Learning path: Jupyter: Learn Jupyter Skills from Scratch
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107 students enrolled

Learning path: Jupyter: Learn Jupyter Skills from Scratch

Probe deep to enhance your expertise into interactive computing, sharing, and integrating using Jupyter
3.4 (8 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
107 students enrolled
Created by Packt Publishing
Last updated 11/2017
English [Auto]
Current price: $119.99 Original price: $199.99 Discount: 40% off
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This course includes
  • 4.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 78 lectures 04:20:23
+ Jupyter Notebook for All - Part I
28 lectures 01:23:12

This video gives an overview of the entire course.

Preview 03:41

Know about Jupyter IDE.

First Look at Jupyter

The ability to install Jupyter on Windows.

Installing Jupyter on Windows

The ability to install Jupyter on Mac.

Installing Jupyter on Mac

Learn about the Jupyter notebook structure and the workflow of Jupyter with some basic operations.

Notebook Structure, Workflow, andBasic Operations

Learn to execute arbitrary code.

Security and Configuration Operations in Jupyter
Learn to use Python scripts in a Jupyter Notebook.
Preview 04:12
Learn Python data access in Jupyter.
Python Data Access in Jupyter

The ability to develop a Python script that uses pandas to see if there is any effect of using it in Jupyter.

Python pandas in Jupyter

Learn to plot the data from the number of births in a year.

Python Graphics in Jupyter

Learn to simulate rolling a pair of dice and looking at the outcome.

Python Random Numbers in Jupyter

The ability to make R scripting available in your Jupyter installation.

Adding R Scripting to Your Installation

Learn how the steps progress for an R script.

Basic R in Jupyter
The ability to use the Irisdataset to build R installations and the common use of R in several visualizations
R Dataset Access and Visualization in Jupyter

The ability to use R's cluster analysis functions to determine the clustering in thedataset.

R Cluster Analysis and Forecasting
Learn to make separate steps for Julia scripting available in your Jupyter installation.
Adding Julia Scripting to Your Installation

Learn to use the Iris dataset for some standard analysis.

Basic Julia in Jupyter
Know about Julia’s limitations and the standard capabilities.
Julia Limitations and Standard Capabilities
Learn to use the plot function with standard defaults no type arguments to generate a Scatterplot.
Julia Visualizations in Jupyter

Ability to use Vega for a pie chart and to produce an interesting visualization.

Julia Vega Plotting and Parallel Processing

Learn about the small function that determines the larger of two numbers.

Julia Control Flow, Regular Expressions, and Unit Testing
Learn to install JavaScript scripting on Mac.
Adding JavaScript Scripting to Your Installation

Learn the Hello world program using JavaScript in Jupyter notebook.

JavaScript Hello World Jupyter Notebook

The ability to use JavaScript for application development with data access and analysis features.

Basic JavaScript in Jupyter
Handle stats-analysis package which has many of the common statistics you may want toperform on your data.
Node.js stats-analysis Package and JSON Handling
Learn to use all of the plotly features.
Node.js plotly Package

Ability to create threads using Node.js.

Node.js Asynchronous Threads
Know about decision tree package with an example of a machine learning package
Node.js decision-tree Package
Test your Knowledge
5 questions
+ Jupyter Notebook for All - Part II
29 lectures 01:13:52

This video gives an overview of the entire course.

Preview 03:47

Learn how to install widgets and learn about the basics of widgets

Installing Widgets and Widget Basics

Learn how the interact widget can affect many different variations of user input control.

Interact Widget

Learn to know where the parameters of the widget display need a control at runtime.

Interactive Widget

Know how to customize the display.


Learn to have a set of properties to adjust for your display.

Widget Properties

Know how to share notebooks by using HTML and server interaction.

Sharing Notebooks on a Notebook Server
The ability to replace your website with the URL of the website where you can access the notebook.
Sharing Notebooks on a Web Server and Docker

Learn to use R programming in your notebook and to install the R tool set on your machine.

Sharing Notebooks on a Public Server

Learn how to convert notebooks to other formats.

Converting Notebooks
Learn to use a simple notebook that asks the user for some information and displays other information.
Sample Interactive Notebook

The ability to generate a new instance of the Jupyter server and attach all further interactions between that user and Jupyter.

