Data Curious 31.01.2019 - Visual literacy, saving the river Ganges, and a dataset of dairy products
31.01.2019
We made it to the end of January. Celebrate by taking some time to learn and be inspired.
This week I'm thinking about the role of visual literacy in data viz, how pollution is affecting natural landscapes, the links between political parties and using virtual environments more consistently. Read on for the sweet links.
Read_
Data viz for better communication and the importance of visual literacy
How can data visualization better support communication?
Create understanding. Share information faster. Spark emotion. All of the above.
Read on →
What role does visual literacy play in data visualization?
I've been thinking about visuali literacy lately, so I wrote about why it's so important when practicing data viz.
Explore_
Oscar categories, polluted India and saving the river Ganges
Which Oscar category is least open to newcomers?
I'm fascinated by this weird Oscars chart by FiveThirtyEight. Is it a scatter plot? Bubble chart on an axis? Whatever you want to call it, it's working for me.
How did India become the most polluted country on earth?
Read and explore the beautiful yet frightening visualizations in this Financial Times story to find out.
Pretty, but also not pretty →
How can we save the river Ganges?
Due to the huge amount of tributaries leading into it, the river Ganges is quickly becoming one of the most polluted in the world. This Reuters story explains it beautifully with data throughout.
Analyse_
Dairy products and political parties
Got milk?
Dive into dairy data with the USDA ERS database for milk and major dairy products (h/t Jeremy Singer-Vine).
Creamy →
Where can I find data on political parties around the world?
"Party Facts links datasets on political parties and provides an online platform about parties and their history as recorded in social science datasets." Brill.
Learn_
Jupyter Notebook power-ups, quick data merging, and publishing charts with Flask
Why should I be using virtual environments with Python?
Version control, package management, file organization: need I go on? Here's a quick, easy guide on getting started.
What are the best JavaScrpit charting libraries?
As with most tech questions, it depends. Here's an article that provides a nice overview and comparison between using d3.js, Dygraphs, Chart.js and Google Charts.
How can I quickly process and understand text data?
This comprehensive NLP + Machine Learning walkthrough will take you from start to finish. To dive right into the code, just read through the Jupyter Notebook instead.
Read, clean, classify, train →
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Thanks for reading. More to come next week.
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