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# Quick utility to embed the videos below
from IPython.display import YouTubeVideo
def embed_video(index, playlist='PLeFIaIQF2TkB04NMOWoj3vyBa58LdoRLe'):
    return YouTubeVideo('', index=index - 1, list=playlist, width=600, height=350)

Part 1: Loading and Visualizing Data¶

In this video, I introduce the dataset, and use the Jupyter notebook to download and visualize it.

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embed_video(1)

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Relevant resources:

Fremont Bridge Bike Counter: the website where you can explore the data
A Whirlwind Tour of Python: a book introducing the Python programming language, aimed at scientists and engineers.
Python Data Science Handbook: a book introducing Python's data science tools, including an introduction to the IPython, Pandas, and Matplotlib tools used here.

Part 2: Further Data Exploration¶

In this video, I do some slightly more sophisticated visualization with the data, using matplotlib and pandas.

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embed_video(2)

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Relevant Resources:

Pivot Tables Section from the Python Data Science Handbook

Part 3: Version Control with Git & GitHub¶

In this video, I set up a repository on GitHub and commit the notebook into version control.

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Relevant Resources:

Version Control With Git: excellent novice-level tutorial from Software Carpentry
Github Guides: set of tutorials on using GitHub
The Whys and Hows of Licensing Scientific Code: my 2014 blog post on AstroBetter

Part 4: Working with Data and GitHub¶

In this video, I refactor the data download script so that it only downloads the data when needed

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Relevant Resources:

How To Package Your Python Code: broad tutorial on Python packaging.

Relevant resources:

Pytest Documentation
Getting Started with Pytest: a nice tutorial by Jacob Kaplan-Moss

Relevant Resources:

Python strftime reference
Pandas Datetime Section from the Python Data Science Handbook

Part 9: Further Data Exploration: PCA and GMM¶

In this video, I apply unsupervised learning techniques to the data to explore what we can learn from it

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Relevant Resources:

Principal Component Analysis In-Depth from the Python Data Science Handbook
Gaussian Mixture Models In-Depth from the Python Data Science Handbook

Part 10: Cleaning-up the Notebook¶

In this video, I clean-up the unsupervised learning analysis to make it more reproducible and presentable.

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embed_video(11)

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Relevant Resources:

Learning Seattle's Work Habits from Bicycle Counts: A blog post using Fremont Bridge data

This post was composed within an IPython notebook; you can view a static version here or download the full source here.

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Machine learning educational resources (wip)

Part 1: Loading and Visualizing Data¶

Part 2: Further Data Exploration¶

Part 3: Version Control with Git & GitHub¶

Part 4: Working with Data and GitHub¶

Part 9: Further Data Exploration: PCA and GMM¶

Part 10: Cleaning-up the Notebook¶

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