The case for national data education is strong: 90% of the data humanity ever generated was created in just the past few years. Almost 5 billion people are now connected to each other on the internet. And six out of ten of the Top 10 emerging careers either are or involve data science. From leaders of large corporations, to individuals making decisions about their healthcare or finances, everyone needs to be literate in the basics of data—modern computing has been transformational. Yet these changes have yet to be reflected for most students across the country. Less than 25% of high school students complete a statistics course according to 2015 NAEP data, enrollment in computer science courses may be even lower despite increased offerings, and any coursework dedicated to using modern techniques and tools for working with real data is nearly absent.
Yet what is “data science?” And how does it relate to Machine-Learning? Data Engineering? Artificial intelligence? And what separates it (if anything) from Statistics? Computer Science? Mathematics? Science?
In short, the field of “data science” is still being created, driven by near-constant technological change in industry and unresolved debates in academia. In a standards-based education system, this presents a challenge: how can we articulate what a subject is when the subject itself is still evolving? This article is meant to provide a simplified, birds-eye view of the evolution of data science, and give context to those working in K-12 education.
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