Welcome to the Canvas Data Science Meetup Group.
Happy to have you today, Stephen.
My name's Keegan.
Got into the Canvas space in 2018.
Started out as an analyst at a Canvas testing laboratory,
and then started developing software,
and now do data analytics for everyone from laboratories
all the way to producers, processors, and retailers.
And just here to have this group to provide value,
because I think data science is something
that's in high demand here.
So we'd love to hear about what you do
and what you would like to get out of the group
and what your interests make you.
So I'd love to hear from you, Stephen.
Oh, me, I'm not actually a data scientist myself.
I'm more of a hobbyist.
My main interest is cybersecurity,
which I'm trying to break into.
And they definitely go well together.
Because these days, cybersecurity is so complex,
you need automated analytics to really be able to manage risk.
And the other part of the group, PiData, the part I'm a group,
group I'm part of, their main focus
is diversity in data science.
Well, it's interesting.
And Hello, Geri, we'll give you an opportunity
to introduce yourself here in a minute.
I just want to say real quick, while Stephen brought it up,
I was actually thinking there's probably a good demand.
I don't know if it's discovered yet,
but people will probably find out.
And I think there's probably a good demand for cybersecurity
in the cannabis space.
So I'm sure people that are savvy are investing in that.
So I think there's a lot of opportunities for you.
Because everyone's concerned about it.
Whether they have itemized that as one of their concerns
is another thing.
But I think it's definitely on people's minds.
One of the issues is it seems like pretty much like 99.9%
of developers don't think about security at all.
True.
Well, I don't know if that's true.
But that's an interesting statistic.
But I think it's something that should be on people's minds.
Like you said, it may not necessarily be.
So the cannabis space, it's of concern.
And for example, retailers, they go to great extent
to secure their facilities.
And sometimes they don't think it's there
when people are waxed in other regards in the industry.
So they'd say, hey, we're going to these great extent
to secure our facilities.
Make sure your data facilities are up to snuff.
And you're taking all the necessary precautions
in other aspects of work.
So long story short, I can go on and on about this.
So great to have you here, Stephen.
Oh, yeah.
Definitely.
Hopefully, I'm into the cannabis myself a bit.
That would be why.
So I'm hoping to learn what I can
besides about development and maybe
some other aspects of cannabis industry that I'm not aware of.
Well, without stealing the show, Jerry,
would you be interested in introducing yourself
to the group?
They've got some awesome endeavors.
So here's the opportunity to brag about yourself here.
If you want, Jerry.
Me?
Yeah, then we'll go ahead.
Good morning, everybody.
I'm a marketing guy.
And recently took a boot camp in data analytics
and learned a little bit of Python.
So I know enough to be dangerous.
I am the world's worst typist and a terrible coder.
So when I start to work on a project,
like I'm trying to map some of the data that has been
presented here, and it's just taken me a long time,
but I'm getting there.
And as you were saying, Stephen, there's
an awful lot to be learned about cannabis.
And it's a lot of information and insights
that sophisticated data analytics can provide to the industry.
And I think this is a great place
to be to discuss those ideas and see how we can develop it.
Exactly.
We're coincidentally going to speed up
your endeavors here today, Jerry.
We'll be doing some mapping.
And I can share with you the latest work.
Well, I took a boot camp, and it was very wide and not very deep.
And a lot of it didn't stick.
So what I do is I go back to what I was taught
and try and bring in those techniques
and then just build on it.
So for instance, this morning, I was
doing some restructuring how to geocode the addresses
and learned a little bit.
But it's step by step.
But my biggest problem was when I
was trying to bring in a data set,
and I spelled cannabis with an A,
and I couldn't find my directory anywhere.
And it took me about half an hour to figure that out.
So that's what I mean about my typing skills.
Well, you're going to be well positioned for today,
because like you said, the first step is getting the geocoding.
And then today, we'll show you all the value
that we have from that.
So just a little bit of work up front
can go a long way.
So most of the work.
I just actually was reading an article.
