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AI: "It's raining cats, dogs and pitchforks"

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A survival guide for the AI flood nobody asked for but everyone is drowning in Let me start with a small historical detour. Bear with me. When Gutenberg finished tinkering with his printing press around 1440, the world did not just get books. It got flooded with pamphlets, religious texts, political manifestos, bad poetry, and the 15th-century equivalent of LinkedIn thought leadership posts. Scholars at the time complained that there was simply too much to read. Too much information. Too many opinions. Sound familiar? Image: Reproduction of a Gutenberg-era press at the Printing History Museum in Lyon, France. Photograph by George H. Williams, via  Wikimedia Commons . Fast forward to the late 1800s. Electricity arrives. Not just as a concept, but as a roaring industrial wave. Factories rewire. City streets get lit up. People panic about whether sleeping near electrical wires would kill them in their beds. Newspapers ran think pieces about "the overstimulation of modern life." ...
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7 Charts That Lied to Your Face: Why Bad Data Visualization Is the Class My Students Never Miss

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For the past few years, I have been teaching data visualization to data science students at Université Paris Cité at the IUT de Paris — Rives de Seine . And while teaching Python coding is a relatively simple thing, I have decided to shift the course toward a more useful and pedagogical angle: how to make data speak, and how to make good charts, the correct way. One of the chapters I spend around 25% of the course time on is bad data visualization: mistakes made with charts, misleading visual design, and how a graph can quietly manipulate the reader. This is the part of the course where I get 100% attendance and around 97% active interaction from my students. Yes, I am a statistician, and I keep track of a lot of metrics. It is probably because this is the single course section where they are not required to open their computer, nor to work on a practical exercise. I start the chapter by dimming the light of the room. I launch my slides. No explanation. No theory. No code. Just charts...
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The Complete Guide to Python Data Visualization Libraries

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  The Complete Guide to Python Data Visualization Libraries P ython has become the go-to language for data visualization, and for good reason. Its ecosystem offers a tool for every situation, whether you are exploring a dataset for the first time, publishing a polished chart for a research paper, building an interactive dashboard, or rendering millions of data points without melting your browser tab. The problem is that there are a lot of choices. Too many, arguably. If you have spent any time searching for “best Python visualization library,” you have probably come across a dozen names and felt more confused after than before. This guide cuts through the noise. We cover the most important active libraries from the PyViz ecosystem , group them by what they are actually good at, explain what makes each one worth knowing, and give you enough context to pick the right tool for the job. A companion Jupyter notebook with working code for every library is available alongside this article...
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