ISSUE 386  ·   May 10, 2022

Organizations

Hiring Data Scientists With Intention

Hiring is hard. Especially, for data roles. This is an insightful post that offers a systematic and practical hiring approach that will help ensure you get the right people with the right skills on your team.
Emily Thompson and Tara Robertson

Sponsored Link

Introduction to Automated Data Labeling

Preparing high quality vision datasets is a long and cumbersome process. This is why companies are now using automation to accelerate labeling and QA. Take a dive into automating data preparation with Superb AI and whether or not automation is right for you and your team. 

Tutorials, Projects & Opinions

Paper reading time - Forgetting in Data Science

Randy Au dives into a paper from CHI 2022 where Michael Muller and Angelika Strohmayer explore the human work in data science and how, when, and where things are forgotten throughout the data pipeline.
Counting Stuff | Randy Au

In Conversation with Barr Moses, CEO, Monte Carlo

How do you know you can trust the data that you're using? In this interview, Barr Moses, CEO of the rapidly growing Monte Carlo, explores common issues that you're probably not thinking about.
Matt Turck and Barr Moses

How to measure data quality

Great starting point for thinking through why and how to measure data quality in your organization.
Mikkel Dengsøe

Superb AI : Automate Data Preparation

Preparing high quality vision datasets is a long and cumbersome process. This is why companies are now using automation to accelerate labeling and QA. Take a dive into automating data preparation with Superb AI and whether or not automation is right for you and your team. 
// sponsored

Machine Learning

Industrial Scale Text Classification

What do you do when a client comes to you with a problem, unrealistic performance requirements, and your company’s survival depends on the solution? This is a great, first-hand account of building a prediction system in the real world.
Laszlo Sragner

What about MLOps?

Partly inspired by Laszlo Sragner's post, above, Shreya Shankar breaks down key considerations for your machine learning platform, depending on how far along you are.
Twitter | Shreya Shankar

How Should you Protect your ML Models and IP?

How do you make sure your models don't end up helping your competitors? The answer depends on your specific situation but after helping hundreds of teams ship ML-based products, Pete Warden has a few practical suggestions.
Pete Warden

Data Visualization

Horizon Charts

Horizon Charts are a specialized type of chart that allow you to display dense time series data in a limited amount of space. They're useful but can be tricky to understand. This is a slick, interactive explainer that makes it easy to see how Horizon Charts work. 
Devin Lange

Visualization using R for researchers who don't use R

This hands-on tutorial is a great introduction to using R for data visualization. Following a "Getting Started" guide, sections cover data transformations, summary stats, advanced plots, styling, and more.
Nordmann, E., McAleer, P., Toivo, W., Paterson, H. & DeBruine, L.

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