Alright, so let's begin.
So just a little bit of background here, Alice.
So I'll put in the chat.
So we've been working with some of the Washington State data.
So in specific, you can make public records requests and actually get the traceability
data dumps.
So for example, let me find the specific link and I will put it in the chat.
So here you can access, that would be a snapshot of what the data would look like in December.
So when was your group created and what's the goal of the group?
So the group is just to sort of share what we're working on.
So all of us are sort of working tangentially with the cannabis industry.
And so we're trying to do some analysis to just get a better understanding of the industry
and of particular markets.
So it's a group to bring together like-minded people that want to talk about cannabis data.
And so it's actually a pretty open-ended group.
So at the moment, I've just been sharing some of the work I've been doing.
Charles has been doing a lot of data cleaning on some of the data we've been able to come
across.
And then some people have come in that were interested in APIs.
And today I was going to share with you some data that I was looking at in Colorado a few
years ago and see if anyone's interested in that data.
And if so, then it may be worthwhile updating it with the most recent data.
And are you both involved in the cannabis industry?
Yes.
Well, I can't speak for Charles, but I've started a company, Cannelytics, that provides
cannabis analytics.
Primarily, we focus on providing solutions to laboratories.
So we're providing an end-to-end solution for laboratories that need a laboratory information
management system.
And we also provide cannabis analytics to producers, retailers, and consumers.
And then just part-time, I do some data scientist work for a company in San Francisco that does
supply chain management in the cannabis industry.
OK.
And what about you, Charles?
I'm just interested in data science.
And this is a unique data set that not a lot of people have studied.
And so I'm interested in looking at it and seeing what I can find.
Awesome.
Well, speaking of which, let me go ahead and...
Let me share with you some more resources.
So I guess I'll just go ahead and start sharing my screen, and we can kick it off for today.
All right.
So some of the data sources that I've been looking at were...
A while back, I had started writing just this paper, and I was looking at wages.
And I recently saw this post by Leafly.
And I'll also put this link in the chat.
So long story short, it's a post by Leafly.
And they're talking about how there's a large number of jobs in the cannabis industry.
It's a growing field.
And that was an area that I'd looked at previously.
So I thought it would be worthwhile to do some analysis.
So the first place we need is just a nice source of data.
And for no particular reason, but I just chose Colorado.
Their cannabis industry is fairly well established, and they have a fairly good source of public
data.
So I'll put these links in the chat here, but there are some...
There's some good data about the actual number of licensees in Colorado.
It's been legal there for quite a while.
Right?
Exactly.
So they've had medicinal...
I don't want to say how long, but probably more than 10 years.
And then the recreational dates back to really the start of 2015.
So there's more data there.
Exactly.
So just taking a quick look at some of this data, you can actually go to the API docs.
And so this is useful because you can interact with this data in your favorite programming
language.
So they even have all these code snippets down here at the bottom.
I just tend to use Python.
So I'm just going to just show you just a quick example of how to hit this endpoint
in Spyder just to show you how to get some licensee data.
So it's fairly simple.
I'll be using Socrata's development tools.
And then I'm just going to use an unauthenticated client.
However, you can get an API key just for authentication.
Just for authentication, that way you don't have to be throttled.
And then the way you really work with the endpoints is there's a unique ID for a given
data set.
So for example, when we're looking here at this data set, you'll see this ID right there
at the end of the URL.
And then likewise, the same ID.
And then you can just use standard requests.
But in this case, I'll just use the client, and I'm just going to get maybe just 10 observations
here.
So you can make a request.
I'm not exactly sure what type of response object this is.
But we can convert it to a Pandas data frame.
And so then you can look at observations.
So I didn't actually save this data to the GitHub repository, because it does have people's
actual names.
So I guess that's public information, but it's of no interest to me.
I essentially just went and did a count of the number of licensees by county.
I think you get people's city.
So from the city, you can deduce the county.
And then you can just get a nice county count.
There's other fields here that may be of interest.
Not many people fill them out, but there are degrees.
So some people will list their degrees and their
specialties.
