Data Visualization with Python

About Data Visualization with Python

A warm welcome to the

Data Visualization with Python

course by

Uplatz

.

Data Visualizations

allow humans to explore data in many different ways and see patterns and insights that would not be possible when looking at the raw form. Humans crave narrative and visualizations allow us to pull a story out of our stores of data. Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed. As datasets become bigger and more complex, only AI, materialized views, and more sophisticated coding languages will be able to glean insights from them. Advanced analytics is paving the way for the next wave of innovation _._ The human brain processes visual data better than any other kind of data, which is good because most of the information our brains process is visual. Visual processing and responses both occur more quickly compared to other stimuli. A good visualization could be the difference between hard to digest piles of data and useful business information. With increasing volume of data, it is next to impossible to rely on just one way frequency tables and statistics to understand the data. Good visualizations can accelerate the process of understanding data and gaining insights.

Why Python for Data Visualization?

Python offers multiple great graphing libraries that come packed with lots of different features. No matter if you want to create interactive, live or highly customized plots python has an excellent library for you. Python programming language has different types of libraries for all kind of projects. Likewise, python has various libraries for visualization of Data, so that user can understand the dataset in very detailed way and analyze it properly. Each library of visualization has its own specification. Using the particular libraries for specific task helps the user to complete the task in more easy and accurate way. Some liberates work better than the others. Python uses two exclusive libraries for data visualization.

_Matplotlib_

Python based plotting library offers matplotlib with a complete 2D support along with limited 3D graphic support. It is useful in producing publication quality figures in interactive environment across platforms. It can also be used for animations as well. Matplotlib is a library used for plotting graphs in the Python programming language. It is used plot 2 - dimensional arrays. Matplotlib is built on NumPy arrays. It is designed to work with the border SciPy stack. It was developed by John Hunter in 2002. The benefit of visualization is that user can have visual access to large amounts of the dataset. Matplotlib is a library which is consists of various plots such as histogram, bar, line, scatter, etc. Matplotlib comes with a huge variety of plots. Plots are helpful for understanding patterns, trends and for making correlations. It has instruments for reasoning about quantitative information. As matplotlib was the very first library of data visualization in python, many other libraries are developed on top of it or designed to work parallel to it for the analysis of the dataset.

_Seaborn_

Seaborn is a library for creating informative and attractive statistical graphics in python. This library is built on top of the Matplotlib library. Seaborn offers various features such as built in themes, color palettes, functions and tools to visualize univariate, bivariate, linear regression, matrices of data, statistical time series etc. that allows us to build complex visualizations. Seaborn is a library of Python programming basically used for making statistical graphics of the dataset. It is also integrated closely with Pandas, which is used for the data structure of Datasets. Seaborn is very helpful to explore and understand data in a better way. It provides a high level of a crossing point for sketching attractive and informative algebraic graphics. Some of the other key Python libraries used in data visualization are:

Pandas visualization - easy to use interface, built on Matplotlib

Ggplot - based on R’s ggplot2, uses Grammar of Graphics

Pygal

Missingno

Plotly - can create interactive plots

Gleam

Leather

Geoplotlib

Bokeh

Folium

Uplatz

offers this complete course on Data Visualization with Python. This Data Visualization in Python course will help you use Python's most popular and robust data visualization libraries. Learn how to use Matplotlib, Seaborn, Bokeh, and others to create useful static and interactive visualizations of categorical, aggregated, and geospatial data.

Data Visualization with Python - Course Curriculum

1. Introduction to Data Visualization

What is data visualization

Benefits of data visualization

Importance of data visualization

Top Python Libraries for Data Visualization

2. Matplotlib

Introduction to Matplotlib

Install Matplotlib with pip

Basic Plotting with Matplotlib

Plotting two or more lines on the same plot

3. Numpy and Pandas

What is numpy?

Why use numpy?

Installation of numpy

Example of numpy

What is a panda?

Key features of pandas

Python Pandas - Environment Setup

Pandas – Data Structure with example

4. Data Visualization tools

Bar chart

Histogram

Pie Chart

5. More Data Visualization tools

Scatter Plot

Area Plot

STACKED Area Plot

Box Plot

6. Advanced data Visualization tools

Waffle Chart

Word Cloud

HEAT MAP

7. Specialized data Visualization tools (Part-I)

Bubble charts

Contour plots

Quiver Plot

8. Specialized data Visualization tools (Part-II)

Three-Dimensional Plotting in Matplotlib

3D Line Plot

3D Scatter Plot

3D Contour Plot

3D Wireframe Plot

3D Surface Plot

9. Seaborn

Introduction to seaborn

Seaborn Functionalities

Installing seaborn

Different categories of plot in Seaborn

Some basic plots using seaborn

10. Data Visualization using Seaborn

Strip Plot

Swarm Plot

Plotting Bivariate Distribution

Scatter plot, Hexbin plot, KDE, Regplot

Visualizing Pairwise Relationship

Box plot, Violin Plots, Point Plot

11. Project on Data Visualization