Self Driven Data Science — Issue #25

Here’s this weeks lineup of data-driven articles, stories, and resources delivered faithfully to your inbox for you to consume. Enjoy!

Advice to Aspiring Data Scientists: Start Blogging

This post by David Robinson, Data Scientist at Stack Overflow makes a strong argument outlining the key reasons why blogging is well worth your time as a Data Scientist, regardless of your experience level.

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Algorithms Every ML Engineer Needs to Know

Machine learning has been around for a while before deep neural networks took over the scene. This post contains a list of the algorithms you need to know, so you can tackle any problem that comes your way. This isn’t an exhaustive list, but your bases will be mostly covered.

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Data Meta-Metrics

How do we communicate confidences and doubts about data to a non-technical audience (in a way that is efficient and easily interpretable)? This post attempts to answer that question by experimenting with embedding a “state of the data” in presentations through red, yellow, and green data meta-metrics.


How to Learn Pandas Tutorial

This post goes over the basics of Pandas, the most popular data manipulation package for Data Science with Python. The author outlines a strategy to ‘learn pandas’ in a clear and concise way.

Crash Catcher: Detecting Car Crashes in Video

In this post, the author describes how he built a classification machine learning algorithm that employs a hierarchical recurrent neural network to isolate specific, relevant content from millions of hours of video. Very cool project that is well worth looking over for inspiration and learning purposes.

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