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Learn Jupyter Notebook and iPython and its use in cloud computing, deploying graphs and Data Science

What you will learn

In this IPython/Jupyter Notebook Course, we have tried to cover all the topics that fall under Jupyter and iPython

After finishing this course, you will be able to use the Jupyter environment very efficiently.

Learn how to bring python in use when it comes to developing the complex application that demands high computing power or needs some complex functionality

You will be able to solve the real-life problems that need an application for a solution.

Description

The first IPython/Jupyter Notebook Course is going to be a six hours continuous tutorial that has been introduced to help you in getting started with Jupyter-iPython. At the beginning, of the course, you will get a brief introduction to IPython and Jupyter. We have tried to cover every topic with some real-life or actual scenarios to make you understand things much better. After the introduction part, you will be learning about the requirement and implementation of Jupyter and iPython. Later on, we have included some best examples to show you how things work. The video will make you aware of the Jupyter environment so that you can understand things when the educator will show you how to work on this tool. We have captured the screen and shown things practically just to ensure that you are getting an in-depth idea about the tool as you will be supposed to be familiar with this tool by the end of this unit.

IPython and Jupyter collectively can be defined as the development environment that facilitates the efficient development of the application. The iPython is an interactive shell that lets the different languages interact with it and make the programming a bit easier and convenient. The Jupyter environment provides the best way to perform highly complex computations, can be used for graphical representation of data, and much more. It is mainly used in the development environment to develop an enterprise application. It also provides pre-defined functionalities that can help the program to be developed in a very short period. Though it reduces the load of too much manual coding, it is a bit complex to use but its outcome is very fruitful and it also helps the development team to save a lot of time. In the currently implemented framework called agile which demands the completion of work in very less time, it can be proved very much useful.


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In this IPython/Jupyter Notebook Course, we have tried to cover all the topics that fall under Jupyter and iPython. Which means that it welcomes all kinds of an audience to enhance their skills. After finishing this course, you will be able to use the Jupyter environment very efficiently. You will be able to contribute to the development and will learn how to develop a program in less time. You will be learning how to use python or how to bring python in use when it comes to developing the complex application that demands high computing power or needs some complex functionality to be added to it. As the iPython has been developed by the Python programming language, you will also get an efficient exposure to python. You will be able to solve the real-life problems that need an application for a solution. One will also learn how to deploy this tool in the real work environment so that it could be leveraged to solve problems in the organization.

English
language

Content

Introduction

Introduction Jupyter or IPython Notebook

Environment

Environment Setup

Installation and Configuring

Installation of Ipython Notebook
Configuring Jupyter Notebook

Notebook Walkthrough

IPython Notebook
More on IPython Notebook
Simple Arithmetic Calculations
Working on Arithmetic Calculation Continues
Basic Definitions
Markdown Cells
Matplot Library

Plotting Graphs

Line Plots
Scatter Plots
Histograms
Writing Plots to File
Numpy Arange
Numpy Arange Continues
Numpy Arrays
A Complete Graph

Advanced Charts

Color Control
Formatting Markers
Histograms in Detail
Histograms in Detail Continues
Bar Charts
Pie Charts
Scatter Charts
Polar Charts
Annotations and Texts
Decorating Arrows

Advanced Matplotlib with Jupyter

Ipython Pylab
PLotting Multiple Axes
Logarithmic Axes
Shared Axes
Date Formatting with Matplotlib
Date Formatting with Matplotlib Continues
Date and Days Formatting
Mathtext Module
LaTex Renderers
Contour Plotting
Image Plotting