So that could be an interesting analysis to look at what specialties and degrees people
in the cannabis industry tend to fall into.
Because there's people from all walks of life in the cannabis industry.
So just to get back on track here, just to show you the actual work I was doing here.
So I'll actually show you the data.
So I did recently...
Sorry, can I ask you in this data set, so professional and occupational license, are
we talking about people who work for a certain business or are we talking about the businesses
themselves?
I'm looking at the data from right now.
So in Colorado, you have to have a license to work in the cannabis industry.
So these are employee specific licenses.
So I think they're tied to a specific person.
So yeah, is there somewhere where it says the company that they work in, if they are
working in the company?
I was looking at that and I couldn't find that because that would be...
Like you kind of hit the nail on the head, that would be an incredibly interesting breakdown
to look at firm size.
Right?
Yeah.
I mean, in the leafy graph that you had, the infographics, it was...
Or maybe in one of your slides, you say you have...
The company has between one and 10 employees or...
Oh, yes.
So now I was going to kind of show you some of the...
This is some of the research that I was doing.
So we're here talking about data science applied to the cannabis industry.
And so the way you can really capitalize and make interesting observations is to combine
data sets.
So you can get pretty rich data out of one data set.
But now if we start to combine data sets, we may have to make some approximations and
estimations, but we can start to get some insights here.
So this was a survey that was done in 2014.
So this is going to be dated.
And plus, I did this research in 2017.
So the things I'm sharing with you are almost four years old.
And so it would be worthwhile...
And that's why I'm kind of working on them is because these are things I did four years
ago and it may be worthwhile to touch them up with current data.
So that's why I'm sharing it with you in case any of you want to work on this type of stuff
as well.
But long story short here, I found this work and wellbeing survey and I'll have to share
this link after the presentation.
But they found this breakdown where you'd see most company...
The majority of companies have fewer than 30 employees, so you would really consider
that a small business.
And then there are a handful of large companies with greater than 50 employees.
But this is probably not the typical breakdown that you would see in a typical industry.
Was somebody going to say something?
But I'm just conjecturing, but I would imagine that in other industries, you may see employees
typically working for large companies.
But that's just a conjecture.
I'd actually have to look at the data.
So just to kind of keep moving on here with some of these other stats that I was breaking
down.
And before I get too far along here, I was just going to basically show you that if you
go here to the GitHub, I've got all these stats.
So here I'll put this link in the chat.
And just to give you an idea of what this data looks like.
This is old data, and so one of the subject matters of the day is refactoring, which is
taking old code or not the best written code and rewriting it, cleaning it up, making it
better.
And in fact, that's often how you wind up with really, really good code, is by just
cleaning up old code.
So here we've got this mess of work that I did back in 2017.
And so the idea is maybe it's time to refactor this, take this code, clean it up, get the
data up to 2021, and it could be quite interesting.
Long story short, we've got some cool data points here.
So remember I was telling you, well, not that I was telling you, you can find these occupational
licenses, this license data here.
But you just have people's cities.
So you have to actually do a bit of legwork to count the total number of employees in
different places.
And in fact, you may even have to go to the archives to get the actual month by month.
So I'll start sharing some of these resources as I populate this to the recent date.
But I'll have to share the links afterwards.
But I've gone ahead here and month by month just created totals.
So I've got total occupational licenses.
There's a bunch of real cool data points.
I probably wouldn't have formatted the keys like this today.
Like snake case would probably be preferred.
But this is what the data is.
And so you've got all the licensees here.
You have all the sales.
And then you have some of these fields that I started to calculate, which are things like
this is sort of occupational licenses per business license.
So we don't know how many employees actually work at each particular business, which would
be awesome because then we could get some really granular stats.
But we can still just see the average number of licensees per license.
And so what you see if you look at that number is you see it just in the early two years
start to rise.
So that's an indication that you're moving from small to large businesses.
But still, you know, eleven employees per license is quite small.
But OK, so we've got some good data here.
You know, so now remember rule number one about data.
Look at the data.
So here is the growth of occupational licenses.
