Book details

  • Format: epub
  • File Size: 7 MB
  • Print Length: 238 pages
  • Publisher: Packt Publishing; 1 edition (30 April 2018)
  • Sold by: Amazon Asia-Pacific Holdings Private Limited
  • Language: English

Who This Book Is For
This cookbook is for data science professionals, developers, technical data analysts, and programmers who want to execute technical coding, visualize output, and do scientific computing in one tool. Prior understanding of data science concepts will be helpful, but not mandatory, to use this book.

Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle

Key Features
Create and share interactive documents with live code, textandvisualizations
Integrate popular programming languages such as Python, R, Julia, Scala with Jupyter
Develop your widgets and interactive dashboards with these innovative recipes
Book Description
Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications.

The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web.

By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.

What you will learn

  • Install Jupyter and configure engines for Python, R, Scala and more
  • Access and retrieve data on Jupyter Notebooks
  • Create interactive visualizations and dashboards for different scenarios
  • Convert and share your dynamic codes using HTML, JavaScript, Docker, and more
  • Create custom user data interactions using various Jupyter widgets
  • Manage user authentication and file permissions
  • Interact with Big Data to perform numerical computing and statistical modeling
  • Get familiar withJupyter’snext-gen user interface – JupyterLab

Table of Contents

  • Installation & Setting up the Environment
  • Adding an Engine
  • Accessing and Retrieving Data
  • Visualize your analytics
  • Working with Widgets
  • Jupyter dashboards
  • Sharing your code
  • Multiuser Jupyter
  • Interacting with Big Data
  • Jupyter Security
  • Jupyter Labs





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