2016
3 hours
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
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