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Learning Path: Jupyter: Interactive Computing with the Jupyter Notebook

Video Description

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

In Detail

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 is just right for you! This Learning Path is a one-stop solution on Project Jupyter and will teach you everything you need to know to perform scientific computation with ease.

The Jupyter Notebook is an open-source web application and has support for more than 40 programming languages including those popular in data science such as Python, R, Julia, and Scala.

This Learning Path starts with a brief on the Jupyter Notebook and its installation in different environments. You will learn how to write code, mathematics, graphics, and output, all in a single document and in a web browser, using Jupyter. 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 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, mathematics, graphics, and output, all in a single document and web browser, using the Jupyter Notebook.

Prerequisites: Basic programming knowledge of Python, R, Julia, Scala, and Spark.

Resources: Code downloads and errata:

  • Jupyter Notebook for All – Part I

  • Jupyter Notebook for All – Part II

  • PATH PRODUCTS

    This path navigates across the following products (in sequential order):

  • Jupyter Notebook for All – Part I (1h 23m)

  • Jupyter Notebook for All – Part II (1h 14m)

  • Table of Contents

    1. Chapter 1 : Jupyter Notebook for All – Part I
      1. The Course Overview 00:03:41
      2. First Look at Jupyter 00:04:38
      3. Installing Jupyter on Windows 00:02:56
      4. Installing Jupyter on Mac 00:00:47
      5. Notebook Structure, Workflow, andBasic Operations 00:10:52
      6. Security and Configuration Operations in Jupyter 00:03:28
      7. Basic Python in Jupyter 00:04:12
      8. Python Data Access in Jupyter 00:02:10
      9. Python pandas in Jupyter 00:01:44
      10. Python Graphics in Jupyter 00:01:51
      11. Python Random Numbers in Jupyter 00:01:16
      12. Adding R Scripting to Your Installation 00:04:33
      13. Basic R in Jupyter 00:02:03
      14. R Dataset Access and Visualization in Jupyter 00:03:01
      15. R Cluster Analysis and Forecasting 00:03:12
      16. Adding Julia Scripting to Your Installation 00:03:21
      17. Basic Julia in Jupyter 00:02:43
      18. Julia Limitations and Standard Capabilities 00:02:34
      19. Julia Visualizations in Jupyter 00:01:51
      20. Julia Vega Plotting and Parallel Processing 00:02:35
      21. Julia Control Flow, Regular Expressions, and Unit Testing 00:04:34
      22. Adding JavaScript Scripting to Your Installation 00:02:30
      23. JavaScript Hello World Jupyter Notebook 00:02:14
      24. Basic JavaScript in Jupyter 00:02:16
      25. Node.js stats-analysis Package and JSON Handling 00:02:25
      26. Node.js plotly Package 00:01:50
      27. Node.js Asynchronous Threads 00:01:33
      28. Node.js decision-tree Package 00:02:35
    2. Chapter 2 : Jupyter Notebook for All – Part II
      1. The Course Overview 00:03:48
      2. Installing Widgets and Widget Basics 00:02:52
      3. Interact Widget 00:03:06
      4. Interactive Widget 00:00:58
      5. Widgets 00:03:38
      6. Widget Properties 00:04:46
      7. Sharing Notebooks on a Notebook 00:05:40
      8. Sharing Notebooks on a Web Server and Docker 00:02:03
      9. Sharing Notebooks on a Public Server 00:01:38
      10. Converting Notebooks 00:05:50
      11. Sample Interactive Notebook 00:01:53
      12. JupyterHub 00:01:47
      13. JupyterHub – Operation 00:04:53
      14. Docker and Its Installation 00:02:14
      15. Building Your JupyterImage for Docker 00:03:08
      16. Installing the Scala Kernel 00:02:32
      17. Scala Data Access in Jupyter 00:00:57
      18. Scala Array Operations 00:00:52
      19. Scala Random Numbers in Jupyter 00:01:13
      20. Scala Closures andHigher Order Definitions 00:01:22
      21. Scala Pattern Matching andCase Classes 00:01:58
      22. Scala Immutability 00:01:02
      23. Scala Collections and Named Arguments 00:01:15
      24. Scala Traits 00:01:33
      25. Apache Spark 00:03:01
      26. Our First Spark Script and Word Count 00:03:32
      27. Estimate Pi andLog File Examination 00:02:15
      28. Spark Primes andText File Analysis 00:01:30
      29. Spark – Evaluating History Data 00:02:49