So this is just how fast our license is growing.
And so as you would expect when Colorado first started in 2014, it looks like.
Of course, there's a lot of people entering high monthly growth rate of licenses, and
then that gradually then that gradually diminishes.
And so it would be really interesting to plot this out.
You know, it would be real interesting to plot this out and see what's happening all
the way up to, you know, twenty twenty one.
So to that is sort of.
The objective at hand.
OK, and so.
I was just going to show you a little bit of economics.
I was sort of meaning to boil it down a little better.
But.
I'll touch on it, so don't let this scare you off or anything.
But long story short is I was sort of going to do a little analysis here.
And so.
The way economic analysis really starts is you basically assume that.
Things are produced given some sort of function.
So right now, I'm basically saying, OK, so what's being produced?
It's basically just cannabis flower.
Right.
So that's, you know, cannabis flower.
And you know, it's dependent on something.
Right.
So I would just say, OK, well, that depends on the number of plants.
Right.
So so basically just saying, you know, you know, production, you know, is some sort of
function of the number of plants.
And what's nice is if you take the log of both sides, because you can apply any function
to both sides, then you just get a nice linear function which you can estimate with the regression.
So now you can just do an ordinary least squares regression of the log of flowers or sales
on the log of plants.
And you can, you know, you basically can estimate how.
You know how productive the market is.
And so this alpha is really is basically a measure of how productive the market is.
If it's really close to zero, you're not going to have a very.
It's going to be a really inefficient market.
You're going to have to grow a ton of plants to just produce a tiny little bit of flower.
But if alpha is close to one, then you don't, you know, you're it's a very efficient market
and you're getting a lot of flower for the for the number of plants that you're growing.
So that is the simplest, you know, production function.
And so now we're going to introduce labor, right, because.
You know, plants don't grow themselves.
So here you just have plants and you have flower.
OK, but now we're going to say, OK, we're now we're going to say, you know, we've got
the same thing here.
We've got our production.
It depends on plants.
But it also depends on labor.
So you need plants or capital.
So you know, K is essentially all physical goods.
So just any anything physical required to produce.
And then L is any any labor.
So that is your managers.
That is your you know, just your you know, your standard cultivators, your operators,
all their time and energy, thought power, all that.
And so, you know, we're basically saying, OK, you know, given that and, you know, the
state and then.
You know, you've got just things that are taken is given.
So.
So this is just, you know, your tech.
You know, so that's just your tech.
You know, and that's essentially your efficiency.
So that's basically your efficiency of capital.
And then, you know, beta is essentially your efficiency of.
Of labor.
So that's just sort of the mental framework, you know.
So so that's just the mental framework that sort of show.
OK, so.
That's the framework for essentially why we're going to be plotting the data like this.
And so so this is so basically, you know, this is three dimensions, right?
So this is.
Why your production?
Hey, you know, your capital and L, your labor.
So this is really a three dimension.
You can think about that as like a three dimensional dome.
And so this is just two two dimensions of that dome.
And so here I've plotted.
Occupational licenses, which would be our.
Labor.
Right, so this is basically, you know, LT down here.
So right down here, we've got L. And then up here, we've got our production.
So you know, as you would kind of expect.
The more labor.
That's in the market.
You know, the more production you're going to have.
And it's just sort of. You know, we're just beginning to to sort of form formalize a production
function.
OK, and so why?
Well, the reason why is we're sort of trying to measure this curve here, right?
Because.
You know, we want to know.
Because we want to know, OK, does that curve go like that?
Does this curve go like that?
Because.
You know, right here, you know, you would have like, you know, you know, highly efficient
labor and, you know, down here, you'd have, you know, real low efficient labor and.
You know, essentially what economic theory tells us is, you know, the higher your efficiency
of labor, you know, the higher wage you can expect to get.
And so here's a similar chart with.
The medical industry.
And so as you as you see here, see here, you've got a real steep.
Relation between licensees and retail revenue.
And here.
There's not quite a sharp relation.
You've got fairly, fairly flat relation between the number of medical.
