What you'll learn

student will be able to analyze ,explore, experiment ,manipulate , fix, prepare, and present data easily, quickly, and beautifully using MatDescriptionlib and python .This will enable students to take one one step forward for the goal of becoming data scientist or machine learning expert.
This course is designed with lots of hands of and live coding , so students will get the in depth knowledge of the different features of data analysis and data visualization techniques available with matDescriptionlib.

Requirements

Basics of Python

Description

This MatDescriptionlib tutorial takes you through the basics Python data visualization to complete expert.

Humans are very visual creatures.we understand things better when we see things visualized. However, the step to presenting analyses, results or insights can be a bottleneck.you might not even know where to start or you might have already a right format in mind.So this is the right course to understand and clear all your doubts regarding data visualization with MatDescriptionlib.

The anatomy of a MatDescriptionlib Description: what is a subDescription? What are the Axes? What exactly is a figure?

Description creation, which could raise questions about what module you exactly need to import (pylab or pyDescription?), how you exactly should go about initializing the figure and the Axes of your Description, how to use matDescriptionlib in Jupyter notebooks, etc.

Descriptionting routines, from simple ways to Description your data to more advanced ways of visualizing your data.

Basic Description customizations, with a focus on Description legends and text, titles, axes labels and Description layout.

Saving, showing, clearing, ... your Descriptions: show the Description, save one or more figures to, for example, pdf files, clear the axes, clear the figure or close the Description, etc.

Lastly, i will cover two ways in which you can customize matDescriptionlib with style sheets and the rc settings.

Live Descriptioning and updating graph with real time data.

Four complete projects with hands on and live coding.

Who this course is for:

Beginner to advanced