{"id":4509,"date":"2021-03-24T06:35:23","date_gmt":"2021-03-24T06:35:23","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2021\/03\/24\/dsc-weekly-digest-22-march-2021\/"},"modified":"2021-03-24T06:35:23","modified_gmt":"2021-03-24T06:35:23","slug":"dsc-weekly-digest-22-march-2021","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2021\/03\/24\/dsc-weekly-digest-22-march-2021\/","title":{"rendered":"DSC Weekly Digest 22 March 2021"},"content":{"rendered":"

Author: Kurt A Cagle<\/p>\n

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The Future For Data Scientists<\/span><\/h2>\n
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One of the first jobs that I had after college (in the midst of a recession) was working as a typesetter for a small company in Florida in the latter 1980s. Having spent almost every waking hour on computers during school, this was hardly the kind of job where you’d expect there to be an issue about job security. Working on the then-current Linotronic hardware, my job was to markup text to be formatted on film using computer codes, which gave me an early insight into work I’d be doing a decade later with XML.<\/p>\n

Things went swimmingly for the first six months or so, until a small company called Aldus, out of San Jose, California released a program called Pagemaker for the new Macintosh computer. For many companies, Pagemaker was a game-changer, making it possible to create professional-quality content visually in real-time. For our small typesetting firm, it was the End Times. The company went from having a revenue of $10 million when I started to less than $150,000 when I was finally laid off with the rest of the staff a year later. For me, it provided a window into understanding how quickly technology can completely rewrite the landscape.<\/p>\n

The field of data science has changed dramatically since DSC first began in 2012. The niches that opened up after those first few years have largely been filled, and competition for baseline data science jobs has increased even as salaries have dropped. Knowing what a Bayesian is or how to correct for skewed distributions is no longer enough, and in many cases, work that used to require R or Python working in an IDE has now become integrated into mainstream BI tools, available at the click of a button.<\/p>\n

That does not mean that there are no data science positions out there, only that many of them are increasingly specialized. In essence, the future of the data scientist is where it’s always been, as a subject matter expert who has the knowledge and experience to use the tools of data science, machine learning, and AI in order to better understand and interpret their own domain. This shouldn’t be surprising, but for all too many people who want to be data scientists first, I’d make the case that they should look upon data science as a toolset, a set of skills that all researchers and analysts should have.<\/p>\n

This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society.\u00a0Data Science Central<\/a>\u00a0is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to\u00a0submit original articles<\/a>\u00a0and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.<\/p>\n

In media res,<\/span>
Kurt Cagle<\/a><\/span>
Community Editor,<\/span>
Data Science Central<\/a><\/span><\/p>\n<\/div>\n<\/div>\n

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