Learning Path: Jupyter: Interactive Computing with Jupyter
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Learning Path: Jupyter: Interactive Computing with Jupyter

More than 50 videos to help you get started with the Jupyter Notebook
3.8 (4 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
43 students enrolled
Created by Packt Publishing
Last updated 4/2017
English
Current price: $10 Original price: $200 Discount: 95% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 2.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
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 is the target audience?
  • 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
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Curriculum For This Course
57 Lectures
02:37:14
+
Jupyter Notebook for All – Part I
28 Lectures 01:23:23

This video gives an overview of the entire course.

Preview 03:41

Know about Jupyter IDE.

First Look at Jupyter
04:38

The ability to install Jupyter on Windows.

Installing Jupyter on Windows
02:56

The ability to install Jupyter on Mac.

Installing Jupyter on Mac
00:46

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

Notebook Structure, Workflow, andBasic Operations
10:52

Learn to execute arbitrary code.

Security and Configuration Operations in Jupyter
03:28

Learn to use Python scripts in a Jupyter Notebook.

Preview 04:12

Learn Python data access in Jupyter.

Python Data Access in Jupyter
02:10

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
01:44

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

Python Graphics in Jupyter
01:51

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

Python Random Numbers in Jupyter
01:15

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

Preview 04:33

Learn how the steps progress for an R script.

Basic R in Jupyter
02:03

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
03:01

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

R Cluster Analysis and Forecasting
03:11

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

Preview 03:21

Learn to use the Iris dataset for some standard analysis.

Basic Julia in Jupyter
02:42

Know about Julia’s limitations and the standard capabilities.

Julia Limitations and Standard Capabilities
02:33

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

Julia Visualizations in Jupyter
01:50

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

Julia Vega Plotting and Parallel Processing
02:34

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

Julia Control Flow, Regular Expressions, and Unit Testing
04:33

Learn to install JavaScript scripting on Mac.

Preview 02:29

Learn the Hello world program using JavaScript in Jupyter notebook.

JavaScript Hello World Jupyter Notebook
02:14

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

Basic JavaScript in Jupyter
02:15

Node.js stats-analysis Package and JSON Handling

Node.js stats-analysis Package and JSON Handling
02:24

Learn to use all of the plotly features.

Node.js plotly Package
01:50

Ability to create threads using Node.js.

Node.js Asynchronous Threads
01:32

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

Node.js decision-tree Package
02:45
+
Jupyter Notebook for All – Part II
29 Lectures 01:13:51

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
02:51

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

Interact Widget
03:05

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

Interactive Widget
00:58

Know how to customize the display.

Widgets
03:38

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

Widget Properties
04:46

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

Preview 05:39

The ability to replace yourwebsitewith the URL of the website where you can access the notebook.

Sharing Notebooks on a Web Server and Docker
02:02

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

Sharing Notebooks on a Public Server
01:38

Learn how to convert notebooks to other formats.

Converting Notebooks
05:50

Learn to use a simple notebook that asks the user for some information and displays other information.

Preview 01:52

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

JupyterHub
01:46

Know about Jupyter Hub operations and its functions.

JupyterHub – Operation
04:52

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
02:14

Learn how to build Jupyter image for Docker.

Building Your JupyterImage for Docker
03:08

Build the Scala package to launch the Scala shell.

Preview 02:31

Learn to access data and perform some simpler statistics.

Scala Data Access in Jupyter
00:56

The ability to make the calculations in Scala and parse out the CSV file.

Scala Array Operations
00:52

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

Scala Random Numbers in Jupyter
01:12

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

Scala Closures andHigher Order Definitions
01:22

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

Scala Pattern Matching andCase Classes
01:58

Know how to mutable the variables

Scala Immutability
01:02

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

Scala Collections and Named Arguments
01:15

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

Scala Traits
01:32

Learn how to install spark in Mac and Windows.

Preview 03:01

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

Our First Spark Script and Word Count
03:31

Learn how to use map to estimate the Pi and will learn about the log file examination.

Estimate Pi andLog File Examination
02:15

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

Spark Primes andText File Analysis
01:30

Know about some historical data and determine some useful attributes.

Spark – Evaluating History Data
02:48
About the Instructor
Packt Publishing
3.9 Average rating
7,219 Reviews
51,681 Students
616 Courses
Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.