Data Analytics: Python Visualizations

Learn more than 20 types of Data Visualizations through using the Matplotlib, Seaborn and Bokeh Libraries in Python

What you'll learn
Learn to draw over 20 different kinds of Charts and Graphs using Python coding.
Learn Data Analytics Techniques / Exploratory Data Analysis (EDA) using several Data Generation and Manipulation Methods.
Extensive applications of NumPy and Pandas Data capabilities using Python.
In depth coverage of Matplotlib, Seaborn and Bokeh Visualization Libraries.
Learn visualization concepts used by numerous Business and Scientific Applications.
Extensive amount of Python - Matplotlib/Seaborn/Bokeh code used in the course are attached as downloadable resources for you to try out while you learn.

Description
Data Analytics is given meaning by the ability of Visualizations. Any Data Analytics work always finish with Data Visualization.

A staggering amount of Data is generated by Businesses every day. Businesses need to use these data in a very meaningful ways to take decisions.

Data in its raw and voluminous form are not useful to Businesses. Visualizations come to the rescue to create powerful Graphs and Plots that help create meaning out of underlying Data.

Every IT endeavor today has associated Visualization components. It has become paramount for Analysts, Developers, Architects, IT Managers as well as Techno-Business professionals and every Data Science and Data professional to understand the concepts of Visualizations thoroughly using popular tools and techniques to help Businesses drive crucial decisions for their divisions and organizations.

This Course on Python Visualizations brings you just the knowledge required to develop an in-depth understanding of a very crucial part of data Analytics, i.e. creating powerful visualizations using the Python language. It teaches you three very popular and powerful Python visualization libraries – Matplotlib, Seaborn and Bokeh.

This course takes a holistic approach towards teaching Visualizations:

- Take real-life, business scenarios and raw data to go through detailed Exploratory Data Analysis (EDA) techniques to prepare your raw data to suit the appropriate visualization needs.

- Data Analytics and Exploratory Data Analysis Techniques using multiple different data structures using Numpy and Pandas libraries.

- Teach Chart/Graph types, customization and vectorization techniques.

Throughout the course, extensive amount of Code demo along with concepts as a balanced approach to teaching.

Every concept is taught by going deeper into foundational techniques and deeper customizations.

5 Quizzes are infused at logical points to validate the learning effectiveness.

[NOTE: All the code used in the Lectures are attached as downloadable resources (.ipynb files). You may download them and try out while going through the lectures.]

Who this course is for:
Developers
Python Developers
Data Scientists
Data Analysts
Big Data Professionals
Systems Analysts
Architects
IT Managers and Leaders
Techno-Functional Analysts
Business Analysts
Anyone who is in a role in handling and managing Data