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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers with basic Python experience

Who should attend & recommended skills

  • This course is designed for developers who want to leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle.
  • Skill-level: Foundation-level Jupyter Cookbook skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

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 course is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The course 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 course 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 course, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Jupyter Cookbook expert instructor, students will learn about and explore:
  • Creating and sharing interactive documents with live code, text and visualizations
  • Integrating popular programming languages such as Python, R, Julia, Scala with Jupyter
  • Developing your widgets and interactive dashboards with these innovative recipes
  • Installing Jupyter and configure engines for Python, R, Scala and more
  • Accessing and retrieving data on Jupyter Notebooks
  • Creating interactive visualizations and dashboards for different scenarios
  • Converting and sharing your dynamic codes using HTML, JavaScript, Docker, and more
  • Creating custom user data interactions using various Jupyter widgets
  • Managing user authentication and file permissions
  • Interacting with Big Data to perform numerical computing and statistical modeling
  • Getting familiar with Jupyter’s next-gen user interface – JupyterLab

Course breakdown / modules

  • Introduction
  • Installing Jupyter on Windows
  • Installing Jupyter on the Mac
  • Installing Jupyter on Linux
  • Installing Jupyter on a server

  • Introduction
  • Adding the Python 3 engine
  • Adding the R engine
  • Adding the Julia engine
  • Adding the JavaScript engine
  • Adding the Scala engine
  • Adding the Spark engine

  • Introduction
  • Reading CSV files
  • Reading JSON files
  • Accessing a database
  • Reading flat files
  • Reading text files

  • Introduction
  • Generating a line graph using Python
  • Generating a histogram using Python
  • Generating a density map using Python
  • Plotting 3D data using Python
  • Present a user-interactive graphic using Python
  • Visualizing with R
  • Generate a regression line of data using R
  • Generate an R lowess line graph
  • Producing a Scatter plot matrix using R
  • Producing a bar chart using R
  • Producing a word cloud using R
  • Visualizing with Julia
  • Drawing a Julia scatter diagram of Iris data using Gadfly
  • Drawing a Julia histogram using Gadfly
  • Drawing a Julia line graph using the Winston package

  • Introduction
  • What are widgets?
  • Using ipyleaflet widgets
  • Using ipywidgets
  • Using a widget container
  • Using an interactive widget
  • Using an interactive text widget
  • Linking widgets together
  • Another ipywidgets linking example
  • Using a cookie cutter widget
  • Developing an OPENGL widget
  • Creating a simple orbit of one object
  • Using a complex orbit of multiple objects

  • Introduction
  • What is Jupyter dashboards?
  • Creating an R dashboard
  • Create a Python dashboard
  • Creating a Julia dashboard
  • Develop a JavaScript (Node.js) dashboard

  • Introduction
  • Using a Notebook server
  • Using a web server
  • Sharing your Notebook through a public server
  • Sharing your Notebook through Docker
  • Sharing your Notebook using nbviewer
  • Converting your Notebook into a different format
  • Converting Notebooks to R
  • Converting Notebooks to HTML
  • Converting Notebooks to Markdown
  • Converting Notebooks to reStructedText
  • Converting Notebooks to Latex
  • Converting Notebooks to PDF

  • Introduction
  • Why multiuser?
  • Providing multiuser with JupyterHub
  • Providing multiuser with Docker
  • Running your Notebook in Google Cloud Platform
  • Running your Notebook in AWS
  • Running your Notebook in Azure

  • Introduction
  • Obtaining a word count from a big-text data source
  • Obtaining a sorted word count from a big-text source
  • Examining big-text log file access
  • Computing prime numbers using parallel operations
  • Analyzing big-text data
  • Analyzing big data history files

  • Introduction
  • Security mechanisms built into Jupyter
  • Using SSL
  • The Jupyter trust model
  • Controlling network access
  • Additional practices

  • Introduction
  • Installing and starting JupyterLab
  • JupyterLab display
  • JupyterLab menus
  • Starting a Notebook
  • Starting a console