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. Packts 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 NotebookAccess big data in JupyterLets 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. Youll learn to integrate the Jupyter system with different programming languages such as R, Python, JavaScript, and Julia. Youll 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, youll 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.
