Insight. Applied.
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The Pudding creates visual essays to explain issues of cultural significance/importance.
Visual essays are an emerging form of journalism. Some of the most complex, debated topics get lost in “too long; didn’t read” 10,000-word articles. Visual storytelling makes ideas more accessible[…]"
If you are interested in storytelling with data then you’ll enjoy their work.
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The Economist recently selected its (own) 12 favourite charts of 2018.
My favourite is the penalty shoot-out visualisation. A great example of how to present statistical data to a general audience.
I also liked their "Build an American Voter" widget. Apparently I’m not alone as it won them an award.
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A statistical note published in the BMJ (formally the British Medical Journal) reminds us that
…trials that do not show a significant difference between the treatments being compared are often called "negative." This term wrongly implies that the study has shown that there is no difference, whereas usually all that has been shown is an absence of evidence of a difference. These are quite different statements.
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Google have created a tool to make it easier to discover datasets—Google Dataset Search.
One potential downside is that it requires dataset owners to provide metadata. While the Google brand means that this might get some traction, not all dataset owners are motivated to help the cause. Publication of datasets is now mandated by some funding bodies, but that doesn’t mean that the datasets have to be discoverable. Ideally we’ll see funding bodies now mandating the addition of metadata.
While we wait for Google Dataset Search to evolve, we can still rely on curated repositories and lists such as
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Jupyter Notebooks are popular with data scientists. Microsoft even offers a free, hosted, “no-install” service for Python, R and F#.
However, there are some downsides to notebooks—mostly to do with software engineering best practices.
Joel Grus gave a provocative talk at JupyterCon 2018 entitled “I Don’t Like Notebooks”. Yihui Xie then followed up with a response to Grus’ talk.
Both authors make a good case and have interesting points. As ever, the truth is that notebooks are good in some situations and not so good in others.
Personally, I use both. Notebooks for smaller, exploratory, data science projects and IDEs (Visual Studio Code, PyCharm and RStudio) for everything else.