Licenses and then medical revenue.
And so all that would really mean is you would expect that the wage rate would actually be
lower in the medical industry than it would be in the recreational industry.
So that would that would just be a hypothesis that you would you could make.
From looking at this data and, you know, using economic theory.
But, you know, it's nothing more than a hypothesis.
You know, you'd actually have to look at at the wage rates in the in the two in the two
industries.
So and then here's here's just the actual economic theory behind it.
So basically.
You know, economic theory would suggest that.
You know, the competitive wage would be the one where you're basically getting paid, you
know, your marginal product.
So what that says in simplest terms is.
You're going to produce a certain amount of value for your for your work.
You know, no one would pay you more than the value you you generate.
But if they're paying you less than the value you generate in a perfectly competitive market,
somebody else would come along and say, hey, somebody's paying him less than the value
he's producing or she, then I'm going to, you know, pay them just a little bit more.
And so that would just keep happening until you're essentially paid the exact amount
of value that you're producing.
So it's all sort of conceptual.
And, you know, you would expect that, you know, in the real world, you wouldn't expect
that anybody would be getting paid their competitive wage just because of market frictions and
all sorts of factors.
So this is an entirely theoretical wage, but it's just a benchmark that we can just use
to, you know, start estimating.
OK.
You know, OK, we don't know people's wages.
All we know is retail revenue and the licenses.
You know, is there any way we can estimate their wages, you know, given that data?
Well, if you go back to our production function here, so here we have ykT.
Basically we're just going to add, you know, lT to the beta right there.
And then that gets log linearized down here.
And that turns into, you know, beta log lT.
So we still have our linear equation where we're just going to take a regression of production,
which I'll just call sales.
So that'll just be revenue.
So we're just going to be regressing revenue on some measure of capital, which I'm calling
plants and then on labor, the log of labor, which I'm calling total occupational licenses.
So you know, it's all just a crude approximation and just, you know, just seeing what we can
make fit.
It's just interesting to just sort of make this visual conceptual model of the industry.
So we're saying, okay, the wage, that was, we can probably estimate it to be competitive.
Also in the, so this is just more data.
This probably should have belonged earlier.
This is just a breakdown of actual positions held.
Okay.
So we've done the nitty gritty.
Now we want to calculate this.
Okay.
So I'm going to real quick show you how that would be done.
So you're going to import this data here where you've got total occupational licenses.
And we also have total revenue.
So that's basically, this is basically going to be our Y and then this is basically going
to be our L.
And I believe we have the number of plants.
I think yes.
So here I should have average cultivated plants.
So let's, you know, read in that data.
Hopefully everything's there.
Number one rule about data.
Look at the data.
So this is total revenue.
Oops.
Here we have total plants, which will basically be our KT.
So we've got our YT, KT and then our LT.
And so we've got our total labor.
Also and this is what I was wanting to talk about, refactoring.
This is old, bad code that needs to be refactored.
So I wouldn't really actually recommend labeling your variables like this.
So like for Python, you know, you should probably go, you know, total labor.
So this code needs to be refactored.
So that is the name.
That is the buzzword of the day.
And so this code needs to be refactored.
Remember earlier I said it's real interesting to supplement your data with external data
sources.
Well, here I'm actually using the Federal Reserve's thread API.
And so I'm not certain I have this.
Okay.
So we'll need to install the thread Python API.
And so let's actually just, you know, see if we can't do that real quick.
So I think we can just do...
And so what I'm using here is basically the Federal Reserve has, you know, this would
be the go to place if you just need monthly, yearly, you know, potentially even more frequently,
but typically the not so frequent series.
So you know, anything from, you know, so this is where a lot of economists get their data.
Honestly I probably under utilize it because there is a bunch of awesome data here, but
essentially right now pulling the...
Right?
Because we're estimating the wage rate.
So it wouldn't be any fun to just pull the average wage data.
But basically what I'm pulling here is just the...
This is just the average number of hours a week that somebody would work.
Because remember earlier I said, oh, I'm just in a conjecture that they work 40 hours per
week.