It's still up on my screen here about geocoding, how to do it.
So it's not that complicated.
You just have to know the right tools to use.
There's something in the pandas, geopandas,
that I'm looking at.
We'll play with that later today.
Get to that momentarily, in fact.
So before we do, though, Ryan, would you
like the opportunity to introduce yourself to the group?
And what you may like to get out of the group?
OK, Ryan, feel free to speak up.
It's muted.
Ryan's muted.
OK, 100% OK.
Just feel free to listen along.
All right.
Well, without further ado, let's just go ahead and get into it,
because Jerry's brought up exactly what we're
going to be working on here today.
Cool.
Awesome.
So long story short, I want to start with a question
through the day.
So we keep talking about just cannabis, the plant,
the history of cannabis.
Well, it may, because Washington's quite a luscious
state, but I'm not sure if cannabis would naturally
grow up in the Northeast.
And so I like to think humans can sort of be this useful tool
of the plants, right?
And so I think it was going to be interesting just
to kind of look at where is the canopy in Washington state?
So here's the thing.
One of the things that interests me about Washington state
is it is a major apple producer.
And the Hudson Valley in New York state
is a major apple producer as well, second in the country
to Washington state.
They call this the Apple Valley sometimes.
So I think from an agricultural point of view,
it may be very similar to the environment that
we find in Washington.
And that's something that's always been of curious to me
is where are cultivations located in relation
to other agriculture?
So we're just going to scratch the tip of the iceberg today.
But long story short, we're just going
to start looking at plants.
So we can just look at plants by licensee over time.
So I'll show you what I've been working on here.
And then we can just work with the subset of the data here.
So long story short, I've been working
on augmenting these various Washington state data sets.
So nothing fancy.
They're just large.
And they can really be reduced.
So you've got about 40 gigabytes zipped data.
And I've extracted all of this data here.
And once you've done that, you're
actually looking at around 370 to 390 gigabytes of data,
which is really cool because that's coincidentally
about how big the Bitcoin blockchain is at this moment.
So we basically have the Bitcoin blockchain worth of data here.
Then this is cannabis data from Washington state from 2018
to November of 2021.
So this is just shy of four years of data.
So I'm going to be just slowly whittling through this.
So I was hoping to have the sales items processed for today,
but not yet.
So it just started with the plants data here.
And what you can do is if you only
read in a subset of the data points,
so just to show you some of the data points here,
like for example, from plants, you
have a lot of extraneous fields.
And I was just setting out to count the number of plants
by licensee and by date.
So whenever the plant was created
or when it was updated at last is essentially
what I considered it harvested.
So we only really need a handful of fields here.
Really just need to know who grew it
and when they put it in the ground
and when they last touched the plant.
So created at means when they planted?
That's what I'm assuming.
There are other fields here, like plant created at,
plant harvested at.
But they're not reliably entered in the data.
So as you'll see, there are going to be flaws in the data.
And in fact, the beginning portion
of the data from maybe early 2018
may just need to be discarded.
Because it's basically, you'll see,
it's basically people are entering
their beginning inventory.
And it looks like a large spike in the data.
But it may just be a data entry anomaly.
So you'll see.
But I would encourage you to do your own exploration here.
Because I always think there's a trove of analysis that
can be conducted on each and every data point
if you've got the right question at hand.
So these are just errors in your quiver.
And today, I'm just saying, OK, we just
need three of these data fields.
And so just to open up a terminal here,
you're basically taking this file that's 12.9 gigabytes
of data.
And I've already counted these here.
This is 22 fields.
So if we just use three of 22, we're
only using about 14% of the data.
So we're only using about 1.8 gigabytes of data.
So we can actually just go ahead and read in the entire plant's
database if you have 1.8 gigabytes of available memory.
So you're taking this problem of, oh,
do you have 13 gigabytes of memory?
And now you're reducing that to a factor of 1 fifth, at least,
where you just say, OK, now I just need 2 gigabytes of memory.
So I know I'm going on a tangent there.
