ISSUE 353  ·   September 14, 2021

Trends

The ‘Great Attrition’ or the ‘Great Attraction’? 

A record number of employees are quitting their jobs and that's especially true in tech. Whether you're looking for work or looking for workers, understanding what's behind all the movement will help you make smart decisions. 
McKinsey & Company

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Tutorials, Projects & Opinions

Why data scientists shouldn’t need to know Kubernetes

While it may be useful to have full-stack data scientists, better tooling would help ensure that their time is well spent. In this post, Chip Huyen explores the evolving expectations of data science roles, current tools that help abstract away lower level tasks, and what's still missing.
Chip Huyen

Possession Is The Puzzle Of ⚽ Analytics

Great post about soccer's next big metric. Ultimately, "possession value" seeks to model the state of the game at any time and predict what that means about future rewards. For a lot of reasons, it's a non-trivial problem and there are a lot of models to choose from. Here's why.
FiveThirtyEight | John Muller

Gaussian processes From scratch

This first in a three part series introduces Gaussian processes and implements them from scratch using Python and NumPy. This is easy to follow with lots of visuals and code snippets along the way. For parts two and three, follow the links at the top of the post. 
Peter Roelants 

Intro to Bayesian Methods for Media Mix Modeling

Media mix modeling is a type of analysis that helps optimize how marketing dollars are spent across multiple channels. Linear regression is commonly used but here, Michael Kaminsky introduces an approach using Markov chain Monte Carlo (MCMC), which can be more powerful and flexible than linear regression.
Recast | Michael Kaminsky

What’s An OLAP Cube?

Great introduction to OLAP cubes. Slice, dice, pivot, and hypercubes — this covers it all. And along the way you'll learn how this pattern fits with a modern data warehouse.
Claire Carroll

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Code & Tools

River

River is a Python package for machine learning on streaming data. Models process one observation at a time and can be updated on the fly. This is a general purpose library that handles regression, classification, and unsupervised learning.
GitHub | OnlineML

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