10 Jupyter Notebook Frameworks In 10 Days
Published 3/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.55 GB | Duration: 10h 9m
Published 3/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.55 GB | Duration: 10h 9m
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