Using Jupyter Notebooks for Data Science Analysis in Python

Pearson presents Using Jupyter Notebooks for Data Science Analysis in Python.

Using Jupyter Notebooks for Data Science Analysis in Python

Course Description

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 with this course from Pearson. 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 u... Read More »

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 with this course from Pearson.

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.

What you will learn:

  • 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
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Course Details:

Target Audience

  • 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.

Prerequisites

  • 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.
  • 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.
  • Create a Github account (strongly recommended but not required.)
Certificate Info:

Type of Certification

Certificate of Completion

Format of Certification

Digital and Print

Professional Association/Affiliation

This certificate is issued by Pearson LearnIT

Method of Obtaining Certification

Upon successful completion of the course, participants will receive a certificate of completion.

Course Outline

  • Using Using Jupyter Notebooks for Data Science Analysis in Python LiveLessons: Introduction
  • Learning objectives
  • 1.1 What are Project Jupyter and the Jupyter Notebook?
  • 1.2 How Jupyter facilitates collaboration and sharing in data science
  • 1.3 Differentiate between the Jupyter Notebook and other Jupyter projects
  • 1.4 Find resources and connect with the Jupyter community through Jupyter.org
  • 1.5 Learn through example using the Gallery of Interesting Jupyter Notebooks and GitHub
  • 1.6 Contribute to the Jupyter ecosystem via GitHub
  • 1.7 Participate in open source computing through NumFOCUS
  • Learning objectives
  • 2.1 Determine which Python version to install
  • 2.2 Install Jupyter using the Anaconda distribution of Python
  • 2.3 Start your Jupyter Notebook using the command-line interface (CLI)
  • 2.4 Start your Jupyter Notebook using the Anaconda Navigator
  • 2.5 Run an ephemeral Interactive Jupyter Notebook on the web
  • 2.6 Run Jupyter Notebooks in the cloud using Azure Notebooks
  • 2.7 Run Jupyter Notebooks using Nteract
  • 2.8 Navigate the Jupyter Notebook environment
  • 2.9 Maintain good notebook hygiene
  • 2.10 Perform quantitative exploratory data analysis (EDA) in your Jupyter Notebook using Python
  • 2.11 Perform Visual Exploratory data analysis (EDA) in your Jupyter Notebook using Python
  • 2.12 Create Jupyter Notebooks with different kernels (including R)
  • 2.13 Install the R kernel
  • Learning objectives
  • 3.1 Work with .ipynb files
  • 3.2 Install nbconvert
  • 3.3 Convert your Jupyter Notebook to different formats: HTML, PDF, and .py
  • 3.4 Create dynamic presentation slides from your Jupyter Notebook using RISE
  • 3.5 Share Jupyter Notebooks using GitHub and nbviewer
  • 3.6 Access Jupyter Notebooks using Azure Notebooks
  • 3.7 Compare and merge Jupyter Notebooks with nbdime
  • Learning objectives
  • 4.1 Understand the basics of JupyterHub
  • 4.2 Install and explore JupyterLab
  • 4.3 Work with others using Real Time Collaboration
  • 4.4 Enhance your analysis with interactive Jupyter Widgets
  • 4.5 Share custom environments with Binder and BinderHub
  • Summary

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