Data Science

Thirty days of machine learning with Kaggle

Last month, I received an email from Kaggle inviting me to participate in a beginner-friendly “30 Days of Machine Learning” challenge. It was a timely reminder to continue on my data science learning journey, especially since I haven’t made any progress for quite some time.

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Image credit: Jake VanderPlas, O'Reilly Media

A Whirlwind Tour of Python

“A Whirlwind Tour of Python” by Jake VanderPlas is a handy little reference for those with some prior programming experience but who are new to Python. It is compact (only 98 pages) yet feature rich, and is a good starting point for picking up Python.

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Picking up Python through Kaggle Learn

One unexpected benefit of joining Kaggle was the discovery of an introductory Python course on Kaggle Learn. It is free to use and consists of short tutorials and hands-on notebook exercises that highlight the key aspects of the language.

The course has a focus on data science applications and is targeted at those with some prior coding experience.

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Using a shotgun approach for the Titanic competition

After completing Alexis Cook’s very useful Titanic Tutorial, I couldn’t help myself and spent a couple of days hacking around to try and improve my score without going through the usual data science workflow of EDA, feature engineering, model selection, hyperparameter tuning, train/test iterations.

I know it’s not the proper way of doing data science, but like I said, I just couldn’t help myself.

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