Episode 8 features Peter Yacobucci, associate professor of political science and public administration at Buffalo State College. Peter walks us through how he collects and analyzes data and how his results have influenced the decisions of future court rulings. Tune in to hear how his round-about pathway lead to a career in analytics and his advice for how to be successful in your career.
(funky music) - This is Buffalo State Data Talk, the podcast where we introduce
you to how data is used and explore careers that involve data. In today's episode, we
talked to Peter Yacobucci,
Associate Professor of Political Science and Public Administration
at Buffalo State College. Peter walks us through how
he collects and analyzes data and how his results have
influenced the decisions of future Supreme Court rulings.
Keep listening to hear the
advice that Peter has learned through his path to a
career in data analytics. Hello, and welcome back to another episode of
Buffalo State Data Talk. I'm your host, Heather Campbell,
and we appreciate you
joining us for episode eight. Today, we'll be talking
to Peter Yacobucci, Associate Professor of Political Science and Public Administration
at Buffalo State College. Thanks for joining us today, Peter.
- So happy to talk to you. - So as an expert in constitutional law, who's extensively studied
the Supreme Court, can you give us an overview of the work that you do in this area?
- So basically what I try
to do is look for patterns in decision-making that
explain logical inferences. So I'm studying both the
members of the Supreme Court and other members of lower level courts, trying to determine why
they come to the conclusions
that they do. I do this through data analytics. Most previous legal research was done by parsing the words that are in opinions. While I do that,
a lot of what I do is I looked at patterns of justices over time in
their decision-making, assuming that generally they
stay consistent over time, and seeing if I can determine patterns to not only figure out
why decisions were made,
but make predictions
about future decisions that may be coming before the court. - So the data that you collect, what information are you collecting and where are you getting it from?
- So the information
I'm collecting generally is the type of case that's being decided. Is it a case about civil rights? Is it a taxation case? Is it a courts law case,
or one of the different areas of the law? I'm looking at who the litigants are, or is the litigant a
sophisticated, big firm, is the litigant an average
person like you or I? I'm looking at what's the
ideology within that opinion.
Many court cases have a liberal position or conservative position. Many court cases do not have a liberal or conservative position, you know, think of a minutiae tax case,
an interpretation of tax law. And then I'm also looking at, then, each of the justices, okay? So I'm looking at a justice. Let's say we stick to the Supreme Court,
Justice Gorsuch is nominated and approved on the court by President Donald Trump. President Donald Trump was a Republican. Gorsuch such was an active Republican. Does that partisanship make a difference?
Does their ideology expressed from previous cases, or determined from previous
cases make a difference? And many other factors. I'm trying to collect
as much data as I can
to parse out the impacts of, is it ideology that's
pushing this opinion? Is it legal scholarship
that's pushing this opinion? Is it background? What is it that causes this justice
to rule this way on this case. - So once you have, and you've collected all that
data and got it together, what kind of programs, software, spreadsheets do you use
to organize that data
and then analyze it? - I found the easiest thing for me is I simply just use an Excel spreadsheet to create the dataset. Once I have the data set established,
then I import that data set
into a statistical package. Usually its data. There's a very commonly used Social Science Statistical
Package, or sometimes SPSS. And then I'm running statistical models.
I'm trying to find causation, okay? And using statistical logical inference to determine the causation, does this factor affect that? Does this factor affect that?
While holding all other factors constant. That's what I do a lot
in my statistical work. - Could you maybe tell
me about a conclusion that was made from some of
the data that you analyzed, that you found to be
interesting or beneficial.
- One of the cases that was
decided in the past decade was a case that now we refer
to as the Hobby Lobby case. It has a much longer name, but involved the craft store Hobby Lobby, and Hobby Lobby is owned by a family
that have conservative Christian beliefs. And one of the parts of their conservative Christian beliefs is they don't believe
in same-sex marriage. They don't believe in fully
recognizing the rights
of individuals that are gay,
lesbian, transgender, queer, any of those individuals. That case came before the Supreme Court and the court was forced
to look at the rights of the individuals, the
owners of Hobby Lobby,
owners of businesses, to have religious free exercise rights and free opinion, religious opinions, which is, of course,
in the First Amendment, against the right of individuals
to be treated equally
in our society without undue
or salacious discrimination, bad discrimination. And so the court had to decide that, and what the court came
to their conclusion was, an individual, the owner of
Hobby Lobby, in this case,
can discriminate against individuals that have a a different sexual orientation than a heterosexual orientation, as long as they had a sincerely
held religious belief, and they did in this case.
