Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.

Learning Jupyter 5

  • Course Code: Data Analysis / BI - Learning Jupyter 5
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with Jupyter.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Jupyter skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with Jupyter.  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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 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. 

Working in a hands-on learning environment, led by our Jupyter expert instructor, students will learn about and explore: 

  • Learn how to use Jupyter 5.x features such as cell tagging and attractive table styles 
  • Leverage big data tools and datasets with different Python packages 
  • Explore multiple-user Jupyter Notebook servers 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Install and run the Jupyter Notebook system on your machine 
  • Implement programming languages such as R, Python, Julia, and JavaScript with the Jupyter Notebook 
  • Use interactive widgets to manipulate and visualize data in real time 
  • Start sharing your Notebook with colleagues 
  • Invite your colleagues to work with you on the same Notebook 
  • Organize your Notebook using Jupyter namespaces 
  • Access big data in Jupyter for dealing with large datasets using Spark  

Audience & Pre-Requisites 

This course is geared for attendees 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 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python.  

Course Agenda / Topics 

  1. Introduction to Jupyter 
  • Introduction to Jupyter 
  • First look at Jupyter 
  • Installing Jupyter 
  • Notebook structure 
  • Notebook workflow 
  • Basic Notebook operations 
  • Security in Jupyter 
  • Configuration options for Jupyter 
  1. Jupyter Python Scripting 
  • Jupyter Python Scripting 
  • Basic Python in Jupyter 
  • Python data access in Jupyter 
  • Python pandas in Jupyter 
  • Python graphics in Jupyter 
  • Python random numbers in Jupyter 
  1. Jupyter R Scripting 
  • Jupyter R Scripting 
  • 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 
  1. Jupyter Julia Scripting 
  • Jupyter Julia Scripting 
  • 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 
  1. Jupyter Java Coding 
  • Jupyter Java Coding 
  • 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 
  1. Jupyter JavaScript Coding 
  • Jupyter JavaScript Coding 
  • 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 
  1. Jupyter Scala 
  • Jupyter Scala 
  • 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 
  1. Jupyter and Big Data 
  • Jupyter and Big Data 
  • 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 
  1. Interactive Widgets 
  • Interactive Widgets 
  • Installing widgets 
  • Widget basics 
  • Interact widget 
  • Interactive widget 
  • Widgets 
  1. Sharing and Converting Jupyter Notebooks 
  • Sharing and Converting Jupyter Notebooks 
  • Sharing Notebooks 
  • Converting Notebooks 
  1. Multiuser Jupyter Notebooks 
  • Multiuser Jupyter Notebooks 
  • A sample interactive Notebook 
  • JupyterHub 
  • Docker 
  1. What’s Next? 
  • What’s Next? 
  • JupyterHub 
  • JupyterLab 
  • Scale 
  • Custom frontends 
  • Interactive computing standards 
View All Courses

    Course Inquiry

    Fill in the details below and we will get back to you as quickly as we can.

    Interested in any of these related courses?