CS and DS people are getting more applied and gaining domain expertise, and can do a lot of economics work now. Academic economists, especially those who primarily do data science / big data, seem to basically be doing Masters-level data science projects for their Ph.D. The hard part in their Ph.D.s is collecting the data, which used to be a very manual job that relied on connections, but more of them are getting them or imputing them from public sources so it's not that impressive anymore.
Speaking as someone who has attended 3 economics Ph.D. defenses in the past two years.
Data science wasn't even a degree you could get 20 years ago. Twenty years ago if you were interested in what is now called data science, you were getting a degree with some kind of exposure to applied statistics. Economics is one of those disciplines (through econometrics).
No, I did stats as part of economics around then, and it's nothing like modern DS. It overlaps a fair bit, but in practice the classical stats student is bringing a knife to a gunfight.
The practice of working with huge datasets manipulated by computers is valuable enough that you need separate training in it.
I don't know what's in a modern stats degree though, I would assume they try to turn it into DS.
Data science is basically a marketing title given to what would have been a joint CS/statistics degree in the past. Maybe a double major, or maybe a major in one and an extensive minor in another. And it's mostly taught by people with a background in CS or statistics.
Like with most other academic fields, there is no clear separation between data science and neighboring fields. Its existence as a field tells more about the organization of undergraduate education in the average university than about the field itself.
The Finnish term for CS translates as "data processing science" or "information processing science". When I was undergrad ~25 years ago, people in the statistics department were arguing that it would have been a more appropriate name for statistics, but CS took it first. The data science perspective was already mainstream back then, as the people in statistics were concerned. But statistics education was still mostly about introductory classes of classical statistics offered to people in other fields.
No. Data science is different than statistics, because it is done on computers. It also uses machine learning algorithms instead of statistical algorithms. These advances, and the shedding of generations of restrictive cruft - frees data scientists to craft answers that their bosses want to hear - proving the superiority of data science over statistics.
yeah, we called that data mining, decision systems, and whatnot... mapreduce was as fresh and hot as the Paul Graham's essays book... folks were using Java over python, due to some open source library from around the globe...
essentially, provided you were at a right place in a right time, you could get a BSc in it
His work and department was very quant heavy. I'd say the majority of his students spend most of their time in Python/R cleaning datasets running models
I'm not disagreeing with you, and I also know political economists from that time who complain that their discipline is changing. It just has very little to do with what this article is discussing.
Speaking as someone who has attended 3 economics Ph.D. defenses in the past two years.