The data newsletter by @puntofisso.
Hello, regular readers and welcome new ones :) This is Quantum of Sollazzo, the newsletter about all things data. I am Giuseppe Sollazzo, or @puntofisso. I’ve been sending this newsletter since 2012 to be a summary of all the articles with or about data that captured my attention over the previous week. The newsletter is and will always (well, for as long as I can keep going!) be free, but you’re welcome to become a friend via the links below.
Happy birthday to the European Data Journalism Network :) Their approach to creating recipes and datasets for data-driven reporting that everyone can openly use has really been a pioneering approach to journalism in Europe. They have grown to be a network of 31 media outlets based in 19 different European countries, creating ties between different organisations that are advancing data journalism.
The most clicked link last week was the Financial Times’ visual vocabulary website.
‘till next week,
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From reddit’s r/datataisbeautiful. Beautiful and depressing.
(via Daniele Bottillo)
One of the best data viz on this topic, by Bloomberg.
A local analysis and map, looking at what happened in Baltimore during the election.
“The 2022 U.S. midterm election results strayed from the pattern of almost every midterm since World War Two, which normally shrinks the footprint of the party in power in both chambers of congress.“
This is by Reuters as is this look at approval ratings and other forms of opinion shifts.
An interesting look at how media are somewhat selective about which missing people stories they cover. Interactive and based on good data: “Our analysis and model is based on a representative sample of 3,630 news stories about missing persons out of 19,561 collected by Meltwater Jan-Nov 2021. Of this sample, 2,383 stories concerned one or more specific missing individuals, covering 735 unique missing persons who were identified and categorized by age, gender, race / ethnicity, and geography. Missing persons were then cross-referenced with the NAMUS database for the same period. Meltwater identified the publisher of the story, the potential reach of that news outlet, and social sharing for each story.“
There’s currently a good deal on this brilliant course by designer Valentina D’Efilippo, a graphics journalism star.
“Some common geographic mental misplacements”.
“Fast, Declarative, Reproducible, and Composable Developer Environments”. Without containers.
“Let’s say you need to understand how your data changes within a day, and between different days. Functional analysis is one approach of doing just that so here’s how I applied functional analysis to some air pollution data using R!”
“My overarching goal as an MLE is to continuously work towards designing and deploying well-designed, and transparent machine learning systems and to learn the best software engineering practices to do so.“
Machine Learning engineer Vicki Boykis explains how she keeps learning.
A Twitter question (while stocks last) by journalist and educator Paul Bradshaw.
Potentially useful in a DDJ/Dataviz context.
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“It has been a bad year for most of the stock market. But why has it been so much worse for fintech?“
Some great charts by Pranshu Maheshwari.
“Although NFL game outcomes are far from random, teams still get lucky. We identified four key scenarios where teams benefit from actions in a game that are almost entirely derived from opponent performance or lucky bounces.“
Interestingly, this article is on the actual NFL Football Operations website.
Datawrapper’s Head of Data Visualization Gregor Aisch looks at the lead causes of carbon emissions.
“The ages, races, and population density of the United States tell a story. Understand the shifts in demographic trends with these charts visualizing decades of population data.“
“AI researchers are warning developers to focus more on how and why a system produces certain results than the fact that the system can accurately and rapidly produce them.“
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