But it's useful to find tricks like this.
Because the topic of the day is statistics on big data.
And so the idea is it's tricky just
to calculate simple statistics.
And if you are able to wrangle these big data sets,
then just these simple statistics
can be quite informative.
OK.
So long story short, you read the plant's data in here.
And then we're just going to iterate.
I still have a portion of this data left to read.
But I'll just demonstrate what it looks like.
And then I'll show you the initial portion of the data.
So long story short, we just read in the plants
and just parse this day by day here.
So long story short, let's just look at the data.
So this is sort of the baking part of the show,
where this is how you prepare your ingredients.
And now we're just going to pull the baked piece of bread
out of the oven.
So I'm still aggregating this data.
So I'll send you the complete data set
after the presentation today.
But basically what we get here is
we get a daily count of the plants,
as well as the cultivators.
And what it looks like is not all cultivators
have plants in the ground at all times.
But that's something that warrants more study.
But you can at least get the total number of plants
over a day.
And the numbers we're looking at are the total plants
in the ground on that day.
Is that what that means?
Exactly.
The total number of plants that are active.
And so this is my first crude attempt.
And so these could be any way that people are
keeping track of their plants.
So what would cause a rise of 37,000 plants in one day
and then go back down to almost 40,000 plants
the next day, harvesting, or I don't understand the data.
Well, these were why these first few observations
are a bit anomalous.
Let's go ahead and plot this data
because it is quite interesting here.
OK, so this is stuff you can do here.
We're just going to focus here on the Canvas data science
code.
And I'll post this to GitHub afterwards.
Just still need to clean it up a bit.
So pardon the brim we're doing here.
But let's go ahead and look at these plants here.
So let's see here.
So this is how it looks.
So this is how you go about cleaning the data.
We're just going to go ahead and read in the pre-cleaned data.
And we're just using the test data
because we still need to complete the full data set.
We can at least look at the data so far.
So for example, if we just want to look at total plants so far,
then I still need to collect about a year and a half or so of data.
But you'll see in the first few days here,
just say that we just want to look at the first 40 days.
It looks like there is a bit of data entry going on.
So maybe people are just entering in their plants and harvesting them
right away.
I don't want to conjecture too much because I barely
looked at this data.
This is sort of my first pass at looking at this data.
So as you can see, I'm still collecting it.
I've only collected it through July of 2020 at this point.
So still collecting these.
Still have a little more than a year's worth of data to collect.
So that's just the processing is slow.
And so if you have ways that you can improve this cleaning algorithm,
where I'm basically just calculating total plants by day, day by day,
then you can speed this up.
But for now, the algorithm's slow.
But we can do a lot with this.
And so long story short, we can go ahead and start plotting it.
So that way, we can get a rough idea of what our figures will end up
looking like.
What's the word for it?
We can do a proof of concept of some figures.
And then we can do the final copy once all of this is said and done.
So we'll start mapping here in a second.
Since we already have these daily plants pulled up,
we can actually look at this as monthly in-depth mapping.
We can actually look at this as monthly in-weekly,
because that may, it'll be less static than the daily,
so we can get maybe a better plot here.
So let's try to look at this monthly plan.
So long story short, you can look at the number of plants.
And just wanted to show you that we can actually even group these
by licensee.
So that way, you can see which licensees are growing at which times.
And then we can actually look at the number of plants.
And we've talked about entry and exit before.
And so I think this would be an interesting game theoretical.
Here, I'll pull up the data, and then I'll explain the model.
So we've got a nice panel data set here with some identifiers.
So we've got our panel data going over time.
So we've got time, and the individual dimension is the licensee.
And then we have our time series total plants.
So we can track how many plants each licensee has in the ground
at any given time.
And like I said, this is first pass.
So take the data as a grain of salt, and just
know that things may be not counted right.
So I highly encourage you to look at that algorithm.
This is just my first pass at looking at the data.
So what's cool is we've got this nice identifier.
And so we talked last week about augmenting the data.
