Using Jupyter Notebooks for Data Science Analysis in Python

Using Jupyter Notebooks for Data Science Analysis in Python
Using Jupyter Notebooks for Data Science Analysis in Python

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 10m | 829 MB
eLearning | Skill level: All Levels


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.

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
+ Table of Contents

01 Learning objectives
02 1.1 What are Project Jupyter and the Jupyter Notebook
03 1.2 How Jupyter facilitates collaboration and sharing in data science
04 1.3 Differentiate between the Jupyter Notebook and other Jupyter projects
05 1.4 Find resources and connect with the Jupyter community through Jupyter.org
06 1.5 Learn through example using the Gallery of Interesting Jupyter Notebooks and GitHub
07 1.6 Contribute to the Jupyter ecosystem via GitHub
08 1.7 Participate in open source computing through NumFOCUS
09 Learning objectives
10 2.1 Determine which Python version to install
11 2.2 Install Jupyter using the Anaconda distribution of Python
12 2.3 Start your Jupyter Notebook using the command-line interface (CLI)
13 2.4 Start your Jupyter Notebook using the Anaconda Navigator
14 2.5 Run an ephemeral Interactive Jupyter Notebook on the web
15 2.6 Run Jupyter Notebooks in the cloud using Azure Notebooks
16 2.7 Run Jupyter Notebooks using Nteract
17 2.8 Navigate the Jupyter Notebook environment
18 2.9 Maintain good notebook hygiene
19 2.10 Perform quantitative exploratory data analysis (EDA) in your Jupyter Notebook using Python
20 2.11 Perform Visual Exploratory data analysis (EDA) in your Jupyter Notebook using Python
21 2.12 Create Jupyter Notebooks with different kernels (including R)
22 2.13 Install the R kernel
23 Learning objectives
24 3.1 Work with .ipynb files
25 3.2 Install nbconvert
26 3.3 Convert your Jupyter Notebook to different formats – HTML, PDF, and .py
27 3.4 Create dynamic presentation slides from your Jupyter Notebook using RISE
28 3.5 Share Jupyter Notebooks using GitHub and nbviewer
29 3.6 Access Jupyter Notebooks using Azure Notebooks
30 3.7 Compare and merge Jupyter Notebooks with nbdime
31 Learning objectives
32 4.1 Understand the basics of JupyterHub
33 4.2 Install and explore JupyterLab
34 4.3 Work with others using Real Time Collaboration
35 4.4 Enhance your analysis with interactive Jupyter Widgets
36 4.5 Share custom environments with Binder and BinderHub


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