10 Jupyter Notebook Frameworks In 10 Days

Learn about Jupyter Notebook and Jupyter Lab, Anaconda Cloud, Amazon Studio Lab and Google Colab, Kaggle and more

What you'll learn

History of notebook-based framework

Introduction to over a dozen of notebook-based frameworks

How to use intermediate and advanced Jupyter Notebook features

Traditional Jupyter Lite, Jupyter Notebook and JupyterLab user interfaces

Modern Jupyter-based frameworks like Hex, Datalore and Deepnote

Free GPU-based notebook-based cloud frameworks like Google Colab and Amazon Studio Lab

Bootcamp on the Markdown Language

Generate interactive dashboards and static reports from Jupyter notebooks

Perform data analysis and machine learning on Jupyter notebooks

Requirements

Basic programming in Python and SQL

Optional previous experience in data pipelines, data analysis or data science

No prior knowledge on any Jupyter-based product or framework is required

Description

This original high-quality hands-on course will help you understand the basics of experimenting with Jupyter notebooks. You'll learn about the history behind Jupyter Notebook, and all modern products today which are in fact based on this free and open-source project. I'll introduce you to at least 10 different applications, and help you move further, if you want to become indeed an expert in any of them.The 10 Jupyter-based FrameworksJupyter Notebook - the free open-source project based on IPython that started all.Project Jupyter - an ecosystem of other free open-source applications around Jupyter Notebook, including JupyterLab.Anaconda Cloud - a free cloud-based solution based on JupyterLab.Amazon Studio Lab - a free GPU-based cloud-hosted solution, as an alternative to the commercial but famous SageMaker.Google Colab - another practical alternative, with free GPU offerings, from Google.Kaggle - the one-stop social network for Data Science competitions.Hex - the most modern and classy web UI from all Jupyter-based products today.Deepnote - another interesting third-party hosted solution of no-code widgets in Jupyter notebooks.JetBrains Datalore - a practical notebook-based cloud environment from the company behind ReSharper and PyCharm.Snowflake Notebooks - when code must be executed closer to where your big data is stored.A last chapter will offer you a quick bootcamp in the Markdown language. And along the way you'll be exposed to the history behind Jupyter, as well as dozens of other notebook-based products that didn't make the cut.Who Am IExperienced Cloud Solutions Architect and Database expert.Over three decades of professional experience, as both a full-time employee and independent contractor.Snowflake world-class expert, former Snowflake "Data Superhero" and SnowPro Certification SME.I passed over 40 certification exams in 2-3 years alone, all from the first attempt.Over 20 certifications in AWS, Azure and GCP.Almost 20 certifications in Data Science and Machine Learning.Over a dozen of certifications in Data Analytics and Big Data.Learning Jupyter notebooks may seem easy. And you will need to learn about them, make no mistake. However, today it became truly difficult to keep up with all sorts of advanced and modern frameworks using notebooks. They come up with many data integrations, no-code widgets, application builders, artificial intelligence assistants and other advanced features.Allow me to help you out with this domain, to acquire basic and intermediate knowledge in this area in no time.

