Matt Nolan

Discussion Tuesday

/12 Comments/in /by

On a recent Top 10 at 10 I did on interest.co.nz (about economists) came the following comment:

U know what economists will be replaced by in next 10 yrs?

Data Scientists!

Trust me on that.

Thoughts?

12 replies
  1. Daniel J. Taylor
    Daniel J. Taylor says:

    Not so sure on this claim as I think “data scientist” is a catch-all term which requires sub-grouping (I’ll take DS as meaning one who deals in data and drawing conclusions from aligned statistical analysis).
    I would posit that the DS’s themselves split into specialised roles eons ago. One such role was “economist” and the same can be said for other DS’s working in thier own specialised areas (for example, epidemiologists or meteoroligistis etc etc).

    • jamesz
      jamesz says:

      I think that you’ve pretty much defined a ‘data scientist’ as a ‘scientist’. Surely this relates to ‘big data’ and it’s suggesting that economics will soon overcome its data problem by analysing massive datasets. Since a lot of the data problems are longitudinal I’m sceptical, but there’s probably more we could do with the big, cross-sectional datasets out there. Finance types seem to be doing some interesting work with it, after all.

      • Daniel J. Taylor
        Daniel J. Taylor says:

        What you say makes sense, although I would strongly differentiate between what I’ve termed a data scientist (in my head at least) and what I would consider a “scientist”, as scientific method requires reproductivity (not something a fair chunck of data can allow for, nor the point of quite a bit of analysis). Perhaps the term Data Analyst is more appropirate? Agreed that big data is not the panacea many claim it to be but could be useful in many instances.

        • Daniel J. Taylor
          Daniel J. Taylor says:

          I guess what I am trying to say in far too many words and should have said at the start is that the term ‘data scientist’ is a title given to data analyists or statisticians who want to pretend they are scientisits. Seeing as economists are already keen data analysts the quote above about econonomists being replaced by data scientists is moot.

        • jamesz
          jamesz says:

          I’m confused. Are you saying economics is not a science because economists can’t run controlled experiments to reliably reproduce their results. So they’re just data analysts and that’s the same as a data scientist? Because that sounds… controversial!

  2. Matt Nolan
    Matt Nolan says:

    From your comments Dan and James, I think we all agree that there are specialised skills required to “interpret” data – quantitative and analytic. No single discipline can truly encompass all of these, which is what is being suggested by the data scientist call (and is potentially sometimes the way economists act) – as a result the quote appears false.

    But defining the analytic and quantitative content, and the way they inter-relate and are formed (eg the meta concepts of what is and categorization, which qualitative work helps with), is still a useful way to understand what a discipline has to offer. The person being quoted may merely be saying that, from their perspective, there is too little data analysis in economics. Of course this implies that they know virtually nothing about how economics is actually done, but it is still a perspective 😉

  3. Paul Walker
    Paul Walker says:

    Oliver Hart has writeen

    “Although theory may not be as prominent as it once was, it remains essential for understanding the (increasingly) complex world we live in. One cannot analyze the bewildering amount of data now available without the organizing framework that theory provides. I would also suggest that one cannot understand the extraordinary events that we have recently witnessed, such as the financial crisis, or make sensible policy recommendations in response to these events, without the organizing framework of theory.”

  4. ben
    ben says:

    I see complementarity between economists and data, economists bringing the frameworks and tools necessary to understand and use the data. So I would say the opposite is true.

    Like the death of distance argument. Digital networks actually raised the rewards to agglomeration by more than they killed distance costs. A mobile phone is more useful in NYC than it is in Dakota, though it is useful in both places.

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