But then I started thinking, you know, that's not so realistic.
And so what you can do is you can actually, you know, you can actually get the average
hourly work week.
So you know, so like you just slowly, you know, getting your approximations closer and
closer to realistic.
So as you see, it's not, people aren't working 40 hours a week on average.
It's typically, you know, it's close to, you know, 30, 33, 34 or so.
Okay.
And so this is old code and let's see if we can still get this to work.
So it looks like we need numpy.
And we need stats models.
And so essentially what I'm doing here is just getting everything ready for this regression.
I'm just getting the log of y, the log of capital, and the log of labor, as well as
a constant, just to represent technology.
And this is where we're going to start off-roading a little bit.
I need to probably touch up this code.
So I'm not even going to promise that it's going to work here, but we can at least give
it a shot.
See it was even written in Python too.
So we're going to do a regression here.
Okay.
And so remember, we barely even have any data.
Still we have some data.
We can get a lot more data.
So we only have, you know, 29 observations.
So it would be much better to run this through 2021.
And look, we're estimating these exact things.
So we estimated our constant.
So that's, so right, so we just estimated that term.
So we just, we just estimated that, this whole term.
And we estimated x1, which is alpha.
So we write, oops.
So we just estimated alpha.
And we estimated that to be 0.28.
And then we estimated beta to be 0.0684.
And if you look at this, if you add these two coefficients together, well, it's not quite
one, but essentially economic theory would suggest that alpha plus beta would equal one.
So we're obviously leaving some explanation left on the table, right?
There's still about 10% of efficiency that's not really being explained here.
But, you know, we're still explaining, you know, a lot of, you know, alpha and beta,
you know, but they're close to what they should be, you know, theoretically.
And so, so remember, so this is just a measure of efficiency.
So remember, the closer to one it is, the more efficient it is.
So here we've got fairly efficient labor and then capital is not as efficient.
And remember, if we go back here, so we now have beta.
Remember, so beta is, you know, remember beta is like about 0.6 or so.
So now we can actually calculate the competitive wage.
So now at any given point in time, we can say, okay, what is the competitive wage?
Right?
So it's just going to be the total amount of sales divided by the total amount of labor,
you know, times 0.6.
So we now have the competitive wage or an estimate.
So this is, let me really benchmark that there.
This is, you know, essentially, you know, a complete estimate, you know, I mean, keep
in mind all of the assumptions we've made along the way.
So we first assumed that production is a function of plants and labor.
Obviously, production is more than a function of plants and simply measuring labor's total
occupational licenses is also sort of a big mental stretch.
So we did some mental gymnastics there.
And just in fact, the assumption that this is what the production function looks like,
that's also an enormous assumption.
And then we assumed that everything's perfectly competitive.
And you know, obviously, you know, there's a lot of factors, you know, that come into
play there.
And so the long story short, lots of considerations, but we're estimating, you know, a competitive
wage, you know, is fluctuating somewhere between, you know, $9 and $11 per hour.
And so, you know, like I said, this is an estimate.
And we actually can put confidence intervals on that.
And I think I've done that.
Let's try to plot this all in one.
So hold on, I'll be bringing this home here.
All right.
And so here, you know, you could plot this in a more fanciful way.
But here, we're essentially saying, OK, you know, we think, you know, the real wage, you
know, could be anywhere between, you know, $4 and, you know, $18 or so per hour.
And so, you know, with our best guess at around, you know, $10 an hour.
And so, you know, this is sort of bringing, you know, bringing it home.
So basically, given, you know, all we were given was this data set here, just the Colorado
data publicly available, you know, as well as, you know, the Fred API.
We were able to create monthly totals.
Then we were able to use economic theory to estimate what a competitive wage may be.
And then we estimated it and then we plotted it.
And now, you know, now people can know that, hey, this is what, you know, this is what
the competitive wage is in the cannabis industry.
And I mean, if you're looking at like a competitive wage of, you know, up to, you know, $18 or
so an hour, well, that may be why there are a lot of legal cannabis jobs, because you
have a high, a high competitive wage.
