

Bridging Economics and Data Science - calcsam
https://medium.com/about-data/9351f95863e0

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zissou
I'd encourage any economist that has a knack for programming to really put in
some time towards the area. I've owned econpy.org for about 3 years now
(although I haven't updated it in a long time). I also own economics.io and
run econpy.blogspot.com.

It's much easier (relatively speaking) for an economist to pick up some
programming than it is for a programmer to pick up some economics. Economists
are already familiar with the types of questions that are important to
economists, and more importantly, how to frame them. The trouble with
economics is that you can't just pick it up overnight as it is a way of
thinking more than it is a tool set. Programming on the other hand is
something that you can "get working" overnight (economic programmers don't
need to be algorithmic theorists -- they just need to be really good at
getting/scraping data and organizing it so they can run analyses on it).

Over a year ago, I dropped out of my PhD program in economics because I was
not at a school that was going to allow me to do the econ/cs type work I was
working on. Leaving my PhD program was one the best things I ever did because
it has allowed me to pursue whatever I want to do with the skills I've
acquired.

The problem with academic economics is that the data most economists use is so
bad and outdated -- such as data from FRED, BLS, and other publicly available
sources where everyone and their uncle can download the same CSV dataset that
was aggregated by some government employee. The race then is to see who can
put together the most elegant econometric model to handle all the issues with
the data. The rules of the game change when you create your own dataset and
thus have control over while variables to include, the aggregation, the
frequency, etc.

Long story short, if you are an economist wanting to do programming, learn to
adapt those skills in academia (best way is to find a great advisor -- if
there isn't one in your economics department, check the business school as
bschool professors are often much more open to highly empirical analyses and
care [marginally] less about getting the theory perfect). Or, if you want an
easier lifestyle that is much more rewarding, ditch academia for the private
sector. You'll find the economists in the private sector to be much more
knowledgeable about cutting edge technologies and willing to listen and learn
from what you have to say.

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porter
I 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!

~~~
bobbyongce
I have an econs background too. I learned to code in my final year of uni and
now do some freelancing. You are right that knowing how to program will give
you special powers!

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aheilbut
He nails the point, and then misses it himself:

 _the key to success is in cleverly selecting, finding, or creating a data
source that answers a particular question_

It's about asking the right question and then finding or generating the right
data to answer that question. That's what makes it science.

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kfk
When you need to study 30 plus income statements from 30 plus entities around
the globe, you wonder less about the tools and more about getting it done.
Then you become a "spreadsheet monkey", but that is only because nobody has
been able to create a better tool since 1995.

I understand the pain of seeing people using old tech and taking hours to do
tasks that should take seconds, but do not underestimate the importance of
specific field knowledge and the fact that many people do not have time to
learn coding. If you think about it, it is an opportunity for you to build a
bridge between those 2 worlds. For example, nobody has produced yet a decent
tool to consolidate financials...

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alexleavitt
This 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).

~~~
cvet
I'm in a well regarded sociology department and there's a huge gap between the
sociologists who take technical things seriously (programming, stats) and
those who don't. People have actually rolled their eyes in classes when we
read papers about simulations. I was lucky: we have a core group of students
who program and gather data using modern tools, but I gather this is rare both
within my discipline and without.

~~~
algebr
That's a real shame, data science will hopefully cut through the theoretical
BS that are the results of so many social science thesis'.

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mathattack
A financial economist professor once mused that "current phd students are
limited in the problems they can solve because they can't program." Of course
the programming language he preferred was Fortran. :-)

Excel is the best tool for 80% of what bankers and consultants do. It can
middle through the next 10%. The problem is it has just no way to do the last
10%. Either it's too slow or just can't handle the size or computations
required.

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anishkothari
Like 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.

~~~
mlader
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.

~~~
tomrod
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.

~~~
mlader
Macro was my favorite subject! I was lucky enough to take the first year PhD
sequence during my last year, which was my first taste of coding =D

I think in industry (anti-trust at least), they stick with the older models
because their value has legal precedent, and using new methods would require
some more legal hand waving by the attorneys.

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thauck
Analysts 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.

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tomrod
The 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.

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jjindev
Jared Bernstein answers Barry Ritholz on related themes today:

[http://jaredbernsteinblog.com/economics-as-market-
failure/](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.

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tryitnow
Ditto. I am following a similar path. I am curious if there are any groups
dedicated to people like us (econ/consulting/finance people learning data
science) to help facilitate the learning process?

~~~
dev1n
By _facilitate the learning process_ are you referring to the programming
aspect or the math aspect of data science?

