When did Data Science Become Synonymous with Machine Learning?

Many folks just getting started with data science have an illusory idea of the field as a breeding ground where state-of-the-art machine learning algorithms are produced day after day, hour after hour, second after second. While it is true that getting to push out cool machine learning models is part of the work, it's far from the only thing you'll be doing as a data scientist. In reality, data science involves quite a bit of not-so-shiny grunt work to even make the available data corpus suitable for analysis. According to a Twitter poll conducted in 2019 by data scientist Vicki Boykis, fewer than 5% of respondents claimed to spend the majority of their time on ML models [1]. The largest percentage of data scientists said that most of their time was spent cleaning up the data to make it usable.