So, I think that brings us to the end of the presentation.
I kind of wanted to save a little bit more time for questions, but, you know, that's
some of the work I'm doing.
And now the task is to rinse and repeat with all the data up to 2021.
So, thanks.
That was really cool.
But I have another meeting I have to go to.
Okay.
So, I'll see you next week.
Definitely.
But I'll go ahead and conclude here and I'll get this recording up, get some of the material
up.
And I know it was a lot to take in, but it was even a lot for me to take in.
I've sort of got to get refreshed on it.
But, you know, like I said, that's what I'm working on.
It's got a lot of cleaning up to do.
But there's some interesting insights there to be had.
Yeah, definitely.
Thank you.
Thank you for presenting that.
You're very welcome, Alice.
Yeah, there you go.
So, thank you for coming.
I hope you got something out of it.
Do you have a little bit of time for a question?
Definitely, by all means.
If you were in, I don't know anything about economy.
So that was a new piece for me.
But if you were in a market where things are stabilized and you don't have that many more,
like you have more license or less license, like it's very thin variation.
And let's say the revenue is more or less the same.
And in that plot, the reason you were able to plot revenue versus occupational license
and to have this long line is because the market had been growing rapidly, right?
Exactly.
And so if you were in a stable market, you'd have a much smaller plot, per se.
So, exactly, and you hit on something interesting there.
So with statistics, variation is key if you're trying to get some good measurements.
So it will be interesting to see.
And that's why it's so worthwhile to extend this out to the current day is, okay, did
the occupational licenses, did that level off?
And if so, what's the effect on the market?
Did the market keep growing?
Because if the market keeps growing and then the occupational licenses level off, you'd
expect those people maybe to get paid, maybe get paid more.
Maybe they're becoming better at their jobs, becoming more efficient or something like
that.
Yeah.
That's interesting.
Yeah, that's what I'm thinking.
And so, yeah, so it's exciting.
And so I'd wanted to extend this out to 2021 for today, but there's a good bit of data
wrangling ahead.
So that's why I wanted to go ahead and share what I had with you today.
And the data that you had for 2014 and 2015, you had in a spreadsheet.
What was something that you got from a different data source?
And then the links you shared with that.
So you see the GitHub in the chat there?
Yeah.
So I'm so there is an Excel spreadsheet there.
You may have to essentially clone the GitHub if you want.
I could send you my email afterwards and then essentially just email you that data set if
you would like.
Yeah.
For me right now, it's more understanding kind of like where you so I see the Excel.
I mean, I see this CSV file on today's presentation data directory.
But you're saying that to get data up until 2021, then you'd need to do like get the data
actually from those websites.
Exactly.
And so here, let me put that in the chat here.
But essentially, when I did it, I was looking at archived Excel versions.
You may be able to do it with that API I was sharing earlier.
But now that I'm starting to think about it, I think that may just be at least for the
occupational licenses, that may only be a current snapshot.
So I may have to share with you the links to the actual archives.
So I actually do that is actually sort of a gap.
So I realized that in my preparation today that I was missing that data source.
So I still have to scrounge that up.
But I'll send that to you when I get it.
Because I've got it somewhere.
I've got the data.
I just need to find out where I got that online again, so we can get it through 2021.
And on the GitHub, you had a bunch of handouts.
What were you wondering?
Sorry, there's a little...
I hear myself on your end.
Sorry.
Ah, here.
I can mute myself when I'm speaking.
I was saying in the... so I'm on the March 10 repository of your GitHub.
And there were a bunch of handouts.
And I was curious about mapping basics with bucket.
Like you put the links to articles.
What was the idea for you there?
Oh, yes.
So part of what I'm doing here is I'm just sort of keeping track of just interesting
data science slash cannabis data that I come across on a week-to-week basis.
I don't know if it's helpful or not.
But I just kind of thought that I just was starting to just accumulate things for the
group.
And I just kind of say like, hey, these are things I found in the past week.
I think they're interesting.
They're worth looking at.
