MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 14 lectures (1h 31m) | Size: 356.8 MB
This Course is designed for those learners who wish to acquire knowledge on the advance details of data visualization


What you’ll learn:
Creating visualizations with MatDescriptionlib
More complicated classes and functions in MatDescriptionlib
Advanced topics for experienced MatDescriptionlib users and developers
MatDescriptionlib has support for visualizing information with a wide array of colors and colormaps. It cover the basics of how these colormaps look, etc
Requirements
Basic understanding of Python language
Description
MatDescriptionlib is one of the most popular Python packages used for data visualization. It is a cross-platform library for making 2D Descriptions from data in arrays. It provides an object-oriented API that helps in embedding Descriptions in applications using Python GUI toolkits such as PyQt, WxPythonotTkinter. It can be used in Python and IPython shells, Jupyter notebook and web application servers also.
MatDescriptionlib is a low level graph Descriptionting library in python that serves as a visualization utility.
MatDescriptionlib was created by John D. Hunter.
MatDescriptionlib is open source and we can use it freely.
MatDescriptionlib is mostly written in python, a few segments are written in C, Objective-C and Javascript for Platform compatibility.
Audience
This Course is designed for those learners who wish to acquire knowledge on the details of data visualization.
Prerequisites
MatDescriptionlib is written in Python and makes use of NumPy, the numerical mathematics extension of Python. We assume that the readers of this tutorial have basic knowledge of Python.
The source code for MatDescriptionlib is located at this github repository
This MatDescriptionlib lecture takes you through the basics Python data visualization: the anatomy of a Description, pyDescription and pylab, and much more
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, but then questions like “Is this the right way to visualize the insights that I want to bring to my audience?” will have definitely come across your mind.
When you’re working with the Python Descriptionting library MatDescriptionlib, the first step to answering the above questions is by building up knowledge on topics like:
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, you’ll briefly cover two ways in which you can customize MatDescriptionlib: with style sheets and the settings.
What Does A MatDescriptionlib Python Description Look Like?
At first sight, it will seem that there are quite some components to consider when you start Descriptionting with this Python data visualization library. You’ll probably agree that it’s confusing and sometimes even discouraging seeing the amount of code that is necessary for some Descriptions, not knowing where to start yourself and which components you should use.
Luckily, this library is very flexible and has a lot of handy, built-in defaults that will help you out tremendously. As such, you don’t need much to get started: you need to make the necessary imports, prepare some data, and you can start Descriptionting with the help of the Description() function!
Who this course is for
Developers
Programmers
Aspiring Data Scientist
Computer Science students
Homepage

https://www.udemy.com/course/matDescriptionlib-a-comprehensive-course-on-data-visualization/

 

 

 

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