Using Jupyter Notebooks for Data Science Analysis in Python LiveLessons

Video description

2+ Hours of Video Instruction

Create an end-to-end data analysis workflow in Python using the Jupyter Notebook and learn about the diverse and abundant tools available within the Project Jupyter ecosystem.


The Jupyter Notebook is a popular tool for learning and performing data science in Python (and other languages used in data science). This video tutorial will teach you about Project Jupyter and the Jupyter ecosystem and get you up and running in the Jupyter Notebook environment. Together, we’ll build a data product in Python, and you’ll learn how to share this analysis in multiple formats, including presentation slides, web documents, and hosted platforms (great for colleagues who do not have Jupyter installed on their machines). In addition to learning and doing Python in Jupyter, you will also learn how to install and use other programming languages, such as R and Julia, in your Jupyter Notebook analysis.

Skill Level

  • Beginner
  • Intermediate

Learn How To
  • Create a start-to-finish Jupyter Notebook workflow: from installing Jupyter to creating your data analysis and ultimately sharing your results
  • Use additional tools within the Jupyter ecosystem that facilitate collaboration and sharing
  • Incorporate other programming languages (such as R) in Jupyter Notebook analyses

Who Should Take This Course
  • Users new to Jupyter Notebooks who want to use the full range of tools within the Jupyter ecosystem
  • Data practitioners who want a repeatable process for conducting, sharing, and presenting data science projects
  • Data practitioners who want to share data science analyses with friends and colleagues who do not use or do not have access to a Jupyter installation

Course Requirements
  • Basic knowledge of Python.
  • Download and install the Anaconda distribution of Python here. You can install either version 2.7 or 3.x, whichever you prefer.
  • Create a GitHub account here (strongly recommended but not required).
  • If you are unable to install software on your computer, you can access a hosted version via the Project Jupyter website (click on “try it in your browser”) or through Microsoft’s Azure Notebooks.

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Table of contents

  1. Introduction
    1. Using Jupyter Notebooks for Data Science Analysis in Python LiveLessons: Introduction 00:03:53
  2. Lesson 1: Project Jupyter and the Jupyter Ecosystem
    1. Learning objectives 00:00:59
    2. 1.1 What are Project Jupyter and the Jupyter Notebook? 00:03:59
    3. 1.2 How Jupyter facilitates collaboration and sharing in data science 00:02:02
    4. 1.3 Differentiate between the Jupyter Notebook and other Jupyter projects 00:00:59
    5. 1.4 Find resources and connect with the Jupyter community through 00:04:01
    6. 1.5 Learn through example using the Gallery of Interesting Jupyter Notebooks and GitHub 00:02:02
    7. 1.6 Contribute to the Jupyter ecosystem via GitHub 00:00:52
    8. 1.7 Participate in open source computing through NumFOCUS 00:00:40
  3. Lesson 2: Creating Data Science Analyses in the Jupyter Notebook
    1. Learning objectives 00:01:33
    2. 2.1 Determine which Python version to install 00:00:58
    3. 2.2 Install Jupyter using the Anaconda distribution of Python 00:01:12
    4. 2.3 Start your Jupyter Notebook using the command-line interface (CLI) 00:02:31
    5. 2.4 Start your Jupyter Notebook using the Anaconda Navigator 00:00:43
    6. 2.5 Run an ephemeral Interactive Jupyter Notebook on the web 00:01:12
    7. 2.6 Run Jupyter Notebooks in the cloud using Azure Notebooks 00:01:57
    8. 2.7 Run Jupyter Notebooks using Nteract 00:01:20
    9. 2.8 Navigate the Jupyter Notebook environment 00:06:45
    10. 2.9 Maintain good notebook hygiene 00:01:18
    11. 2.10 Perform quantitative exploratory data analysis (EDA) in your Jupyter Notebook using Python 00:22:10
    12. 2.11 Perform Visual Exploratory data analysis (EDA) in your Jupyter Notebook using Python 00:08:08
    13. 2.12 Create Jupyter Notebooks with different kernels (including R) 00:01:15
    14. 2.13 Install the R kernel 00:02:42
  4. Lesson 3: Sharing Jupyter Notebooks
    1. Learning objectives 00:00:44
    2. 3.1 Work with .ipynb files 00:02:23
    3. 3.2 Install nbconvert 00:01:19
    4. 3.3 Convert your Jupyter Notebook to different formats: HTML, PDF, and .py 00:02:42
    5. 3.4 Create dynamic presentation slides from your Jupyter Notebook using RISE 00:03:37
    6. 3.5 Share Jupyter Notebooks using GitHub and nbviewer 00:02:20
    7. 3.6 Access Jupyter Notebooks using Azure Notebooks 00:00:54
    8. 3.7 Compare and merge Jupyter Notebooks with nbdime 00:02:02
  5. Lesson 4: Exploring New Jupyter Projects In-Depth
    1. Learning objectives 00:00:46
    2. 4.1 Understand the basics of JupyterHub 00:21:51
    3. 4.2 Install and explore JupyterLab 00:11:33
    4. 4.3 Work with others using Real Time Collaboration 00:01:31
    5. 4.4 Enhance your analysis with interactive Jupyter Widgets 00:03:47
    6. 4.5 Share custom environments with Binder and BinderHub 00:04:49
  6. Summary
    1. Summary 00:01:47

Product information

  • Title: Using Jupyter Notebooks for Data Science Analysis in Python LiveLessons
  • Author(s): Jamie Whitacre
  • Release date: June 2018
  • Publisher(s): Addison-Wesley Professional
  • ISBN: 0135174295