I'm just going to share them with the group because I've got, you know, 1,001 pies in
the oven.
And I may not get to all these awesome ideas and projects.
And so I found that instead of just leaving them to get dusty, I'll just share them because
like, for example, Charles was finding some use for the data.
And so instead of them just getting dust, I'm just going to share them.
And I'll get to them eventually.
And then if you want to poke around at them for the time being, have at it.
So that's great.
So you have past week's presentations in that GitHub.
So the actual videos, I'm still uploading online.
So I'm going to I'm basically creating a web page where I'll just have all the video archives.
Like I said, it's on my to do list.
And in fact, it's on my today's to do list.
So you should check your messages on Meetup and I'll make sure to I'll send you the links
because those will be coming out the pipeline real soon.
Yeah.
Do you have some time to talk just in general about data science?
I don't know.
I don't want to take too much.
Oh, I definitely do.
I'm always happy to talk about data science.
Would you want to like schedule like a one on one?
Maybe.
Maybe it'd be better if you like you have it in your schedule and we can we can chat.
I'm an aspiring data scientist.
Awesome.
I'm pivoting from physics research.
And I've enrolled into a data science boot camp that I'm doing in a couple of months.
And one of the things that they ask us is to build a project.
And so I'm kind of looking at different things that like different data sets that people
haven't necessarily looked at a lot.
And kind of his data seems like really rich.
And actually, I don't know that that many people have looked at it.
And so I'm thinking that that could become my project.
If that was the case, then I would work on it probably like 10, 20 hours per week on
it.
And so I'm trying to see whether it's a good opportunity for me to start getting into.
Definitely Alice.
Well, I can definitely point you in the direction of some good resources, because like you said,
it may be unparalleled, the amount of public data, because very few industries are actually
regulated on such a granular level.
So they are recording data at every stage.
And the public has kind of opted towards a public data approach towards cannabis, because
you just kind of want to let people know about it, know that it's going on, measure it.
And it's new, so people were able to set up, in some cases, APIs.
So there are some awesome sources.
And like you said, it's basically a firehose of data.
And people are just scrambling at it.
And so there's a lot to be uncovered.
OK.
Yeah, yeah.
It's really, really exciting.
I'm going to try to come through a little bit of your GitHub repo for the past presentations
that you've done, kind of trying to understand.
Right now, I can see there's a lot of data.
I'm still a little bit unclear on what are the most interesting questions to focus on.
And I guess that's your real house.
Ooh.
Well, the warning, the repository is a bit of a mess right now.
It needs a bit of cleanup.
I've been just posting things for the time being.
But if you'd like, I could think of some things.
And then when we speak, we could hammer out some good projects that you may want to poke
around at.
That sounds great.
Do you know what your availabilities are in the next week or next week?
I'm always available afternoon.
And then on Fridays, I'm usually wide open.
So how about tomorrow afternoon?
Definitely.
OK.
Do you have a time that's best for you?
2 PM.
And tomorrow is Thursday.
So I do have an engagement at 2.30 on tomorrow.
So if we can do Friday.
How about I'm going to send you an email and you think of the best time for you.
I'm going to give you my time windows when I'm free and then you pick the time.
Awesome.
All right.
Well, this is exciting, Alice.
It was awesome meeting you.
Can you put your email in the chat?
Yes.
So you can just reach me at just Keegan Ski at Gmail.
All right.
Well, it was great.
I wanted to attend this like, I don't know, three, four weeks ago and I couldn't.
I'm really glad I got to do that.
Well, today ended up being an interesting presentation.
There was a lot in there, but I thought it was an interesting one.
But I think it still needs to get cleaned up.
Well, you get the content and then you had the data science and the coding.
And so that was that was a lot to put in like one hour.
But I definitely like the approach and like knowing a little bit more background about
like the economical science behind it.
So that was really interesting to me.
Oh, well, all right.
I'll send you an email right away.
Nice.
I'm glad you got something out of it, Alice.
So I'll look forward to speaking with you.
All right.
Bye.
Have an awesome day.
Thank you.