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Home » Technology » Big Data & Data Science » Learning Jupyter 5

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    LJUPY5L21E09

Who should attend & recommended skills:

Developers, analysts with basic Python skills

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who wish to create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with Jupyter.
  • Skill-level: Foundation-level Jupyter skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The course ok starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this course, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the course. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Jupyter expert instructor, students will learn about and explore:
  • How to use Jupyter 5.x features such as cell tagging and attractive table styles
  • Leveraging big data tools and datasets with different Python packages
  • Exploring multiple-user Jupyter Notebook servers
  • Installing and running the Jupyter Notebook system on your machine
  • Implementing programming languages such as R, Python, Julia, and JavaScript with the Jupyter Notebook
  • Using interactive widgets to manipulate and visualize data in real time
  • Sharing your Notebook with colleagues
  • Inviting your colleagues to work with you on the same Notebook
  • Organizing your Notebook using Jupyter namespaces
  • Accessing big data in Jupyter for dealing with large datasets using Spark

Course breakdown / modules

  • First look at Jupyter
  • Installing Jupyter
  • Notebook structure
  • Notebook workflow
  • Basic Notebook operations
  • Security in Jupyter
  • Configuration options for Jupyter

  • Basic Python in Jupyter
  • Python data access in Jupyter
  • Python pandas in Jupyter
  • Python graphics in Jupyter
  • Python random numbers in Jupyter

  • Adding R scripting to your installation
  • Basic R in Jupyter
  • R dataset access
  • R visualizations in Jupyter
  • R cluster analysis
  • R forecasting
  • R machine learning

  • Adding Julia scripting to your installation
  • Basic Julia in Jupyter
  • Julia limitations in Jupyter
  • Standard Julia capabilities
  • Julia visualizations in Jupyter
  • Julia Vega plotting
  • Julia parallel processing
  • Julia control flow
  • Julia regular expressions
  • Julia unit testing

  • Adding the Java kernel to your installation
  • Jupyter Java console
  • Jupyter Java output
  • Java Optional
  • Java compiler errors
  • Java lambdas
  • Java Collections
  • Java summary statistics

  • Adding JavaScript scripting to your installation
  • JavaScript Hello World Jupyter Notebook
  • Basic JavaScript in Jupyter
  • JavaScript limitations in Jupyter
  • Node.js d3 package
  • Node.js stats-analysis package
  • Node.js JSON handling
  • Node.js canvas package
  • Node.js plotly package
  • Node.js asynchronous threads
  • Node.js decision-tree package

  • Installing the Scala kernel
  • Scala data access in Jupyter
  • Scala array operations
  • Scala random numbers in Jupyter
  • Scala closures
  • Scala higher-order functions
  • Scala pattern matching
  • Scala case classes
  • Scala immutability
  • Scala collections
  • Named arguments
  • Scala traits

  • Apache Spark
  • First Spark script
  • Spark word count
  • Sorted word count
  • Estimate pi
  • Log file examination
  • Spark primes
  • Spark text file analysis
  • Spark evaluating history data

  • Installing widgets
  • Widget basics
  • Interact widget
  • Interactive widget
  • Widgets

  • Sharing Notebooks
  • Converting Notebooks

  • Multiuser Jupyter Notebooks
  • A sample interactive Notebook
  • JupyterHub
  • Docker

  • JupyterHub
  • JupyterLab
  • Scale
  • Custom frontends
  • Interactive computing standards

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