Birding and data visualization

Jer Thorp has combined birding and data visualization into a unique course called Binoculars to Binomials:

I dreamt up Binoculars to Binomials as a hybrid site of learning. It’s for coders who are interested in cultivating an observational practice, and for birders who want to dive into the rich pool of data that comes out of their hobby.

More broadly, it’s for anyone who’s interested in the overlap between nature, data and creativity.

Sounds good to me.

One of the best ways to learn how to visualize data is to apply it to a specific field. You figure out the mechanics and the context behind the data, which makes visualization meaningful and useful. In this case, you get your hands in all parts of the process.

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Steps for getting started with data visualization

Getting started with data visualization can be tricky because of all the resources and tools available these days. Approaches also change with what you want to visualize data for. For Datawrapper, Lisa Chartlotte Muth outlines how to take your first steps.

The main theme is to make more charts.

See also: getting started with visualization after getting started with visualization.

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Introduction to statistical learning, with Python examples

An Introduction to Statistical Learning, with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani was released in 2021. They, along with Jonathan Taylor, just released an alternate version with applications in Python. So if Python is your thing, have at it. Like the R version, it is free to download as a PDF.

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Introduction to statistical learning

An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani:

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

The PDF version of the book is free to download. There’s also a free online course companion.

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10 statistical lessons from the past pandemic year

The Royal Statistical Society published ten lessons governments should takeaway from this year, which should naturally apply to standard data practice:

  1. Invest in public health data – which should be regarded as critical national infrastructure and a full review of health data should be conducted 
  2. Publish evidence – all evidence considered by governments and their advisers must be published in a timely and accessible manner
  3. Be clear and open about data – government should invest in a central portal, from which the different sources of official data, analysis protocols and up-to-date results can be found
  4. Challenge the misuse of statistics – the Office for Statistics Regulation should have its funding augmented so it can better hold the government to account
  5. The media needs to step up its responsibilities – government should support media institutions that invest in specialist scientific and medical reporting
  6. Build decision makers’ statistical skills – politicians and senior officials should seek out statistical training
  7. Build an effective infectious disease surveillance system to monitor the spread of disease – the government should ensure that a real-time surveillance system is ready for future pandemics
  8. Increase scrutiny and openness for new diagnostic tests – similar steps to those adopted for vaccine and pharmaceutical evaluation should be followed for diagnostic tests
  9. Health data is incomplete without social care data – improving social care data should be a central part of any review of UK health data
  10. Evaluation should be put at the heart of policy – efficient evaluations or experiments should be incorporated into any intervention from the start.

See the full report here.

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How practitioners learned data visualization

Visualization is still a relatively young field, so people learn about and how to visualize data in a lot of different ways. For instance, there weren’t any visualization-specific courses when I was in school, so I picked up a lot ad hoc. Alli Torban, looking at responses to the 2018 Data Visualization Survey, shows how others learned. The top three: examples, collaboration with those more skilled, and books.

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Beginner’s guide to visualization literacy

Mikhail Popov, a data scientist at the Wikimedia Foundation, led a workshop on visualization literacy recently. A short guide from that workshop is now freely available online.

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Statistics crash course

Odds are if you’re reading this, you know what statistics is already, but if not (or you want to explain to someone else), Crash Course just started a series to explain the basics. Watch below.

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Data journalism lessons available from ProPublica Data Institute

ProPublica runs a small annual workshop to teach journalists a bit about data and how it can be used to report. The training materials and some of the lectures are online now.

Though more than a thousand people have applied to ProPublica’s Data Institute, we’ve only been able to accept about 24 in the two years it’s been running. Faced with such a high demand, we’ve looked for ways to help more journalists trying to learn data journalism and interactive database design. In past years, we’ve put our slides and homework assignments online (here’s 2016 and 2017 ), but we also know how valuable it is to be able to see and hear what’s happening in the classroom.

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C’est la vis, teaching kids how to visualize data

By adulthood, it’s expected that we can read charts to some degree. You’re supposed to know how visual encodings map to data and then interpret. I don’t remember actually learning how to do that though. Do you? C’est la vis is a research project and app that aims to help with that. The project, by Basak Alper from NASA JPL and Nathalie Riche from Microsoft Research, along with Fanny Chevalier, Jeremy Boy, and Metin Sezgin, aims to help kids learn how charts work and help teachers create a curriculum that’s useful.

Hear about their work on the latest Data Stories podcast. A lot of the lessons learned can cross over to teaching grown ups visualization too.

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