions 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 Descriptions
Professionals who are not happy with the Descriptions created in MS Excel, and see them as dull and boring

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