Communicating data science: An interview with a storytelling expert

In May I announced that I was assembling a series for the blog covering topics related to creating and presenting analyses including: the ingredients of a well-constructed analysis, data visualization, and practical guides to using tools like Rmarkdown and Jupyter notebooks. The internet is host to innumerable tutorials on every aspect of machine learning from simple linear regression to cutting edge algorithms in deep learning. However, it’s often acknowledged that a career in data science typically requires more time and effort spent on data analysis and understanding than on intense computation. What more does it take to translate Kaggle ranking points into tangible job prospects? Soon after, I came across an excellent write-up by Tyler Byers, Data Science: Beyond the Kaggle, and I knew I was in good fortune. Tyler,…


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