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Jupyter Cookbook

  • Course Code: Data Science - Jupyter Cookbook
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Jupyter Cookbook skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle. 
  • 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. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

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

  • Create and share interactive documents with live code, text and visualizations 
  • Integrate popular programming languages such as Python, R, Julia, Scala with Jupyter 
  • Develop your widgets and interactive dashboards with these innovative recipes 

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

  • 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 with Jupyter’s next-gen user interface – JupyterLab 

Audience & Pre-Requisites 

This course is designed for developers wants to Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python and ML 
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Installation and Setting up the Environment 
  • Installation and Setting up the Environment 
  • Introduction 
  • Installing Jupyter on Windows 
  • Installing Jupyter on the Mac 
  • Installing Jupyter on Linux 
  • Installing Jupyter on a server 
  1. Adding an Engine 
  • Adding an Engine 
  • 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 
  1. Accessing and Retrieving Data 
  • Accessing and Retrieving Data 
  • Introduction 
  • Reading CSV files 
  • Reading JSON files 
  • Accessing a database 
  • Reading flat files 
  • Reading text files 
  1. Visualizing Your Analytics 
  • Visualizing Your Analytics 
  • 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 
  1. Working with Widgets 
  • Working with Widgets 
  • 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 
  1. Jupyter Dashboards 
  • Jupyter Dashboards 
  • Introduction 
  • What is Jupyter dashboards? 
  • Creating an R dashboard 
  • Create a Python dashboard 
  • Creating a Julia dashboard 
  • Develop a JavaScript (Node.js) dashboard 
  1. Sharing Your Code 
  • Sharing Your Code 
  • 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 
  1. Multiuser Jupyter 
  • Multiuser Jupyter 
  • 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 
  1. Interacting with Big Data 
  • Interacting with Big Data 
  • 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 
  1. Jupyter Security 
  • Jupyter Security 
  • Introduction 
  • Security mechanisms built into Jupyter 
  • Using SSL 
  • The Jupyter trust model 
  • Controlling network access 
  • Additional practices 
  1. Jupyter Labs 
  • Jupyter Labs 
  • Introduction 
  • Installing and starting JupyterLab 
  • JupyterLab display 
  • JupyterLab menus 
  • Starting a Notebook 
  • Starting a console 
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