O'Reilly - Jupyter Notebook for Data Science Teamsseeders: 1
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O'Reilly - Jupyter Notebook for Data Science Teams (Size: 800.26 MB)
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. 01. Introduction 0101 Introduction And Course Overview 0102 About The Author 0103 How To Access Your Working Files 02. Setting Up Environment 0201 Installing The Jupyter Notebook And Setup 0202 Setting Up Git And GitHub Account 03. Jupyter Notebook Features 0301 Standard Browser Use 0302 Installing Notebook Extensions 0303 More On Notebook Extensions 0304 SQL Magic And Pandas 0305 Conda Environments 0306 R In Jupyter Notebook 0307 Autocreate Documents In HTML Or PDF 0308 Interactive Widgets 0309 Bleeding Edge - JupyterHub 04. Sharing Notebooks With A Team 0401 Organizing A Workflow 0402 Lab Vs. Deliverable Notebook 0403 Directory Structure And Naming Conventions 0404 Version Control 05. Project - Data Science With The Notebook End-To-End Example 0501 Get Data 0502 Load The Data 0503 Initial Data Cleaning 0504 Creating A New Github Repository 0505 Version Control 0506 Exploratory Data Analysis - Regression Plotting 0507 Exploratory Data Analysis - Variable Transformations 0508 Git Branch Store Data Cleaned Pipeline 0509 Feature Engineering 0510 Random Forest Prediction And Evaluation 0511 Final Analysis Cleanup 0512 Pull Request, Peer Review, And Merge With Master 06. Project - Data Science: Statistics And Data Visualizations 0601 Initial Data Visualization 0602 Advanced Pandas Plotting 0603 Advanced Seaborn Plotting 0604 Statsmodels Analysis - Part 1 0605 Statsmodels Analysis - Part 2 07. Conclusion 0701 Resources And Where To Go From Here Sharing Widget |