Overview

Section 1: Introduction

Lecture 1 Course Structure and Content

Lecture 2 How to Benefit Most from this Course

Lecture 3 Frequently Asked Questions

Lecture 4 History of the Notebook Interfaces

Section 2: Day 1: Jupyter Notebook

Lecture 5 Introduction to Jupyter Notebook

Lecture 6 Traditional Applications

Lecture 7 Jupyter Lite

Lecture 8 Basic Cell Editing

Lecture 9 Jupyter Notebook

Lecture 10 VSCode Notebooks

Lecture 11 GitHub Codespaces

Lecture 12 Magic Commands

Lecture 13 Pros and Cons of Jupyter Notebook

Section 3: Day 2: Project Jupyter

Lecture 14 Introduction to Project Jupyter

Lecture 15 Project Jupyter Overview

Lecture 16 JupyterLabs and Other UIs

Lecture 17 Jupyter Widgets and Other Controls

Lecture 18 Voila and Interactive Dashboards

Lecture 19 JupyterHub and Backed Subprojects

Lecture 20 Pros and Cons of Project Jupyter

Section 4: Day 3: Anaconda Cloud

Lecture 21 Introduction to Anaconda Cloud

Lecture 22 Anaconda Overview

Lecture 23 Conda Package Manager

Lecture 24 Create a Free Anaconda Cloud Account

Lecture 25 Code Debugging

Lecture 26 Pros and Cons of Anaconda

Section 5: Day 4: Amazon SageMaker Studio Lab

Lecture 27 Introduction to Amazon SageMaker Studio Lab

Lecture 28 Studio Lab Overview

Lecture 29 Create a Free Studio Lab Account

Lecture 30 Exploratory Data Analysis in Studio Lab

Lecture 31 Pros and Cons of Studio Lab

Section 6: Day 5: Google Colab

Lecture 32 Introduction to Google Colab

Lecture 33 Google Colab Overview

Lecture 34 Free Access to Google Colab

Lecture 35 Google Colab Features

Lecture 36 Widgets and Forms

Lecture 37 Input and Output

Lecture 38 TensorFlow Certification Exam Problem in Google Colab

Lecture 39 Colab XTerm Terminal

Lecture 40 Pros and Cons of Google Colab

Section 7: Day 6: Kaggle

Lecture 41 Introduction to Kaggle

Lecture 42 Kaggle Overview

Lecture 43 Create a Free Kaggle Account

Lecture 44 Exploratory Data Analysis in Kaggle Notebook

Lecture 45 Kaggle Competitions

Lecture 46 Pros and Cons of Kaggle

Section 8: Day 7: Hex

Lecture 47 Introduction to Hex

Lecture 48 Hex Overview

Lecture 49 Create a Free Trial Account for Hex

Lecture 50 App Builder with Hex

Lecture 51 Exploratory Data Analysis in Hex

Lecture 52 Anomaly Detection in Hex

Lecture 53 Pros and Cons of Hex

Section 9: Day 8: Deepnote

Lecture 54 Introduction to Deepnote

Lecture 55 Deepnote Overview

Lecture 56 Create a Free Deepnote Team Trial Account

Lecture 57 System Architecture of a Deepnote Notebook

Lecture 58 Deepnote Features

Lecture 59 Pros and Cons of Deepnote

Section 10: Day 9: JetBrains Datalore

Lecture 60 Introduction to JetBrains Datalore

Lecture 61 JetBrains Datalore Overview

Lecture 62 Create a Free Trial Account to JetBrains Datalore

Lecture 63 Datalore Report Builder

Lecture 64 Notebook Features in Datalore

Lecture 65 Pros and Cons of Datalore

Section 11: Day 10: Snowflake Notebooks

Lecture 66 Introduction to Snowflake Notebooks

Lecture 67 Snowflake Notebooks Overview

Lecture 68 Create a Free Snowflake Trial Account

Lecture 69 Getting Started with Snowflake Notebooks

Lecture 70 System Architecture of a Snowflake Notebook

Lecture 71 Snowflake Notebooks on Container Runtime

Lecture 72 Snowflake Notebook Features

Lecture 73 Pros and Cons of Snowflake Notebooks

Section 12: Markdown Language Bootcamp

Lecture 74 Introduction to Markdown Language Bootcamp

Lecture 75 Markdown Language Overview

Lecture 76 Markdown Inline Styles

Lecture 77 Markdown Block Styles

Lecture 78 Markdown Heading Styles

Section 13: Wrapping Up

Lecture 79 Other Notebook Frameworks

Lecture 80 Congratulations, You Made It!

Lecture 81 Bonus Lecture

Data Scientists in need of a better environment for their experiments,Data Analysts who want to learn or improve their knowledge of notebooks,Programmers and Software Engineers willing to learn about a different way of building apps,Data Engineers willing to explore how to build data pipelines using notebooks,Any technical and non-technical person with the desire to learn about Jupyter notebooks,Anyone willing to explore the new modern products today based on Jupyter notebooks