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Jupyter Notebook for Data Science Teams

Video Description

In this Jupyter Notebook for Data Science Teams training course, expert author Jonathan Whitmore will teach you about Jupyter Notebook extensions, widgets, and team sharing. This course is designed for data scientists who need to collaborate on projects.

You will start by learning how to install and set up the Jupyter Notebook, as well as how to set up Git and GitHub accounts. From there, Jonathan will teach you about Jupyter Notebook features, including extensions, SQL Magic and Pandas, and interactive widgets. This video tutorial also covers how to share notebooks with a team. Finally, you will run through an example of using a single Git repository for a team data science project from start to finish.

Once you have completed this computer based training course, you will have learned how to use Jupyter Notebook for data science teams.

Table of Contents

  1. Introduction
    1. Introduction And Course Overview 00:02:10
    2. About The Author 00:01:20
  2. Setting Up Environment
    1. Installing The Jupyter Notebook And Setup 00:05:33
    2. Setting Up Git And GitHub Account 00:02:38
  3. Jupyter Notebook Features
    1. Standard Browser Use 00:04:24
    2. Installing Notebook Extensions 00:05:53
    3. More On Notebook Extensions 00:04:18
    4. SQL Magic And Pandas 00:05:18
    5. Conda Environments 00:06:13
    6. R In Jupyter Notebook 00:07:00
    7. Autocreate Documents In HTML Or PDF 00:06:49
    8. Interactive Widgets 00:05:40
    9. Bleeding Edge - JupyterHub 00:04:22
  4. Sharing Notebooks With A Team
    1. Organizing A Workflow 00:03:05
    2. Lab Vs. Deliverable Notebook 00:04:54
    3. Directory Structure And Naming Conventions 00:01:44
    4. Version Control 00:06:15
  5. Project - Data Science With The Notebook End-To-End Example
    1. Get Data 00:00:58
    2. Load The Data 00:03:52
    3. Initial Data Cleaning 00:03:27
    4. Creating A New Github Repository 00:01:26
    5. Version Control 00:04:38
    6. Exploratory Data Analysis - Regression Plotting 00:06:51
    7. Exploratory Data Analysis - Variable Transformations 00:04:58
    8. Git Branch Store Data Cleaned Pipeline 00:05:45
    9. Feature Engineering 00:07:39
    10. Random Forest Prediction And Evaluation 00:10:19
    11. Final Analysis Cleanup 00:13:01
    12. Pull Request, Peer Review, And Merge With Master 00:07:44
  6. Project - Data Science: Statistics And Data Visualizations
    1. Initial Data Visualization 00:06:31
    2. Advanced Pandas Plotting 00:07:46
    3. Advanced Seaborn Plotting 00:09:46
    4. Statsmodels Analysis - Part 1 00:09:27
    5. Statsmodels Analysis - Part 2 00:08:20
  7. Conclusion
    1. Resources And Where To Go From Here 00:03:05