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Description
This webinar will highlight universities, teaching institutions, community colleges, and minority-serving institutions that have implemented data science education undergraduate programs as case studies for workshop participants to consider and compare to their own contexts.
Share Your Input on this Topic at: https://goo.gl/forms/8DO4Fu9PV4ZfGi7O2
Resources:
View the Final Keeping Data Science Broad Report, Learn more about the Workshop and View all Webinars in the Series from the links below.
Report: http://bit.ly/KeepingDataScienceBroad_Report
Workshop: http://bit.ly/KeepingDataScienceBroad_Workshop
Webinars: http://bit.ly/KeepingDataScienceBroad_Webinars
For more information on the South Hub Data Science Education and Workforce Working Group visit: http://bit.ly/SouthHub_EducationWG
A
B
Hi
welcome
to
the
cubic
data
science,
Brad,
Thank,
You
Costa
I
am
the
co-executive
director
of
the
South
big
data
hub,
which
accelerates
partnerships
between
business,
academia
and
government,
and
also
the
Georgia
Institute
of
Technology
in
Atlanta.
And
what
we're
trying
to
do
today
is
part
of
a
series
on
keeping
data
science
broad.
B
So
first
up
I'd
like
to
introduce
our
Paul
Anderson
who's
from
the
College
of
Charleston,
and
he
will
be
talking
about
their
Bachelor
of
Science
in
data
science,
Bachelor
and
computer
science,
and
is
associate
professor
of
computer
science
department
and
at
the
director
of
the
data
science
program.
So
full
it's
great
to
have
you
with
us.
B
C
Thank
you
for
having
me
here,
I
appreciate
the
opportunity
to
come
on
and
I
really
look
forward
to
hearing
what
everyone
has
to
say.
I
have
a
few
slides,
they're,
pretty
text-heavy
but
kind
of
remind
me
what
to
say,
and
people
maybe
can
go
back
and
look
later.
Let
me
try
to
share
my
screen
real,
quick.
C
A
C
Me
make
that
big
for
you
guys
alright,
so
data
science
at
the
the
College
of
Charleston
I
when
I
was
thinking
about
what
applications
to
kind
of
talk
about
and
what
are
some
of
the
challenges
I
did
want
to
kind
of
first
start
with
what
what
type
of
University
of
College
of
Charleston
is.
So
it
gives
you
a
little
bit
of
context
about
how
we
solved
our
problems,
even
though
there
I
tried
to
keep
the
problems
as
independent
as
possible,
so
the
College
Charleston
is
in
South
Carolina.
C
We
what
I
would
consider
kind
of
a
small
to
medium
sized
state
institution.
We
have
about
10,000
undergraduates.
We
have
a
historical
focus
on
the
liberal
arts
education,
so
we
still
try
to
maintain
that
even
at
our
large
size,
which
so
it's
kind
of
a
unique
approach
that
we
try
to
take
I'm,
particularly
from
the
computer
science
department.
We
are
separate
from
the
Department
of
Mathematics
I
know.
Sometimes
some
universities
have
those
together.
So
we
have
about
500
meters
majors.
In
total
data,
science
comprises
about
50
of
those
majors.
C
C
That
seems
to
always
come
back
to
me
whenever
I'm
trying
to
do
anything,
whether
that's
change,
curriculum
or
introduce
something
new
is
there.
Science
is
still
a
pretty
unknown
entity
and
I'm
guessing
most
people
get
that
both
on
campus
and
also
to
people
in
your
different
disciplines.
So
it
doesn't
really
have
the
name,
recognition
that
your
English
or
biology,
or
mathematics,
or
even
computer
science,
which
is
often
misunderstood
by
ID
or
for
ID
all
the
time,
so
that
that
gotta
get
some
unique
challenges
that
I
think
we
all
have
to
overcome.
C
We
have
to
be
very
good
communicators
when
we're
talking
about
our
program
and
why
it's
unique
and
why
it's
important
and
and
why
it
needs
to
be
a
separate
program.
Sometimes
it's
also
kind
of
question
just
because
of
its
its
brief
history.
Some
of
the
first
conversations
that
I
had
that
caught
me
off
guard
we're
kind
of
just
people
questioning
how
something
so
new
could
could
you
know,
be
relevant
and
so
on
so
I
had
to
kind
of
step
back
and
say:
okay,
that's
a
question.
