Learning Path: Jupyter: Learn Jupyter Skills From Scratch

Probe deep to enhance your expertise into interactive computing, sharing, and integrating using Jupyter

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 Jupyter Notebook

Use interactive widgets to manipulate and visualize data in real time

Start sharing your Notebook with colleagues

Organize your Notebook using Jupyter namespaces

Access big data in Jupyter

Configure Jupyter, console, client, and core modules

Build data dashboards

Monitor application directories

Use remote notebooks

Requirements

Basic understanding on programming languages (preferably JavaScript, Python, R, Julia, Scala, and Spark) is needed.

Description

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. It is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. It supports a number of languages via plugins ("kernels"), such as Python, Ruby, Haskell, R, Scala and Julia. So, if you're interested to learn interactive computing with Jupyter, then go for this Learning Path.

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 highlights of this Learning Path are:

Implement programming languages such as R, Python, Julia, and JavaScript with Jupyter Notebook
Access big data in Jupyter

Let’s take a quick look at your learning journey. This Learning Path starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. You’ll learn to integrate the Jupyter system with different programming languages such as R, Python, JavaScript, and Julia. You’ll then explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you'll master interactive widgets, namespaces, and working with Jupyter in multiuser mode. The Learning Path will walk you through the core modules and standard capabilities of the console, client, and notebook server. Finally, you will be able to build dashboards in a Jupyter notebook to report back information about the project and the status of various Jupyter components.


Towards the end of this Learning Path, you’ll have an in-depth knowledge on Jupyter Notebook and know how to integrate different programming languages such as R, Python, Julia, and JavaScript with it.

Meet Your Experts:

We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:


Dan Toomey has been developing applications for over 20 years. He has worked in a variety of industries and companies of all sizes, in roles from sole contributor to VP/CTO level. For the last 10 years or so, he has been contracting companies in the eastern Massachusetts area under Dan Toomey Software Corp. Dan has also written R for Data Science and Learning Jupyter with Packt Publishing.


Jesse Bacon is a hobbyist programmer that lives and works in the northern Virginia area. His interest in Jupyter started academically while working through books available from Packt Publishing. Jesse has over 10 years of technical professional services experience and has worked primarily in logging and event management.

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 Server

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 Jupyter Image 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 and Higher Order Definitions

Lecture 49 Scala Pattern Matching and Case 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 and Log File Examination

Lecture 56 Spark Primes and Text File Analysis

Lecture 57 Spark – Evaluating History Data

Section 3: Jupyter In Depth

Lecture 58 The Course Overview

Lecture 59 Setting Up

Lecture 60 Jupyter CLI Introduction

Lecture 61 The Jupyter Core Module

Lecture 62 The Jupyter Client

Lecture 63 The Jupyter Console

Lecture 64 Generating Configurations from the CLI

Lecture 65 Storing Configurations

Lecture 66 Configuration Extras

Lecture 67 Ipyleaflet

Lecture 68 More Fun with Ipywidgets

Lecture 69 Using the GitHub API

Lecture 70 Utilizing Twitter

Lecture 71 The Notebook Package

Lecture 72 Gdrive Custom Content Managers

Lecture 73 Customer Bundler Extensions

Lecture 74 Custom File Save Hook

Lecture 75 Custom Request Handlers

Lecture 76 Crafting a Dashboard

Lecture 77 The Dashboard Server

Lecture 78 Bokeh Dashboards

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 in the browser. Data science professionals will also find this Learning Path very useful in performing technical and scientific computing in a graphical, agile manner.