Self Driven Data Science — Issue #52
Weekly rundown of interesting news and insights focused on data science, machine learning, and artificial intelligence
Here’s this weeks lineup of data-driven articles, stories, and resources ranging across the broad field of data science while offering value to both beginners and experienced practitioners alike. If you would like this post delivered faithfully to your inbox each week, then go ahead and subscribe. Enjoy!
Using data science and the concept of Pareto efficiency, this post dives into exploring the most effective Mario Kart player combinations through concise explanations and excellent visualizations.
Models make mistakes if their patterns are overly simple or overly complex. The long-awaited sequel to their decision tree focused initial post, R2D3 explains tuning and the Bias-Variance tradeoff.
The team at Stitch Fix shows how optimization modeling can be applied to real problems. This post is an introduction to constrained optimization aimed at data scientists and developers fluent in Python, but without any background in operations research or applied math.
The author makes a strong argument for the importance of Logistic Regression as a tool for learning fundamental data science concepts, especially when it comes to learning the basics of the ETL pipeline.
This post touches on a common misconception that d3 and data visualization are the same thing by emphasizing how d3 is a toolkit for communicating complex data-driven concepts on the web.
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