Know about Jupyter Hub operations and its functions.
JupyterHub – Operation
Learn to know about Docker and its mechanism that can be used to allow multiple users of the same notebook without collision.
Docker and Its Installation
Learn how to build Jupyter image for Docker.
Building Your Jupyter Image for Docker
Build the Scala package to launch the Scala shell.
Installing the Scala Kernel

Learn to access data and perform some simpler statistics.

Scala Data Access in Jupyter
The ability to make the calculations in Scala and parse out the CSV file.
Scala Array Operations

Learn to pull data from the Scala random library and present it in histogram for illustrative purposes.

Scala Random Numbers in Jupyter

Define a multiplier function and learn how to take other functions as arguments or returns a function as its result.

Scala Closures and Higher Order Definitions

Learn about Scala pattern matching using Jupyter and Scala case classes.

Scala Pattern Matching and Case Classes

Know how to mutable the variables

Scala Immutability

Learn to collect mutable and immutable usage of Scala collections and its arguments.

Scala Collections and Named Arguments

Define a set of features that can be implemented by classes.

Scala Traits

Learn how to install spark in Mac and Windows.

Apache Spark

Initialize spark; it takes every line and computes the length of the prefix statement.

Our First Spark Script and Word Count
Learn how to use map to estimate the Pi and will learn about the log file examination.
Estimate Pi and Log File Examination

Know about spark primes & Spark test file analysis to run a series of numbers through a filter.

Spark Primes and Text File Analysis

Know about some historical data and determine some useful attributes.

Spark – Evaluating History Data
Test your Knowledge
4 questions
+ Jupyter In Depth
21 lectures 01:43:19

This video provides an overview of the entire course.

Preview 01:38

In this video, we will get the environment running and store configurations for restoration.

Setting Up

In this video, we will see how to give Jupyter command line operations.

Jupyter CLI Introduction

In this video, we will see how to explore the Jupyter core package.

The Jupyter Core Module

In this video, we will be shown how to explore the Jupyter client package.

The Jupyter Client
In this video, we will see how to explore the Jupyter console.
The Jupyter Console

This videos guides us how to break out the configuration values and interact with them using ConfigParser and Traitlets config objects.

Generating Configurations from the CLI
The aim of this video is to show how to quickly and easily store configurations in a local or remote database using Pandas and SQLite.
Storing Configurations
In this video, we will see overriding configurations and file system monitoring in Jupyter with Python.
Configuration Extras
In this video, we will create simple maps with Jupyter widget Ipyleaflet.

In this video, we will do a sample experiment with audio files in Jupyter to showcase Ipywidgets.

More Fun with Ipywidgets
This video gives a brief tour of the capabilities of the GitHub REST API and GraphQL.
Using the GitHub API

The aim of this video is to Obtain actionable intelligence from the Twitter REST API.

Utilizing Twitter
In this video, we will see how to get started with the Notebook package and what are the included tools. We will have a quick look at the workings of an included script to set up SSL in the Jupyter Notebook and the available Notebooks in the documentation.
The Notebook Package
The Jupyter Drive module allows you to mount a Google drive as a local content source. Using the API Quick Start, a client application is coded for the user to interact with the service from a running notebook.
Gdrive Custom Content Managers

With a custom extension coded from open source libraries you can securely backup your research.

Customer Bundler Extensions
How can we automatically back up my work so that we do not lose my code on accident? We can add file save hooks so that our bundler extension is automatically run when the file is saved.
Custom File Save Hook
We have a really neat widget, can we serve it using the Notebook server? With request handlers, you can link code to URL and host patterns for dynamic content delivery.
Custom Request Handlers

This video guides us how to convert Notebooks to dashboards and display them.

Crafting a Dashboard

In this video, we will see what is required to run a dashboard.

The Dashboard Server
In this video, we will understand how Bokeh data applications are ported to the dashboard server.
Bokeh Dashboards
Test your Knowledge
3 questions
  • Basic understanding on programming languages (preferably JavaScript, Python, R, Julia, Scala, and Spark) is needed.

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.
Who this course is for:
  • 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.