Jupyter Notebook for Data Science Teams Training Video

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AU$ 189 - (132 )

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A Practical Training Course That Teaches Real World Skills In this project-based Jupyter Notebook for Data Science Teams video tutorial series, you'll quickly have relevant skills for real-world applications. Follow along with our expert instructor in this training course to get: Concise, informative and broadcast-quality Jupyter Notebook for Data Science Teams training videos delivered to your desktop The ability to learn at your own pace with our intuitive, easy-to-use interface A quick grasp of even the most complex Jupyter Notebook for Data Science Teams subjects because they're broken into simple, easy to follow tutorial videos Practical working files further enhance the learning process and provide a degree of retention that is unmatched by any other form of Jupyter Notebook for Data Science Teams tutorial, online or offline...so you'll know the exact steps for your own projects. Course Fast Facts: Only 3 hours to complete this course 36 tutorial videos Expert instructors lead each course Download to any Windows PC or Mac and save for viewing off line Course is accessible 24/7 from any computer once downloaded You can study from home or at work at your own pace in your own time Course 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...

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  • 01. Introduction
    • Introduction And Course Overview
    • About The Author
    • 0103 How To Access Your Working Files
  • 02. Setting Up Environment
    • Installing The Jupyter Notebook And Setup
    • 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

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