C
I
wasn't
expecting,
and
but
even
those
I
think
with
experience
with
data
science
have
to
deal
with
the
fact
that
we
have
different
definitions,
different
interpretations
and
this
really
crosses
over
both
industry
and
academia.
You
know,
if
you
meet
with
a
data
science
company,
they
have
a
very
particular
notion
about
what
that
means.
C
So
what
do
we?
You
know
what
are
some
of
the
things
that
we've
done
here
to
kind
of
do
that
and
some
of
our
specific
things,
so
I
would
start
by
saying
that,
even
if
you
have
all
your
disciplines,
kind
of
agreed
on
some
kind
of
proposal
or
change
or
something
like
that,
the
curriculum
approval
process
in
my
experience
can
still
be
a
little
difficult.
C
So
this
is
saying
that
even
if
you
get
math
and
stat
and
CS
and
your
domains
that
you
want
to
involve
business
or
science
all
to
agree
on
like
a
great
curriculum
and
you're
ready
to
push
it
forward,
you
still
have
to
go
through
that
process.
You
still
have
to
communicate
and
talk
to
people
outside
the
field
that
can
be
kind
of
challenging.
C
I,
have
kind
of
cope
with
that
and
deal
with
that
by
trying
to
get
a
small
team
of
people
who
I
really
like
who
can
really
help
push
through
the
curriculum,
and
everyone
can
can
kind
of
pick
up
the
baton
every
once
in
a
while
and
and
keep
the
the
train
moving.
And
so
recently,
we've
spent
some
time
updating
our
the
domain
component
of
our
curriculum.
I
want
to
talk
I'm,
not
gonna,
talk
about
our
specific
curriculum.
C
So,
in
addition
to
that,
if
even
if
your
colleagues
kind
of
know
what
data
science
is
is
exactly,
how
is
a
high
school
student
gonna
know
what
data
science
is
and
whether
or
not
that's
a
feel
for
them.
So
that's
another
XI
I'll
talk
about
in
a
second
I,
whether
or
not
your
curriculum
is
prereq
heavy.
How
do
you
solve
some
of
those
issues?
This
is
a
very
deep
field,
as
well
as
a
broad
field
of
knowledge
that
students
need
them
to
learn.
C
Can
they
pick
it
up
as
a
freshman
or
do
they
have
to
pick
up
as
a
freshman,
or
they
can
pick
it
up
as
a
junior
or
senior
and
still
finish
your
major
in
a
reasonable
amount
of
time?
How
easy
it's
a
double
major,
so
these
are
some
of
the
things
I
think
about
pretty
consistently
when
I'm
talking
to
thinking
about
how
they
affect
recruiting
and
so
how
I
try
to
mitigate
and
deal
with.
C
Some
of
these
issues
are
so
the
way
I
deal
with
the
fact
that,
like
our
data,
science
curriculum
is
pretty
intense
as
far
as
math
and
stat
and
CS
go.
We
have
an
introductory
data
science
course
I'm
sure
pretty
much.
Everyone
has
one
and
when
I
first
joined
the
program
about
six
years
ago,
when
the
first
things
I
did
was
revamp.
C
That
course,
so
it
was
like
a
little
microcosm
of
the
degree,
because
I
felt
it
was
very
misleading
to
have
kind
of
a
survey
lightweight
course
at
the
beginning
that
really
it's
not
that
I
want
to
blow
people
out
of
the
water
in
this
first
course,
and
and
and
set
it
at
such
a
level
that
they
never
can
get
through
it.
But
I
want
them
to
get
a
taste
of
what
the
rest
of
the
curriculum
is
gonna
be
like,
and
that's
really
helped
us
retain
and
recruit
the
right
students
we've.
C
We
obviously
lose
some
students
to
that
because
they
come
in
and
it's
not
what
they
expect,
but
we
also
gain
a
lot
of
students
that
come
in
they're
like
oh,
my
gosh.
This
is
actually
I.
Thought
I
wanted
to
be
a
computer
science
major,
but
this
is
actually
what
I
want
to
do.
This
sounds
awesome,
so
it
kind
of
goes
both
ways:
I'm,
not
exactly
sure
how
to
recruit.
At
the
high
school
level,
I
mean
we've
done
various
visits.
We've
done
advertising,
not
so
sure
I've
seen
a
lot
of
direct
payoff
with
that.
