Academia needs Data Science


Academia has proven slow to adapt best practices and retain many brilliant quantitative scientists. This is bad for science, as prevalent data analyses are often suboptimal, data-driven discoveries remain underdeveloped, and highly-skilled people, who could use their unique expertise to move forward the most pressing scientific questions, are lost to industry. We propose three reasons why this is happening. In all, this results in a slow uptake of best data science practices and a ‘brain drain’ out of academia. Some research institutions are already taking measures to ensure this does not slow scientific progress. Others should take note, lest they be left behind.


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