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#datavisualization

9 posts7 participants0 posts today
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Stefan Bohacek

Interesting.

"Liu worked with professors at Princeton to test how people responded to two different graphs. One showed winter temperatures of a fictional town gradually rising over time, while the other presented the same warming trend in a black-or-white manner: The lake either froze in any given year, or it didn’t. People who saw the second chart perceived climate change as causing more abrupt changes."

grist.org/science/break-throug

Two charts side-by-side. Left: A scatter chart of winter temperatures from 1940 through the 2020s, gradually growing from 22°F to 36°F. Right: A scatter chart with only two values, Freeze and No Freeze, showing how the lake froze almost every winter until around 1990, when it became far less common. Accompanying text from the image: "The climate binary Both charts demonstrate the same warming trend, but the gradual temperature data is less striking than the binary lake data."
#dataviz#data#DataVisualization
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TrevorEddy5250

Speaking with customers over the last year, I have been fascinated by the failed promise of self-serve analytics. High expectations made this a priority for companies large and small looking to drive efficiencies and a competitive advantage from the democratization of data.

In this blog post, I will explain the challenges, including real world examples, and offer an alternative solution that actually works.
#dataviz #datavisualization @observablehq

observablehq.com/blog/the-fail

Public
InkDesign

✨ Transforme Dados em Visuais: Heatmaps com Python!
📝 Descubra como utilizar Python para sobrepor heatmaps a mapas reais com técnicas de machine learning. Aumente sua compreensão de dados geoespaciais e crie visuais impressionantes que contam histórias! Clique e mergulhe neste guia prático e inovador.
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#AI #MachineLearning #DataVisualization
inkdesign.com.br/python-sobrep

INK|DESIGN NEWS · Python sobrepõe heatmap a mapa real com machine learningBy Tiago F Santiago
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No Starch Press

Geometry and topology are reshaping how we think about data. The Shape of Data explores how structure, distance, and dimension impact machine learning—covering networks, manifolds, TDA, and quantum methods.

Case studies span text, survey, medical, and social data, with accessible math and real code examples in R (Python too). A valuable reference for anyone working in geometric or graph-based ML.

nostarch.com/shapeofdata

The image shows the book The Shape of Data centered on a black-and-yellow abstract background with swirling, geometric patterns. At the top of the image, bold yellow text in a rounded black box reads: “Rethink data.” Below the book, another rounded black box with yellow text reads: “Through a geometric lens.” The book cover features a cartoon robot with a ruler examining the contour of a yellow vase. The title text on the book reads: THE SHAPE OF DATA Geometry-Based Machine Learning and Data Analysis in R Colleen M. Farrelly & Yaé Ulrich Gaba In the bottom-right corner is the No Starch Press logo.
#datascience#machinelearning#geometry
Public
Jen Christiansen

In this article on illness from Vibrio bacteria along the U.S. east coast (yup, the genus that includes V. cholerae), cartographer @pinakographos pays homage to John Snow's classic cholera map with modern data from Kyle Brumfield. scientificamerican.com/article #dataviz #datavisualization

Scientific American · Vibrio Bacteria in Beach Water Can Make You Seriously IllBy Kyle Brumfield
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Statistics Globe

I recently made a very popular LinkedIn post about Simpson's Paradox, which resulted in an engaging conversation. Paul Julian made a great comment on the relationship between Mixed Effects Models and Simpson's Paradox that I wanted to share with you.

In the plot below (generated from reproducible code – thanks, Paul!), you can see how different models compare:

Original post: linkedin.com/posts/joachim-sch

Further details: eepurl.com/gH6myT

#datavisualization#database#datastructure
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