Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Learning Path: Jupyter: Interactive Computing with Jupyter
Rating: 3.7 out of 5(37 ratings)
300 students
Created byPackt Publishing
Last updated 4/2017
English

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

Course content

2 sections • 57 lectures • 2h 37m total length

  • The Course Overview3:41

    This video gives an overview of the entire course.

  • First Look at Jupyter4:38

    Know about Jupyter IDE.

  • Installing Jupyter on Windows2:56

    The ability to install Jupyter on Windows.

  • Installing Jupyter on Mac0:46

    The ability to install Jupyter on Mac.

  • Notebook Structure, Workflow, andBasic Operations10:52

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

  • Security and Configuration Operations in Jupyter3:28

    Learn to execute arbitrary code.

  • Basic Python in Jupyter4:12

    Learn to use Python scripts in a Jupyter Notebook.

  • Python Data Access in Jupyter2:10

    Learn Python data access in Jupyter.

  • Python pandas in Jupyter1:44

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

  • Python Graphics in Jupyter1:51

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

  • Python Random Numbers in Jupyter1:15

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

  • Adding R Scripting to Your Installation4:33

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

  • Basic R in Jupyter2:03

    Learn how the steps progress for an R script.

  • R Dataset Access and Visualization in Jupyter3:01

    The ability to use the Irisdataset to build R installations and the common use of R in several visualizations.

  • R Cluster Analysis and Forecasting3:11

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

  • Adding Julia Scripting to Your Installation3:21

    Learn to make separate steps for Julia scripting available in your Jupyter installation.

  • Basic Julia in Jupyter2:42

    Learn to use the Iris dataset for some standard analysis.

  • Julia Limitations and Standard Capabilities2:33

    Know about Julia’s limitations and the standard capabilities.

  • Julia Visualizations in Jupyter1:50

    Learn to use the plot function with standard defaults no type arguments to generate a Scatterplot.

  • Julia Vega Plotting and Parallel Processing2:34

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

  • Julia Control Flow, Regular Expressions, and Unit Testing4:33

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

  • Adding JavaScript Scripting to Your Installation2:29

    Learn to install JavaScript scripting on Mac.

  • JavaScript Hello World Jupyter Notebook2:14

    Learn the Hello world program using JavaScript in Jupyter notebook.

  • Basic JavaScript in Jupyter2:15

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

  • Node.js stats-analysis Package and JSON Handling2:24

    Node.js stats-analysis Package and JSON Handling

  • Node.js plotly Package1:50

    Learn to use all of the plotly features.

  • Node.js Asynchronous Threads1:32

    Ability to create threads using Node.js.

  • Node.js decision-tree Package2:45

    Know about decision tree package with an example of a machine learning package.

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.

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

Report abuse