C
Instead,
I've
kind
of
shifted
the
focus
and
try
to
work
on
our
website,
things
that
reach
a
more
broad
audience
and
then,
as
far
as
the
whether
or
not
a
curriculum,
is
prereq
heavy
and
what
that
entails.
It
used
to
be
very,
very
prereq
heavy
and
it
still
is
to
a
certain
degree,
because
we're
looking
for
depth
of
knowledge
and
stats
and
math
and
computer
science.
C
But
we've
done
a
lot
to
try
to
alleviate
some
of
those
pipeline
issues
that
affect
students
that
are
kind
of
coming
to
this
curriculum,
maybe
in
their
sophomore
and
junior
year,
because
they
didn't
know
it
existed
as
a
so.
Those
are
things.
I
would
recommend
everyone
think
about
when
they're
constructing
their
new
great,
exciting
curriculum,
and
then
that
kind
of
translates
into
how
easy
it
is
to
double
major,
as
students
find
you
from
different
disciplines.
C
Right,
maybe
they
say:
I
really
like
biology,
but
I
like
data
science
as
well
or
I,
like
economics
and
I,
like
data
science.
So
how
flexible
are
you
gonna
be
and
try
to
make
your
curriculum
so
that
they
can
pull
off
a
double
major?
So
all
those
things
kind
of
really
go
into
how
I
like
to
think
about
recruiting
the
third
and
final
challenge
I
just
wanted
to
mention
really
briefly,
is
that
a
lot
of
you
I
think
are
probably
going
to
have
some
version
of
a
capstone
course.
C
So
we've
had
multiple
iterations,
because
we've
had
our
program
for
so
long
and
I
just
wanted
to
mention
briefly
kind
of
what
I
liked
about
each
of
them
in
case
you're.
Thinking
about
those
things
for
us,
a
capstone
is
a
single
semester,
senior
synthesis
course,
and
that
brings
together
all
the
disciplines.
C
The
the
issues
that
I
see
where
these
things
is
that
the
department
resources
are
often
very
stretched.
What
discipline
leads
the
capstone?
Is
there
a
real-world
component
and
so
on?
So
dealing
with
this?
Our
first
iteration
was
an
individual
enrollment
course
where
a
student
and
a
mentor
were
matched
up.
This
was
good
that
it
distributed
the
resource
workload
but
and
it
led
to
customized
project,
but
we
didn't
really
have
great
training
for
the
mentors
involved.
It's
a
little
too
scattered
to
kind
of
give
a
consistent
experience
for
the
students.
C
The
second
iteration
tried
to
move
a
component
of
that
inside
a
single
classroom,
so
we
have
a
single
professor
overseeing
all
these
projects
and
we
still
have
the
individual
mentors.
While
this
was
good,
it's
still
ended
up
being
a
little
bit
problematic
as
far
as
getting
what
I
would
consider
a
full
data.
Science
experience,
what
we've
kind
of
settled
on
and
and
we're
all
in
my
kind
of
talk
is
the
fact
that
our
students
now
are
working
in
teams.
C
These
teams
can
actually
have
CS
math
and
data
science,
people
on
it
that
the
real
difference
is
instead
of
having
one
student
for
one
project
and
one
mentor
for
one
student.
We
now
have
these
teams
and
we
solicit
the
projects
way
ahead
of
time.
So
we
vet
about
whether
or
not
they're
appropriate
make
sure
that
they're
fully
kind
of
explained
and
SPECT
out,
so
the
teams
can
execute
them.
C
This
gives
us
a
much
more
consistent
and
I
think
impactful
data
science
experience
at
the
capstone
level,
but
obviously
we
lose
some
flexibility
in
the
ability
of
tailoring
it
to
some
of
our
honors
students
and
some
of
those
type
of
things,
and
hopefully
I
haven't
gone
over
too
much
of
my
time.
So
thank
you.
Everyone.
B
So
going
forward,
I'd
like
to
introduce
and
that's
equal
Mary
Brutus,
who
is
from
she's,
going
to
talk
about
a
program
from
Great
Bay,
Community,
College
lovely,
to
have
you
she's,
currently
at
Bates
College
and
she
is
was
integral
in
putting
together.
This
program
is
really
the
certificate
in
practical
data,
science
and
an
associate's
degree
in
analytics
and
so
Mary
welcome.
D
Thank
you
for
having
me
I
really
enjoy
being
here.
Alright,
so
Kostas
going
to
be
running
my
slides.
