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.
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.
Know about Jupyter IDE.
The ability to install Jupyter on Windows.
The ability to install Jupyter on Mac.
Learn about the Jupyter notebook structure and the workflow of Jupyter with some basic operations.
Learn to execute arbitrary code.
Learn 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.
Learn to plot the data from the number of births in a year.
Learn to simulate rolling a pair of dice and looking at the outcome.
The ability to make R scripting available in your Jupyter installation.
Learn how the steps progress for an R script.
The ability to use the Irisdataset to build R installations and the common use of R in several visualizations.
The ability to use R's cluster analysis functions to determine the clustering in thedataset.
Learn to make separate steps for Julia scripting available in your Jupyter installation.
Learn to use the Iris dataset for some standard analysis.
Know about Julia’s limitations and the standard capabilities.
Learn to use the plot function with standard defaults no type arguments to generate a Scatterplot.
Ability to use Vega for a pie chart and to produce an interesting visualization.
Learn about the small function that determines the larger of two numbers.
Node.js stats-analysis Package and JSON Handling
Learn to use all of the plotly features.
Ability to create threads using Node.js.
Know about decision tree package with an example of a machine learning package.
Learn how to install widgets and learn about the basics of widgets
Learn howthe interact widget can affect many different variations of user input control.
Learn to know where the parameters of the widget display need a control at run time.
Know how to customize the display.
Learn to have a set of properties to adjust for your display.
Know how to share notebooks by using HTML and server interaction.
The ability to replace yourwebsitewith the URL of the website where you can access the notebook.
Learn to use R programming inyour notebook and to install the R tool set on your machine.
Learn how to convert notebooks to other formats.
Learn to use a simple notebook that asks the user for some information and displays other information.
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.
Learn to know about Docker and its mechanism that can be used to allow multiple users of the same notebook without collision.
Learn how to build Jupyter image for Docker.
Learn to access data and perform some simpler statistics.
The ability to make the calculations in Scala and parse out the CSV file.
Learn to pull data from the Scala random library and present it in histogram for illustrative purposes.
Define a multiplier function and learn how to take other functions as arguments or returns a function as its result.
Learn about Scala pattern matching using Jupyter and Scala case classes.
Know how to mutable the variables
Learn to collect mutable and immutable usage of Scala collections and its arguments.
Define a set of features that can be implemented by classes.
Initialize spark; it takes every line and computes the length of the prefix statement.
Learn how to use map to estimate the Pi and will learn about the log file examination.
Know about spark primes & Spark test file analysis to run a series of numbers through a filter.
Know about some historical data and determine some useful attributes.
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