ISSUE 345   ·   July 20, 2021

Insight

For SQL

As a counter to last week's popular article "Against SQL," Pedram Navid takes a look at SQL from a different perspective. SQL has become the universal language for data and many people who work with SQL everyday are for SQL. But articles that attack SQL for its technical imperfections reinforce what's become a class divide between "Software Engineers" and "Data People." Wherever you work in data, there are important issues brewing here.
​data based | Pedram Navid

Analytics is at a crossroads

Benn Stancil's response to the For/Against SQL posts explores the emergence of a new role, the "analytics engineer," and the crossroads it presents for analytics. Down one path, analysts are largely measured by technical skills on a continuous spectrum with engineers. Down the other path, being an analyst requires great critical thinking and not necessarily technical skills. This is an insightful post that speaks to the heart of an important debate.
Benn Stancil

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Tutorials, Projects & Opinions

Let's get analytical about ML model updates

Is it time to retrain your machine learning model? Even though data science is all about the data, the answer to this question is oftentimes based on a gut feeling. Here's how to do better.
Evidently AI | Elena Samuylova, Emeli Dral

Learning Kedro

This introductory tutorial explores the open-source Python framework called Kedro. Kedro uses concepts from software engineering to help you create ML code that's reproducible, maintainable and modular.
Jo Stichbury

The Quick & Dirty Guide to Building a Data Platform

There are a lot of technologies you could use to build a data platform - but what do you really need?
Monte Carlo Data | Barr Moses, Lior Gavish

Workflows for querying databases via R

In this post, Emily Riederer walks through a few useful patterns for querying databases in R.
Emily Riederer

How to become a better R code detective?

These tips and resources will help you approach unfamiliar code and get better at debugging and reading it.
Maëlle Salmon

Selecting data labeling tools doesn’t have to be hard

Preparing data for an AI system can be challenging, laborious, and expensive. Luckily there’s a litany of tools that mitigate the tedium of the process. Selecting the right tool, however, is a challenge on its own. iMerit, a provider of high-quality AI training data shares simple tips and insights on making the right choice, based on first-hand experience. Read here.
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Resources

Charting the ‘Data for Good’ Landscape

"Data for Good" means different things to different people, which makes the space less effective than it might be otherwise. In this post, Jake Porway introduces the Data for Good landscape, including challenges, opportunities and links to key initiatives.
data dot org | Jake Porway

Project Pick

Is This Prime?

This game was recently resurrected on Hacker News and quickly racked up over 100K plays. How many primes can you guess in a minute?
Christian Lawson-Perfect

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