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MARY COHEN: The University of Chicago's Master of Science
and Analytics program culminates in a capstone project
which allows students to work with an industry partner
to understand a business problem, create a solution,
and implement that solution in a way that
has an immediate impact on that business.
ANIL CHATURVEDI: The projects at the University of Chicago MScA
program take nine months to finish,
where we place special emphasis on the most difficult aspects
in analytics.
ARNAB BOSE: Students sit down with the client
to define the problem and to define
what possible solution the students can
deliver to the client.
Once they have all the data, they
have to spend a lot of time understanding and cleaning
and getting the data ready for the model
that they are going to use.
They have to understand the business requirements as well
as the data side equally well.
You cannot have a very good solution on the data side
without impacting the business.
And conversely, you cannot have a very good business solution
that is not supported by a data model.
ANIL CHATURVEDI: Students work to write up a written report
and make an oral presentation to communicate the findings
from the technical work that they did in language
that businesses can understand.
ARNAB BOSE: In addition to delivering models
and delivering insights, they have
to map that to actionables from a client perspective.
Only then, the insights are actually
useful in the real-life setting, and only
then, it makes an impact on the business.
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OLIVIA FOX: Last year, Goose Island
was challenged with a goal
So to get there, we had to outline
how we're going to invest in our team and the market
MATT CONDON: We have all matter of data.
But the guys in the University of Chicago team
can really make this data sing.
WILL NUNEZ: We had the research design class,
which was a quarter-long class that required us to formalize
the business use case.
And we went through that class with Sema and Anil,
who really taught us the methodology and framework
that we have to take before actually digging into the data.
JON WORTHEY: We're gathering a ton of the data
from Goose Island, which is beer sales, point
of sales information, both from the wholesaler side
and from the consumer side.
But then there's open use data.
That's everything from Divvy bike trips
to small business improvement funds.
So we've really scoured that to get a wealth of knowledge
around consumers in that market.
JAMES BUSHNELL: What did we say?
We’ve got about 2,500 retailers that we're attempting
to do forecasting on.
And so to do time series forecasting on 2,500 retailers
is just not realistic.
That's not a good approach.
So what we have to do is work out
which retailers are similar to each other,
and then forecast on those in a similar way.
MINA ABEIKODOUS: So the industry buzzword
is data science, machine learning, AI.
And we've always talked about that
and how we could fit it in or benefit or add value
to our organization.
SCOTT COFFMAN: While we focused on sales forecasting
in this instance, I think it's very
adaptable to other portions of our business.
So success is how we can adopt the internet and big data
and adapt this to our current business practices.
BOB KNOX: We're defining queries that go to the API
that Twitter has.
We pulled down a set of tweets from the time period
that we want for the products that we're interested in.
Basically trying to get a feel for the relative sentiment
of a given product.
From there, build that as a time series so that way,
we can sort of see if any of those lags
are significant relative to the Scholle data.
ADETOLA ADEDEJI: It's a lot of data.
And then there are a lot of dynamics that comes into play.
Well, based on what you've done so far,
you are limited to what model you can use at what point.
In this scenario, the reality actually
now decides what exactly you want
to use in terms of how to go best in achieving the results.
XIAOLEI ZHANG: And so we first built a linear regression
model based on the keywords and lags that has strong cross-correlation
with Scholle’s tomato sales data.
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BOB KNOX: The point of becoming an expert in this area
is so that not only can you do the work,
you can also communicate the work back and have
it be applicable to people that are in an organization where
they may not be as technical, but they certainly
have to communicate a lot.
JON WORTHEY: It's about just trimming it back
when you're communicating to business stakeholders
because that's really the value that you're supposed
to add as a data scientist.
You know, you obviously have the muscle
through this program that you've developed over the last two
years.
But then when you're communicating
to your stakeholder and saying, hey, this
is what we're trying to do, they might not
be interested in the machine learning techniques,
models like random forest or support
vector machines and the like.
But they'll sure be interested in what
comes out of those models.
So Goose Island would take what we're doing with these models
and say, you know, do we have a different approach around route
to market, around where we deploy our sales force, how
we work with our wholesalers, how we incentivize, et cetera?
So they're able to take what we're doing
and truly make a business impact, which
could be separate from how they're making decisions today.
 So we started this by really just taking
reams and reams of data.
This ended up being 2 million rows of data from 2015
through 2018.
And this was split very granularly
into the different zip codes and the like.
We appended 24 different data sets,
what we're calling the geodemographic data sets.
And these were mainly from the Chicago data portal,
and appended onto--
at that monthly level--
the Goose Island sales.
JAMES BUSHNELL: So the constraints
that we applied was-- this is based on Steve's input.
So we said that 85% of the visits should
be to existing retailers, and then 15%
to the non-buy accounts that we identified.
And so the results of that, we have on the next slide.
So the original approach, the 27,000 barrels,
that would be if you visited, in 2019,
exactly the same retailers as you did in 2018.
Those visits would be associated with 27,000 barrels of sales.
If you visit the retailers that we
think you should visit based on the forecasts,
that takes it up to 36,500.
And if you visit those retailers plus the non-buy
accounts that we identified for you,
that takes you up to 46,700 barrels.
TODD AHSMANN: That's amazing, especially
when you can attach a number to that.
You can get down to this level and show
“We predict to be up 34% if you just follow this route.”
MATT CONDON: Yeah, this is a lot more in-depth.
TODD AHSMANN: I mean, be the hero of 600 wholesalers,
for sure.
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This is something that the beer industry has been trying
to solve for a long time.
People still rely on their gut.
I have 400 employees at Goose Island.
I have one analyst.
We do have a lot of analytics at our disposal.
We just don't have the dedicated time or resources
to really use that information.
So to have a team dedicated to putting this together for us
really helped us out a lot.
SCOTT COFFMAN: And this was-- quite frankly,
with this team and the way this program was
put together, very easy.
It gave us insight and an ability
to tap into some resources we just didn't have.
MARY COHEN: The Capstone Project is difficult.
And it's designed to be rigorous so
that students are well-prepared to solve business problems.
Our full-time students are really
looking for an immersive experience.
And they come away with a portfolio
that they can bring to their first job interview.
Our part-time students often bring projects
from their companies to work on as part
of the Capstone experience.
They deepen their understanding of analytics
and make an immediate impact on their organization.
ARNAB BOSE: Through the Capstone experience,
students are understanding the workflow
and the methodology of how to do an industry project.
And they also learn how to take something
from a prototype to a big enterprise.