So we can augment the licensee data and go ahead and get
the latitude and longitude, which are variables
that Jerry was interested in.
And I realized last week we basically
coded latitude and longitude as strings.
If we code them as floats, then everything goes through smoothly.
But here, just reading in the licensee's data,
which we'll visualize momentarily,
just getting the name.
There's just a couple test cases in here,
but the rest are active licenses.
And then you just have latitude, longitude.
Those were the only variables we needed.
And long story short, you can begin visualizing.
So long story short, we had just our simple panel data,
where we just have, well, that's just our daily data,
which is even more simple.
That's just the time series.
We actually have a nice panel data set here,
where we've got date, individual.
We can augment this.
And so we'll augment this with our licensee data.
We're merging.
So the thing about augmentation is we basically just
need a nice field we can merge on.
So basically, just think about a Venn diagram,
and we're just going to basically merge these two objects.
And so we'll merge on this ID.
And then we get a nice geocoded data set.
And the problem I had last week was all of the geocodes
were strings, and they need to be
floats for all the plotting to go through correctly.
So simple fix, but it threw me for a week last time.
So we've got our data here.
And we can even visualize this as panel data.
So whether you want to group this by month first or ID,
or you could just group this by ID and then month.
Well, maybe you can.
Maybe you can't.
So that was not really our main focus here.
So let's get on to plotting, because long story short,
we've got some awesome data.
Rule number one, look at the data.
So we're just going to start off with a simple map here,
since we've got the licensees data.
So just reading in the licensees again with one more field,
the type, so that way we can look at the licensees.
Specifically, we're going to exclude some of the minor
licenses just to save space on the map.
And you'll see here in a second.
So long story short, the code's boring.
So if you're interested in the code,
I'll let you look through it afterwards on GitHub.
But we're just making a simple base map.
I'm specifying some latitude and longitude coordinates
of Washington state.
And then there are some formatting functions.
But what's cool is we're basically, I'll run it,
and then we can talk about the code.
So basically what we're doing here
is we're putting a bubble at each location.
So for now, we're just going to color code these by license type
here.
And so I'll open this up as an actual figure,
so that way we can see this in full here.
Actually, that's very interesting.
So here we have the first visualization.
And what we were talking about last week
is what's cool about data augmentation
is we can begin to visualize multiple dimensions
of the data in two dimensions.
And so here we're visualizing the geographic dimension,
latitude and longitude, and the type.
So we're able to visualize three dimensions here on this one map.
And there's ways you can make this more complex.
But any thoughts at first, Jerry?
Or anyone?
So the orange is the dispensaries.
Let's see.
Orange will be people who are doing processing.
Processing.
That's production.
So OK.
So the reddish color is dispensaries.
Exactly.
And I think it would be worthwhile to plot major cities here,
because basically you have Seattle, Olympia.
This is Vancouver.
It's interesting that a lot of the stuff is close to water.
Exactly, because a lot of the cities are close to the water.
So in particular, the Seattle, Tacoma.
And you've got the Cascade Mountains
with that big empty area in the center, pretty much.
And then to the east of the Cascades is desert.
Very dry.
It's an arid area.
Exactly.
And it's a good place for these cultivator producers,
so people doing cultivation and production.
And just a few outposts.
So a lot more blue to the east of the mountains
than to the west, it looks like.
Definitely a lot more green, it looks like.
You don't see too much many green dots, the cultivators
here in Seattle.
That's what I observe.
You still see a lot of processors, it looks like.
Or not a lot, but a handful.
Right.
So this is what we wanted to do last week.
So we'll keep trailblazing.
And so I'll get this data shared with you afterwards,
so that way you can actually do this.
Because believe it or not, I think in an old meetup,
we geocoded these at one point.
So I'll share the latest data with you,
because it'll just have the latest licensees geocoded.
If you also want to find the script yourself,
basically I'm just using the Google Maps API
and I'm just pinging them one by one with the address.
So I'm just formatting it street, city, state, zip,
and asking them for the latitude and longitude
and keeping track of that.