So coming out of that opinion a number of years ago, lower courts then had to interpret opinion to many, many cases of employment, and serving customers,
and whether an individual, you know, a gay couple that wants to get married can they get a cake and
a certain cake bakery, and there's famous cases like that. Last year, just this last summer,
there was another decision
made called the Bostock case. Again, court opinions are titled based on the name of the litigants, not about what it's about, which is kind of difficult,
but anyways, in the Bostock case it was a challenge to an employer being able to fire someone solely because they didn't approve
of their sexual orientation. In between that time, the Hobby Lobby case
and the Bostock case, myself and a number of other colleagues did a large dataset of
all these other cases. And what we found is these lower courts were misinterpreting Hobby Lobby,
and making Hobby Lobby a bigger
ruling than it should have, which allowed employers to
have essentially carte blanche. They can hire and fire anybody they want for no other reason
except their orientation. That information made its way
into the briefing materials
for the Bostock case, and became one of the fundamental parts of the Bostock case ruling, that if you are discriminating
against an individual and employee solely because
of their sexual orientation,
that's a violation of the 14th Amendment Equal Protection Clause. And it greatly limited
the impact of Hobby Lobby, the original case on
employment situations. And so that's an example
of research that was done,
you know, by academics, including myself, having an impact on the
court-making decisions going forward. And, you know, it takes years. It takes a long time to do that,
but it was a fascinating process to watch both the original case, how it was interpreted in lower courts, how it was misinterpreted in lower courts, and how us, as researchers,
using data analytics, to then reinform the Supreme Court as a similar issue came up years later. - That's really
fascinating and really cool that you were working on that.
That's amazing. So I want to talk a little
bit about your background and how you ended up where you are now. So you received your
PhD in political science from the University of Arizona,
and you also got a master of science in public policy from Georgetown. Why did you choose to get these degrees, and how did it affect your career path? - As a caveat, let me say,
I'm not the best example to follow. And I'll tell you why. So I graduated from high school and then went on to college, and I was a sociology,
I had a double major in
sociology and political science. And what really fascinated me about political science was
the public policy side of it, is how do we solve social problems? You know, so how do we bring
down the level of poverty?
How do we create a cleaner environment? And that's a policy area. So I thought, okay, I need the
skills to be a policy analyst as someone that could do that. And Georgetown University was
the best school at the time.
It still is one of the
best schools at the time, the McGovern Public Policy Institute. And so I went there to get my degree and almost as soon as I got there, because I didn't come from a
wealthy family, I needed a job.
If you've ever been to
DC, DC is very expensive. And I was able, working
through the school, to become a a junior policy analyst in the Department of Justice, just, you know, in the middle of a DC,
which is another reason
it's great to study in DC. And I worked with them
as a policy analyst, and they offered me a
position before I graduated with my master's degree
as a permanent position, which I took,
but I said to them, I said, "I'm working to in
the Department of Justice on policies that affect field
level implementation of laws." You know, so things that
law enforcement officers, federal law enforcement
officers are doing.
I said I need more information on that. I need that end. I said, "Can you send
me out to the field?" Like, make me a law enforcement
officer out in the field? And they said, sure.
And so I went through
law enforcement training that they send all the FBI
agents for those things through. I was strangely assigned
to here, to Buffalo, as my field office, and worked here for several years,
and found that I was
excellent at policy analyst. I was not the greatest
law enforcement officer, mainly because I was
too trusting of people. And I thought, well, I can become less
trusting of people,
or I could do this a different way. And I didn't want to become
less trusting of people. So what I did is I, on a whim, I applied to the University of
Arizona for their PhD program in political science.
And I think it was a cold day in Buffalo. And I said, "Where's the warmest place in the United States?" And I always wanted to visit the desert, let me apply to Arizona.
And that's why I say
that is not the right way to pick your graduate school, because of the weather. Please don't do that. I knew nothing about the school.
