
Lectures in Quantitative Economics as Python and Julia Notebooks - westurner
https://lectures.quantecon.org/
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evrydayhustling
It's amazing how we are watching use cases for notebooks and spreadsheets
converging. I wonder what the killer feature will be to bring a bigger chunk
of the Excel world into a programmatic mindset... Or alternatively, whether we
will see notebook UIs embedded in Excel in the future in place of e.g. VBA.

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b_tterc_p
That’s not a bad idea. Spreadsheets are pure functional languages built that
use literal spaces instead of namespaces.

Notebooks are cells of logic. You could conceivably change the idea of
notebook cells to be an instance of a function that points to raw data and
returns raw data.

Perhaps this just Alteryx though

~~~
20190205
This is brilliant.

I'm picturing the ability to write a Python function with the parameters being
just like the parameters in an Excel function. You can drag the cell and have
it duplicated throughout a row, updating the parameters to correspond to the
rows next to it.

It would exponentially expand the power of excel. I wouldn't be limited to
horribly unmaintainable little Excel functions.

VBA can't be used to do that, can it? As far as I understand (and I haven't
investigated VBA too much) VBA works on entire spreadsheets.

Essentially, replace the excel formula `=B3-B4` with a Python function
`subtract(b3, b4)` where Subtract is defined somewhere more conveniently (in a
worksheet wide function definition list?).

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garrettdc
You can build user defined functions in Excel with VBA as well as with Python
through something like xlwings. One of the issues that I ran into with xlwings
(or any third party integration into the Office suite) is portability between
users.

The ubiquity of Excel is both a blessing and a curse in that everyone has it,
so everyone uses it, regardless of whether or not it is the best tool for the
job.

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rmbeard
Google Colaboratory is now Ubiquitous in the sense you use the term, as is
Microsoft Azure Notebooks, so the Ubiquity argument is no longer unique to
Excel. The big argument in favor of notebooks is transparency and the breadth
of tools that they can make use of. Economists will increasingly move away
from Excel as the QuantEcon website demonstrates. Perhaps accountants will
still uses spreadsheets, after all they invented them, but it's unclear why
anyone else really needs them when there are better tools available.

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projectramo
I'm jealous of present day students.

I wish I had these tools when I was a student (lectures laid out as notebooks
that you can interact with to see how the graph changes).

Of course just reading through or listening to clear explanations is still
key.

~~~
albertshin
Hi, present day (grad) student here that has been seeing this change happen
gradually over my academic career. honestly there have been times I wish that
technology stayed out of education because I feel like that the clear
explanations have disappeared in exchange for cool graphics or videos (maybe a
budget reallocation to the design/graphics team on the publisher's side?). or
a related thing is I've noticed in class discussion happens less as slides
have replaced the whiteboard/chalkboard as the class speeds through pre
written formulas or texts. overall, perhaps there's a case for more quantity
of info being relayed thanks to tech but I feel like quality has suffered as a
result.

of course ymmv according to prof and institution

~~~
sinuhe69
The problem is not the tech but the learning style. With interactive notebooks
and ton of other materials available, students can participate in pro-active
reading before the class so the actual class time can be used more for
discussion, not just lecturing. Sadly, most students and professors still do
the old way of passive listening.

~~~
4thaccount
I can't possibly see how any student in certain subjects like engineering can
do much reading ahead. I listened to the lecture, tried to study notes, did
insane amounts of homework which always took hours, worked on projects, and
then had to study for quizes and tests. I would've loved to check out a
chapter before class, but what little time I had was for sleeping, eating, and
a little socializing. Granted there are some people that are way smarter than
me or especially if you had an easy program you might have been able to read
ahead.

I like the concept of Notebooks a lot, but you have to be careful that
students aren't getting slightly flashier presentations that come with
confusing installation woes.

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westurner
There are undergraduate and graduate courses in each language:

Python version:
[https://lectures.quantecon.org/py/](https://lectures.quantecon.org/py/)

Julia version:
[https://lectures.quantecon.org/jl/](https://lectures.quantecon.org/jl/)

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kaffee
Does anyone else find it strange that there is no real-world data in these
notebooks? It's all simulations or abstract problems.

This gives me the sense, personally, that economists aren't interested in
making accurate predictions about the world. Other fields would, I think, test
their theories against observations.

~~~
wjnc
It's an educational course in quantitative economical methods. Fitting real
world data is messy and would probably distract. There obviously is overlap
with metrics but as an undergrad course I'd separate this too. They do have
ample links to scientific papers that do use real world data. There's a pages
long list of references [1]. Do check them out if you're into economical
science.

[1]
[https://lectures.quantecon.org/jl/zreferences.html](https://lectures.quantecon.org/jl/zreferences.html)

~~~
kome
yeah, doing real empirical stuff is messy. but that's exactly why people
should learn on real data, and not in a sandbox.

~~~
wjnc
I let my kids practice with hammers, nails and wood (tools / supplies) before
I introduce building a piece of furniture (the educational end-goal). These
models are the tools of the trade.

I agree with the sentiment that if work is messy, teaching should have messy
as well. But not when you're starting out with new tools.

~~~
rezahandzalah
+1 for the kids tools. To extend, I let my kids practice hammering first, then
sawing, then some other skill. Learning to work with messy data can wait until
you are used to the new tools.

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bigmit37
Just gave it a quick look— Loaded with lots content.

Thank you so much for sharing.

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abdullahkhalids
Does anyone have suggestions on how to go about monetizing an interactive
course like this? And any comments on how successful it could be?

~~~
rmbeard
Look at the data camp model.

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thoughtstheseus
Does anyone have a sense of how useful an economics background is for data
science? I've heard mixed things.

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logancg
I'm econ undergrad -> DS -> Machine learning. Econ is very useful for data
science if you focus on the right subjects: statistics, math, and experimental
design. You get all the hard skills you need to interact with data that a
statistician or computer scientist gets, with the (significant, unique)
benefit of learning how to ask the right question or design the right
experiment given what is likely a messy, weird, social scientific question.

On the other hand, if you don't do any quantitative, empirical, or
experimental economics -- i.e. you only do theory or political econ -- then
you won't pick up these skills (as much).

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nwhatt
Might as well call intro to machine learning, many models overlap.

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zwaps
Probability theory, optimization, statistics and so forth do not differ
between economics and computer science, so it makes sense they are the same.

You would see a difference in that these sort of models are used for causal
inference and counterfactual analysis, whereas Machine Learning is mostly
predictive.

That being said, Machine Learning is starting to apply methods developed in
econometrics and/or stats, like GMM and Time Series methods. For example,
Long-Term Memory models are quite recent additions to Machine Learning. The
short-memory process restriction of autoregressive models has been worked on
since the early 80's.