But long story short.
That's coming from Google Maps all the way.
Exactly.
And so they have their own terms.
So they've got usage limits.
So you may be able to make so many free calls,
but they generally want you to be passing them an API key.
And they will bill you to a certain extent
if you make too many calls.
But I think data is valuable.
So you can get these latitude and longitude points.
So feel free to use this function.
It generally works with any data as long
as it's a data frame with street, city, state, and zip.
You have to try the Microsoft map?
I haven't, but I've been thinking
about looking at different tools,
because you want to have different options for people.
You don't want to just force people
into this certain solution that may be paid.
I'm sure Microsoft will find a way of monetizing it.
For now, this is just an old function
that we wrote a long time ago.
So I use it.
But we've got the licensees geocoded now.
And so now I was going to show you a novel visualization.
And I think it's pretty cool.
So we've got our plants.
And we've got a location of all the cultivators.
So I figured, OK, let's try to plot even more dimensions
on this map here.
So here, I'll just do it for the first observation.
And then we'll do it for all of them here.
So just going to be looking at the geocoded plants.
And I'm going to just look at them month by month since.
Well, we can do day by day, but month by month
just to shorten the number of observations
for the time being.
And so what I'll show you what we're going to do here.
So we just made this map, very similar code here.
Except now, I'm going to use instead of the hue parameter,
I'm going to be using size s.
And we just say, OK, size is a function of total plants.
I'm scaling it by 10% just to keep the markers
under control.
You'll see here in a second.
It's all just a function of plants.
And eventually, we'd like to put a legend on here.
But I'll just show you the first plot.
Let's make sure we have augmented our data correctly.
Then try this plot one more time.
So here, we're just plotting plants.
And for some reason, it didn't import all of our helper
functions.
For some reason, we needed that one.
Let's try to make this plot one more time.
Cool.
And so I think this is essentially, well, maybe this.
Well, I hope we're getting saved now.
But anyways, we can start looking at more of these plots
here.
And so basically, what we have here
are plants at a particular date.
And so well, sometimes we just have
to power through some of these technical problems.
But I think we've got it mostly figured out here now.
OK, this figure is looking a little better.
So here it is full screen.
So this is actually sort of a misleading month.
So this is a month where we're doing a lot of data entry.
So let's go ahead and let this play forward a few months.
And we can start to see essentially
where the canopy is over time.
So I like to think about it as cannabis
is this biological organism.
And it's found this creative way to habitate Washington State.
It does coincidentally happen to be a lot indoors.
But here this plant found this ingenious or this ingenious.
So you're not differentiating between indoor
and outdoor cultivation?
I am not.
However, I would conjecture the vast majority
is indoor cultivation.
There's a small amount of outdoor cultivation done.
And I know there is greenhouse cultivation done.
But I just don't feel like a bulk.
And this is just my naive.
Would you consider the greenhouse to be indoor or outdoor?
My naive prior is almost all of this is indoor.
I'm sure there are people doing it outdoor.
But it's just a different product to a certain extent.
OK.
Well, so it's hard to maintain the same quality when
you do it outdoor.
You do see people doing hemp in large scale outdoor.
And I don't know.
So I think it's definitely a profitable thing
if you're in the right part of the country.
So just anecdotally, I know I've heard people say that, OK,
a lot of the Californian cannabis is grown outdoor.
But I'm just personally skeptical about places
like Washington State.
And we've got the Massachusetts.
Cold and wet.
Yeah.
I'm just uncertain as to how much is actually grown outdoor.
But that's just me talking.
So this warrants a lot more investigation.
And this would be sort of an investigative project.
But we do have all of the licensees' data here.
So I don't know if they would be thrilled about this.
But you could potentially reach out to these licensees.
Some of them may have websites.
And just ask them, hey, do you do indoor?
Do you do outdoor?
Sort of try to conduct a survey.
That would be really the only way you could really
get that data point.
Nobody collects that information.
Not to my knowledge.
