Self Driven Data Science — Issue #51
Weekly rundown of interesting news and insights focused on data science, machine learning, and artificial intelligence
In this blog post, the Twitter Engineering team discusses the history, evolution, and future of their framework for machine learning. Awesome technical post, especially if you find yourself on a fast growing data science team.
While this guide is about a year old and some resources have popped up since, it’s still well-worth bookmarking. I’m excited to use this as a jumping off point for starting an online course specifically focused on dataviz.
Jupyter is so effective for interactive exploratory analysis that it’s easy to overlook some of it’s other powerful features and use cases. This post covers some of the lesser known tricks and tips that can and will boost your productivity.
Especially as Data Scientists, reading up on a tool or technique before diving into a project will only save you time and energy later on. This post includes some useful resources for reading up and ensuring your development skills are up to par.
It’s crucial to ask the right questions prior to analysis in order to ensure that you fully understand the problem. What are the ‘right questions’ you ask? While it’s largely dependent on the problem, these 20 examples serve as a solid starting point.
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