So
if
you
hear
me
saying
next,
it's
just
a
simple
signal
to
him
to
change
this
life
in
the
next
10
minutes,
or
so
it's
my
hope
that
you
will
come
away
with
a
deeper
understanding
and
a
broader
understanding
of
what
today
is
community
colleges
are
doing
and
the
role
that
they
play
in
developing
programs
for
people
who
don't
always
know
what
they
want
out
of
life,
but
have
the
desire
to
better
themselves
next.
D
So
good
news
is
that
educational
/
attainment
overall
is
increasing
the
National
Center
for
Education
Statistics,
published
information
recently,
showing
that
the
percentage
of
25
to
29
year
olds,
with
associate's
degrees,
bachelor's
degrees,
master's
degrees,
it's
all
on
the
rise.
So
that's
the
good
news
next,
the
challenges-
and
this
was
something
that
was
a
big
eye-opener
to
me
when
I
was
doing
some
research
about
five
years
ago
on
the
subject.
D
The
challenge
is
that,
at
least
in
science
and
engineering
in
STEM
fields,
those
will
go
on
to
complete
bachelor's
and
master's
degrees
are
not
as
diverse
as
we
would
like.
And,
as
you
can
see,
I
mean
you
can't
even
see
the
the
areas
that
are
shaded
in
for
some
of
these
races.
Because
of
how
large
of
a
number
the
the
white
category
is,
and
the
stars
represent
races
where
more
than
half
of
them
are
actually
starting
at
a
community
college
level
prior
to
moving
on
to
more
furthering
their
education.
D
D
It's
also
not
a
very
good
story
when
we
talk
about
non-traditional
students,
that
is,
students
that
are
coming
from
families
in
which
either
the
father
or
the
mother
or
both
parents
do
not
have
an
education
beyond
high
school
or
do
not
have
any
kind
of
a
degree
higher
than
high
to
the
left
of
the
red
line.
Here
we
have,
you
can
see
among
those
students
among
those
achievers
people
who
get
the
their
bachelor's
and
master's
degrees.
D
D
D
D
D
There
we
go.
That's
the
problem
with
sorry
back
up
to
that.
The
plan
here
was
that
I
wanted
to
engage
you
in
in
an
exercise
to
show
you
exactly
what
it
is
that
we
deal
with
at
the
community
college
level
and
the
kinds
of
students
that
come
to
us
are
not
necessarily
have
not
necessarily
been
trained
throughout
their
life
to
be
curious
and
to
want
to
know
more
about
the
world.
So
for
those
of
you
here,
here's
the
exercise,
you
like
look
at
this
infographic
and
I
want
to
do
it
just
imagine
for
the
moment.
D
Those
of
us
who
are
on
this,
what
involved
in
this
webinar,
are
going
to
all
be
thinking
that
maybe
one
or
two
of
these
questions,
and
maybe
other
questions
similar
to
it,
you're
going
to
have
a
little
bit
of
curiosity
about
where
the
data
came
from.
What
how
was
the
data
collected?
Did
anybody
bother
to
look
at
other
data
that
might
have
been
related
to
this?
You
know
that
we
we
just
have
this
natural
curiosity
about
what
is
going
on
in
that
infographic
and
I
mean
the
bay.
D
Well,
so,
there's
all
kinds
of
questions
that
come
as
a
result
of
that,
but
in
reality,
a
student
coming
to
a
Community
College,
who
does
not
come
from
a
home
where
the
parents
were
were
involved
in
Sciences
or
stammer
or
college
level,
academic
pursuits,
and
so
those
a
lot
of
students
that
come
to
community
colleges
today
still
need
a
little
bit
of
help
in
developing
that
natural
curiosity
that
the
rest
of
us
take
for
granted.
So
now
go
to
the
next
one,
all
right
so
here.
D
Not
only
that,
but
we
also
have,
as
I
would
say,
students
or
kids,
not
just
kids,
but
these
are
older
adults
as
well.
People
who
come
from
backgrounds,
where
one
parent,
or
at
least
the
father
in
this
case,
did
not
have
any
education
beyond
high
school,
are
not
getting
those
higher
degrees
and,
and
those
that
do
more
than
half
of
them
are
starting
out.
Community
College
next.
D
Okay-
and
here
again,
is
where
we
see
that
a
huge
huge
portion
of
people
get
removed
going
on
to
get
their
PhD
in
stem.