I think it would be a good data point to have.
Obviously, you're in demand of that data point.
Well, and to your point, it's a lot easier
to do quality control when it's indoors.
There's pros and cons both ways.
So the major con to indoors, just the high energy
intensity.
So you're basically replicating the sun with indoor lights.
And the sun is remarkably good at doing what it does.
And it just does it for free.
But at the same time, you are controlling
for things like pests.
You're keeping a really standardized climate.
So it's easy to control.
Because that's what a lot of people say
is the name of the game.
Is climate control or environment control.
Where you just anecdotally people
have said that the more standardized and regular.
What becomes more of a manufacturing product
than an agricultural product in a lot of ways.
Well, I think that's a lot of the ways people are doing
agriculture these days.
They're really methodical about it.
So they're measuring it and just trying
to find out what works, what doesn't,
and just trying to be as efficient as possible.
And you see that happening a lot with Canada's cultivation.
It's just different.
And like I said, I don't want to say this is the end all be all.
Because you do see a lot of people
doing it in Oregon and Washington.
But that's just not captured in this data set.
You were talking about data augmentation.
It would be real interesting to overlay this cultivation.
So this would be called the I-502.
So this would be the recreational cannabis.
It would be real interesting to overlay this
with a map of hemp production.
So that way you can see maybe if people
are doing hemp in different locations than they're doing
potentially indoor cannabis.
Awesome discussion there, Jerry.
Definitely let me know if you have any more thoughts
come to mind.
But basically, I'm just going to quickly start generating
these charts one by one.
And then I'll start to tell you about sort
of the grand scheme of all of this.
So here we are just generating these charts through time.
And we may have to increase the size of these squares.
I kind of shrunk them quite a lot.
So let's maybe even crank these sides up a bit here.
OK.
Then we can just run through these real quick.
Anyone else from the group?
Any questions, comments about sort of the work
we've done here today?
So this is just sort of a first pass
at looking at some plant statistics here
in Washington state.
And so basically, the first thought that came to my mind
is, OK, let's just get a count of the number of plants.
And so basically, the first thought that came to my mind
was, OK, let's just get a count of the number of plants
in Washington state.
And just look at them essentially
in a spacetime dimension.
So we can just look at them in space,
so where they're happening in Washington state.
And then over time, so here we're
looking at them month by month.
And so we can just see how they're doing over time.
It would be interesting to plot this against sales.
Sure.
See how cultivation should lead sales.
OK.
So you're thinking do similar bubbles,
where they're going to take to get from the production
phase to the consumer.
I love how you think, Jerry, because that's basically
just data augmentation on data augmentation
and just adding more and more dimensions to this.
Because if we can add the retail dimension,
then like you said, you can see how
this evolves over time.
And you can see how cultivation will bubble up.
And that may be followed by or preceded by bubbles in retail.
So that's brilliant.
So that will add a lot more, at least another dimension.
More than that, depending on how you classify dimensions.
But it's just another demonstration
about how the more you augment data,
the more you're able to visualize it and essentially tell
stories and inform people.
So just whatever your goal may be with the data.
So right now, we're just doing an exploratory exercise here.
So next week, we'll start diving into some of these sales items.
And so maybe we can try to augment these maps with sales
by retailer over time.
So here are plants by cultivator.
So now we can try to add sales by retailer.
I wonder why you go from 359 cultivators in June
to 46 cultivators in July.
There's two things that could be going on here.
One, I stopped counting it at a certain point.
So I may have just interrupted this routine
while this was mid-count.
So that's one rookie answer.
And the second is there's something going on here
with these total numbers of cultivators.
And total plants.
And so I think it could just be how people are keeping
track of their data.
So it doesn't really make sense for 30 cultivators just
to go without a plan.
So there could be oddities to how these cultivators actually
record their data.
So I'm not ruling that out, that there's just weird things
going on with the way people are keeping
track of these cultivations.
However, I think that's a good point.
I think that's a good point.
People are keeping track of these cultivations.
