Bridging Economics and Data Science
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Bridging Economics and Data Science
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Re: Bridging Economics and Data Science
#2Like the author, I have an Economics background but have gotten into programming as I graduated from working on Excel. Economics has suffered because of a lack of good data - this is why so many explanations by economists begin with assumptions. I'm hopeful that the data sets now available will improve economic models and that people working in the public sector will put them to good use.
Re: Bridging Economics and Data Science
#3Analysts in finance/consulting certainly haven't had to upgrade their skills, but analysts in media/marketing definitely have had to.
Certainly there are people in this space who can't do much beyond spreadsheets, but there are many analyst now who use python/pandas or R to do work.
Re: Bridging Economics and Data Science
#4Like the author, I have an Economics background but have gotten into programming as I graduated from working on Excel. Economics has suffered because of a lack of good data - this is why so many explanations by economists begin with assumptions. I'm hopeful that the data sets now available will improve economic models and that people working in the public sector will put them to good use.
I also come from an Economics background, and am now a software engineer/budding data scientist. As I've delved more into machine learning topics, I'm amazed (though not surprised!) at how both academic and industry economists are still mostly focused on running OLS/logit/probit regressions, and not other classification techniques. My undergraduate thesis did use some computational models that sought convergence for dynamic & stochastic conditions, but that was definitely not the norm.
Re: Bridging Economics and Data Science
#5I have an econ/finance degree and also worked for several years in finance. Then I quit my job and learned how to code. Now I have a web app with a paying and growing customer base. Knowing how to program will give you special powers when combined with your econ background. Congrats!
Re: Bridging Economics and Data Science
#6This is the same in the social sciences. I'm currently a PhD student in one of the top Communication departments in the US, and it's painful to see how far behind in technical skills and tools the curricula are (eg., Excel and SPSS). I've been self-teaching Python, R, and SQL, and extending my knowledge base from simple regression-based stats to data mining and machine learning, to make up for it. Not only does that allow me to work on massive datasets (and push the field forward across methodologies), but it allows me to improve more 'traditional' approaches by sharing data and models (eg., with .R scripts).
Re: Bridging Economics and Data Science
#7This is the same in the social sciences. I'm currently a PhD student in one of the top Communication departments in the US, and it's painful to see how far behind in technical skills and tools the curricula are (eg., Excel and SPSS). I've been self-teaching Python, R, and SQL, and extending my knowledge base from simple regression-based stats to data mining and machine learning, to make up for it. Not only does that…
Quite interesting; I've heard similar things. A good friend of mine, not as math-y as me, was a psychology PhD student in John Bargh's lab in Yale. She ended up getting her doctorate and leaving the field because she didn't want to be the statistics police.
Re: Bridging Economics and Data Science
#8The biggest issue I see from the ivory tower I'm surrounded by is that economists typically doubt the results of data mining. Neural networks, machine learning, etc. are all well known toolkits in computational economics (one of my specialties) but the results from their application are rarely believed.
Re: Bridging Economics and Data Science
#9Like the author, I have an Economics background but have gotten into programming as I graduated from working on Excel. Economics has suffered because of a lack of good data - this is why so many explanations by economists begin with assumptions. I'm hopeful that the data sets now available will improve economic models and that people working in the public sector will put them to good use.
I also come from an Economics background, and am now a software engineer/budding data scientist. As I've delved more into machine learning topics, I'm amazed (though not surprised!) at how both academic and industry economists are still mostly focused on running OLS/logit/probit regressions, and not other classification techniques. My undergraduate thesis did use some computational models that sought convergence for…
Macroeconomics and empirical industrial organization are leading the forefront in terms of theoretical and applied technical advances. You ought to look at discrete choice analysis sometime--great stuff.
I can't speak for industry economists, but the reason we academics tend to spend so much time with OLS/Logit/Probit is their flexibility and scalability.
Re: Bridging Economics and Data Science
#10The biggest issue I see from the ivory tower I'm surrounded by is that economists typically doubt the results of data mining. Neural networks, machine learning, etc. are all well known toolkits in computational economics (one of my specialties) but the results from their application are rarely believed.
Jared Bernstein answers Barry Ritholz on related themes today:
http://jaredbernsteinblog.com/economics-as-market-failure/
It's really hard to tell, especially outside the field, whether someone's computation has found signal and not noise in their data series, or even whether that data series has any significance for different times and different places ...
(You can "Monte Carlo" the past as much as you want, it won't become the future.)
Edit: I probably should have just referenced Sliver's Signal and Noise and left it at that.