I didn't know anything
about the department, but then they accepted me and
they gave me a scholarship. And once they gave me a scholarship, I couldn't say no. Again, as I said, I didn't have any money.
So I went to Arizona. I absolutely loved the desert. I loved it. Went and was at Arizona for, I think, six years, seven years.
Almost finished my PhD. And then I got a job at
a little school in Ohio, a Catholic school in Ohio. And while I was working there, in fact, before I started working there,
the first day I was introduced by the President of the School, not as the new political scientist, I was introduced as the new
director of the pre-law program. No one had ever mentioned that to me.
Not in the interview process, nothing. And I said... Afterwards, I went to the president and we ended up becoming friends. He was a really good guy.
He's since retired. I said, "You know, you
never even mentioned that in the interview." He goes, well, if I
mentioned it the interview, I figured you wouldn't take the job."
And I thought, well, that's
what a president would do. And so because of that, I began to research
more and more into law, so I could actually know
what I was talking about as the pre-law director.
And I worked with
several local law schools including Akron, Cleveland
State, Case Western Reserve. They were all within an
hour of where I was living, and became more and more
fascinated by the law, more and more fascinated
with the process of making legal decisions of how does a justice
with all this information determine the right side and
the wrong side on a case. And that got me interested. And after about five years working there,
the University of Akron came to me, 'cause I began to know
their admissions people, being pre-law director. They said, "You know,
we do a night program. Would you want to go to
law school at night?"
And I agreed to do it. And I went two years at night, and then the job opened up here, and my thought was, well, I'd love to get back to Buffalo.
Buffalo is my hometown. I grew up in Wales. And so we took the job here. My thought was I would
finish my law degree at UB, not knowing that UB does not
have a night law program.
They only have a day law program, and I was working full-time at Buff State. So I didn't end up
finishing my law degree, but I started here 10 years ago, and I've been happily employed
by Buff State ever since.
- You know, I hear a lot of stories about people who grew up in Buffalo. They leave for school and
a lot of 'em come back. What made you decide you
wanted to come back to Buffalo? - When I left for DC in 1991,
I never thought I would come back. I'm like, "I'm done with the cold. I'm done with blizzards. I'm done." Like, I wanna go where it's warm.
A couple of things about Buffalo, okay? Buffalo is incredibly
cheap to live in, right? And until you've lived in New
York City or Washington, DC, or almost anywhere else in the country, you realize how much
cheaper it is to live here.
You can, you can have a job, buy a house, afford to buy things for your family, actually go on vacations. Whereas in other places, you know, it's so expensive now to live there.
So that was a big pulling point. The other things that
really made me like Buffalo, and for those people that
are not from Buffalo. Buffalo was originally designed to house about a million people.
Right now it houses well
less than 250,000, right? Which means there are no traffic jams. There are not, really, lines that you ever have to worry about. It's not crowded.
The parks aren't overrun. It has great infrastructure. It has wonderful arts communities
and cultural communities. It has fantastic food. If you like food, Buffalo is ideal.
And more important than all that, to me, for what I do, Buffalo has the most
thriving civic community of any city I've ever lived in. And I've lived in a number of places,
where people get together they discuss politics, they discuss books. They are constantly meeting. We have festivals. So Buffalo is a great place.
And if you've ever lived
in Buffalo in the summer, it has the most ideal weather
in the world for summer. Winters are rough, summers are fantastic. - Oh, the winters aren't
that bad (laughs). - No, they're not that bad.
But as someone who lived in
Arizona, they're not Arizona. - That's definitely true. - So you started off your
career in political science and then you moved to more
law and Supreme Court. So how exactly did data
science fit into all this?
When did that start and how did you move into that area? - Oh, that started really young. I've always loved math. Loved it. And eve, when I went to school,
I was very good at math.