However, I do actually think this last one,
I think this last chart, you may just
want to scrap this for the time being.
Not to an outlier.
Oh, actually.
Yeah.
So this either was a miscount or there was one period
where the Washington State traceability system sort
of stopped to update and there wasn't much data entry.
And so this could have been that time period.
I don't think it was.
I think this is just, I think I just stopped counting this
halfway because I needed to have some material prepared
for today's presentation.
And so after the meetup, I'm going
to finish running this algorithm.
And you're welcome to run it too.
I'll run it with you once it's finished.
But essentially the idea is you can walk away
at the end of the day with a series of these figures.
And I think it would be cool to create a video audit,
sort of a video of this.
You've got a little animation.
Exactly.
So I was thinking you see things like the trees
are breathing in and out.
Well, this is sort of cannabis.
You can think about this from a biologist's point of view
where you see species migrate and populations wax and wane
over time.
And so I just think it's interesting
that you can see cannabis in Washington state.
You wax and wane over time where in the state it is.
And largely, this is a human phenomenon.
Like I said, it wouldn't surprise me
if cannabis just would grow here maybe in splotches here
and there because it is such a lush climate.
But I would say a lot of this canopy
is because of humans maintaining these cultivations.
So I just thought it was just an interesting observation
that cannabis has just found a unique niche
and way to thrive here in the Pacific Northwest.
Thanks for bearing through all those interruptions.
But I think we got on track there.
And let me know what you think of it.
But I think we're just scraping the tip of an iceberg
of some real interesting analysis here.
So that was just a first pass.
The data still needs to be finished, collected.
Well, the algorithm needs to finish running.
Then I'll share that with you because I
think these are valuable data points to have.
So let me know if you think of the other interesting ways
to augment this data.
I love the sales idea.
So we can add that to the figure.
And let's just keep adding layers.
I think there's a lot we can do with this.
Unfortunately, I guess it's also not all specifically
so new to me.
I don't really have anything to add.
But I'm intrigued enough to continue.
Do you have your meetings at the regular same time every week?
Exactly.
So you can catch the cannabis data science Wednesdays,
830 Pacific time AM, and then 1130
AM Eastern Standard Time.
And yeah, exactly.
I'll get the code posted to GitHub.
So that way, you can follow through there.
And then, yeah, feel free to let me
know if you have any ideas or anything
you want covered in the group.
Because it goes.
You mentioned speed of the code.
I am interested.
There's been a push towards this other language called Julia.
So I'm wondering if it would run a lot faster than that.
I heard about Julia a long time ago.
I haven't heard too much noise.
I don't necessarily have my ear to the ground.
There's a few things that I've been interested in.
I've just been lucky enough that Python
has found a bit of a popularity.
I know it's got its downsides.
But I just say, oh, it's just a good job.
I got to go, guys.
Thank you very much.
See you, Jerry.
But yeah, definitely, we're not set in stone
with any one tool or the other.
So if you have any good tools or uses
or you want to share your work, you're always welcome to.
Because this is a platform for everyone.
So you're always welcome to.
If you did crunch some interesting numbers,
you're really welcome to share them with the group.
Oh, cool.
Yeah, I'll try to make it to next week's one.
And I'll try to find you on LinkedIn.
You can find the TANlytics on LinkedIn.
It's the company I started, Data Analytics Company.
So that's sort of where I spend the bulk of my time.
And they help just get the videos up,
make sure everything stays online and up and running.
So that's sort of the backbone of the group.
But the main presence is the Meetup page for now.
But we've put together some good work on the TANlytics website.
You can actually find the paper from one of our team members,
Paul Kitko.
So he was a regular while he was pursuing his master's
in data science.
He's in hot demand right now.
So he's basically a hot cake since he's
got a master's in data science.
So we were lucky enough to have him write the paper with sales
data in Washington state, which we'll be exploring next week.
So if you want to tease her, check out some of his work.
All right.
Awesome.
Well, keep your nose to the grindstone.
And I hope you have an awesome week.
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