Learning Path: Jupyter: Interactive Computing With Jupyter

More than 50 videos to help you get started with the Jupyter Notebook

What you'll learn

Install and run the Jupyter Notebook system on your machine

Implement programming languages such as R, Python, Julia, and JavaScript with the Jupyter Notebook

Use interactive widgets to manipulate and visualize data in real time

Share your Notebook with colleagues

Invite your colleagues to work with you in the same Notebook

Perform scientific application development by leveraging Big Data tools such as Spark

Requirements

Modern Windows or Macintosh machine with Internet access

Basic programming knowledge of Python, R, JavaScript, Julia, Scala, and Spark would be beneficial

Description

Are you looking forward to write, execute, and comment your live code and formulae all under one roof? Or do you want an application that will let you forget your worries in scientific application development? If yes, then this Learning Path will surely help you out by provide all that you need to know to work with the Jupyter Notebook — a console-based approach to interactive computing! 
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The Jupyter Notebook is an open-source web application that supports more than 40 programming languages including those popular in data science such as Python, R, Julia, and Scala. This Learning Path is a one-stop solution for all you want to know about the Jupyter Notebook. It will teach you everything you need to know to perform scientific computation with ease. 
This Learning Path starts with a brief introduction to Jupyter Notebook and its installation in different environments. Next, you will see how to integrate the Jupyter system with different programming languages such as R, Python, JavaScript, and Julia. Moving ahead, you will master interactive widgets, namespaces, and working with Jupyter in the multiuser mode. You will also see how to share your Notebook with colleagues. Finally, you will learn to access Big Data using Jupyter. 
By the end of the Learning Path, you will be able to write code, compute mathematical formulae, create graphics, and view the output, all in a single document and web browser, using the Jupyter Notebook.
About the Author:
For this course, we have combined the best works of this esteemed author:
Dan Toomey has been developing applications for over 20 years. He has worked in a variety of industries and companies in roles from the sole contributor to VP/CTO level. For the last 10 years or so, he has been contracting to companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corporation again as a contractor developer in the area.

Overview

Section 1: Jupyter Notebook for All – Part I

Lecture 1 The Course Overview

Lecture 2 First Look at Jupyter

Lecture 3 Installing Jupyter on Windows

Lecture 4 Installing Jupyter on Mac

Lecture 5 Notebook Structure, Workflow, andBasic Operations

Lecture 6 Security and Configuration Operations in Jupyter

Lecture 7 Basic Python in Jupyter

Lecture 8 Python Data Access in Jupyter

Lecture 9 Python pandas in Jupyter

Lecture 10 Python Graphics in Jupyter

Lecture 11 Python Random Numbers in Jupyter

Lecture 12 Adding R Scripting to Your Installation

Lecture 13 Basic R in Jupyter

Lecture 14 R Dataset Access and Visualization in Jupyter

Lecture 15 R Cluster Analysis and Forecasting

Lecture 16 Adding Julia Scripting to Your Installation

Lecture 17 Basic Julia in Jupyter

Lecture 18 Julia Limitations and Standard Capabilities

Lecture 19 Julia Visualizations in Jupyter

Lecture 20 Julia Vega Plotting and Parallel Processing

Lecture 21 Julia Control Flow, Regular Expressions, and Unit Testing

Lecture 22 Adding JavaScript Scripting to Your Installation

Lecture 23 JavaScript Hello World Jupyter Notebook

Lecture 24 Basic JavaScript in Jupyter

Lecture 25 Node.js stats-analysis Package and JSON Handling

Lecture 26 Node.js plotly Package

Lecture 27 Node.js Asynchronous Threads

Lecture 28 Node.js decision-tree Package

Section 2: Jupyter Notebook for All – Part II

Lecture 29 The Course Overview

Lecture 30 Installing Widgets and Widget Basics

Lecture 31 Interact Widget

Lecture 32 Interactive Widget

Lecture 33 Widgets

Lecture 34 Widget Properties

Lecture 35 Sharing Notebooks on a Notebook

Lecture 36 Sharing Notebooks on a Web Server and Docker

Lecture 37 Sharing Notebooks on a Public Server

Lecture 38 Converting Notebooks

Lecture 39 Sample Interactive Notebook

Lecture 40 JupyterHub

Lecture 41 JupyterHub – Operation

Lecture 42 Docker and Its Installation

Lecture 43 Building Your JupyterImage for Docker

Lecture 44 Installing the Scala Kernel

Lecture 45 Scala Data Access in Jupyter

Lecture 46 Scala Array Operations

Lecture 47 Scala Random Numbers in Jupyter

Lecture 48 Scala Closures andHigher Order Definitions

Lecture 49 Scala Pattern Matching andCase Classes

Lecture 50 Scala Immutability

Lecture 51 Scala Collections and Named Arguments

Lecture 52 Scala Traits

Lecture 53 Apache Spark

Lecture 54 Our First Spark Script and Word Count

Lecture 55 Estimate Pi andLog File Examination

Lecture 56 Spark Primes andText File Analysis

Lecture 57 Spark – Evaluating History Data

This Learning Path caters to all developers, students, and educators who want to execute code, see the output, and comment all in the same document, the browser,Data science professionals will also find this Learning Path very useful in performing technical and scientific computing in a graphical, agile manner