Math came very, very easily to me. And so when I went to college, I focused on, I took a lot of classes in
statistics and data processing, I was one of the very early people
to start computer programming back when you actually used to code in BASIC and DOS and
those types of things. And it just sort of came together. And so then when I went to Georgetown
to get my master's
degree in public policy, a lot of studying public
policy is numbers, is, you know, how do we
know if a program works? Well, we've gotta collect
numbers on what was before, what was after and then
use statistical inference
to see did it make a difference? Was it worth it? Was it cost benefit? So I took a lot of classes at Georgetown, based in numbers and
quantitative analysis. And it's something
that I found easy,
enjoyable, understandable. And what I found, especially
over the last decade, is no matter what field you're in, whether you're in the arts, whether you're in education,
whether you're in a policy area, legal like I am, the ability to use numbers, to organize numbers, and to analyze numbers
through your logical inference is becoming more and more
important in any field. For example, if you
wanna work in the arts, the arts now are becoming
more and more funded by competitive grants.
You have to show the grantor, why should they give you money? You know, you show up and say, well, we're good at arts. That's not good enough.
You have to show the impact you have. And you have to show that quantitatively. And I think the ability to do that and the ability to show that
for what I do now, legally, has reinterpreted the
way we think of courts
and reinterpreted the way we think of decision-making on courts. - Your path that you took, you said you wouldn't
necessarily recommend. Students often, you know, ask what skills
or education they need to
go into a specific career. Somebody who's interested in data science, what do you think are the most important hard or soft skills that they would need? - Well, I think if you're
interested in data skills
you probably have, at least are comfortable with numbers. I don't think many people
that are, you know, that hate math and hate numbers, and hated math all the way through school
are considering data analytics unless their parents are
forcing them to do it. So if you're comfortable with numbers, and you do not have to be good at math. The most complicated models I run,
the computer does the math for me. I just gotta know how to
interpret the results. I think what employers are looking for, at least when I speak to employers about graduates that I produce,
they want somebody that's proficient in using numbers and
analyzing with numbers, but what they really want
is someone that can do that, and then interpret it, and provide that to an
audience that isn't, right?
Can you write? Can you orally present your
findings to someone else? And I tell my students all the time that take my statistics class, it's great if you can run the model,
but if you can explain what the model did, it's useless, okay? You have to be able to explain it to individuals that don't know statistics, that don't know quantitative analysis.
So what I always recommend students is, of course, you've got to
get the data side down, the science side down, but besides that, take English classes, take oral speaking classes.
I encourage students to do theaters, to do any performance art
that you can think of, where you're up before an audience that's looking at you and judging you. Of you're comfortable
speaking on your feet,
if you're comfortable
making a presentation, that's gonna be a big skill that's gonna help you
throughout your career if you do data analytics
or any other field. - Yeah. I mean, communication
is really important
in any area. And I think that when you
have such a central job that you're analyzing data, for whatever field you end up in, healthcare or law or wherever you are,
you have to be able to show that the work that you're
doing is important, and why are they paying you? Why are you there? Here are the results.
And you have to be able to show the data that you've collected and why it's important for their company. - Right, as I say this, for your entry level job,
you're most likely gonna
be doing an analysis for a person that doesn't
have the skills you do. They don't have the
quantitative skills you do, but you have to be able
to give them the ability to then take the information
you've provided them,
for them to turn around and sell it to their boss above them in a comprehensive,
meaningful, and objective way. And so that's so important, the ability to communicate both
orally and in written form,
whether it's a memo, a short report, clear, concise, right to
the point, punchy language. If you can do that, employers will love you and you'll get promoted very quickly.
- So this is the last question. Before we let you go, is there anything else you'd
like our listeners to know that we weren't able to cover today? - What I will tell people
is for data analytics,
every field, okay, whether it's in a science community like an academic like I am, whether it's in a for-profit company, whether it's in a nonprofit organization
or a government organization, the need for people to be able
to analyze data, organize it, and then be able to interpret it to people that don't have those skills is just gonna continue to grow
and grow and grow. As our computer power, our
storage power has gone up, data has exploded, especially big data, and people that know how
to manipulate that big data and use it to the benefit
of whatever institution
they're gonna work for will always be able to find a job, and will always have a
very well-paying career. - Well, Peter, thank you so much for joining us today.
And to all of our listeners, if you haven't already checked
out our previous podcasts, they're available wherever
you listen to podcasts. And for more information about starting your career
as a data scientist,
go to dataanalytics.buffalostate.edu, and don't forget to subscribe so that you get a notification each time we release a new episode of Buffalo State Data Talk.
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