Is
it's
it's
very
off
balance?
And
next,
so
I
was
looking
at
all
of
this
data
five
years
ago
and
as
a
member
of
college
in
New,
Hampshire
I,
just
I
just
felt
compelled
to
do
something,
and-
and
you
know
it
didn't
even
occur
to
me
just
how
bad
it
really
was.
D
These
were
the
objectives
and
goals
we
wanted
to
support
the
the
needs
of
Greater
New
England
companies
by
providing
a
modern
curriculum,
a
foundational
set
of
coursework
that
students
can
apply
it
immediately
and
slipstream
into
either
a
four-year
or
higher
data
science
program,
and
also
we
need.
We
knew
we
needed
to
enhance
existing
computer
science
and
computing
resources
with
modern
data
analytics
and
visualization
tools.
D
So
we
asked
for
$10,000
we
got
five
next,
but
that
didn't
stop
us.
So
the
first
step
was
to
ask
the
question
who's
hiring
one
of
the
big
things
in
Community
College
programming
is
that
I
have
to
be
able
to
show
my
VP
a
a
and
my
Chancellor
that
there
are
companies
out
there
that
are
prepared
to
hire
our
graduates
and
I
reached
out
to
executives
in
industries
like
healthcare
insurance,
financial
services.
D
You
you
name
it.
The
big
ones
in
the
in
the
Greater
Boston
region,
I,
reached
out
the
data
science
community
at
large
and
I
reached
out
to
other
institutions
of
higher
education
and
they're
promising.
At
the
time
in
2013,
I
was
told
that
nobody
hires
a
data
scientist
unless
they
have
a
master's
degree
and
if
there
were
internships
available
they're
going
to
students
like
MIT
grads
and
people
going
coming
from
the
big
four
year.
D
Research
institutions
where
the
glimmer
help
came
was
when
I
spoke
to
someone
at
UNH,
Manchester
and
I,
found
out
that
they
were
creating
a
Bachelors
of
Science
in
data
analytics,
and
they
actually
were
very
positive
and
wanted
to
work
with
me
to
create
an
articulation
agreement.
That
would
allow
a
student
to
transfer
from
a
community
college
to
the
four-year
program.
Next.
D
So
this
is
where
we
started.
We,
we
decided
in
order
to
address
the
issue
of
how
do
you
recruit
like
how
do
you
figure
out
what
students
are
going
to
make
good
data
scientists,
because
the
students
don't
really
know
you
know
they
don't
know
what
data
science
is.
So
we
we
created
the
certificate
as
a
means
for
existing
programs
to
sort
of
embed
them
as
like
a
stackable
credential
in
their
existing
programs,
but
also
to
give
students
an
idea.
D
You
know
hey
is
this
for
me
or
is
it
not
for
me
and
by
the
way
we
felt
pretty
strongly
about
incorporate,
including
an
arts
class,
the
arts
class?
Here
it's
visual
language,
because
in
my
mind
you
can't
be
a
data
scientist
unless
you,
unless
you
really
can
think
creatively
with
color
and
with
with
presentation
and
with
communication,
you
must
be
able
to
communicate.
D
B
A
B
C
C
C
C
E
So
first
want
to
thank
the
organizer
having
us
all.
This
is
I'm
excited
to
hear
everyone
else's
stuff
gets
new
things,
and
I
also
have
to
give
a
shout
and
the
thanks
to
Paul,
because
it
was
one
of
Paul's
workshops
at
CC
a
few
years
ago.
That
actually
got
me
on
the
kick.
I've
even
got
us
and
I
blog.
So
that's.
E
Paul
previously
presented
about
his
program,
so
I'm
gonna,
actually
echo
a
lot
of
the
things
that,
or
some
of
the
things
at
least
of
Paul
was
talking
about
in
the
challenge.
The
things
I
want
to
talk
about,
so
I
thought
something
that
might
be
really
useful
for
everyone
to
sort
of
hear
if
they're
gonna
make
a
program
what
goes
on
with
that
was
critically
thinking
carefully
about.
You
know
what.
E
But
we
usually
what
is
essentially
a
liberal
arts
college
or
fielding
Raj
College
is
that
we
have
500
undergraduate
engineers
without
a
master's
degree,
and
so
we've
got
I
think
we're
ranked
in
the
top
15
or
top
10
engineering
programs
in
the
country.
So
we've
got
a
really
strong
engineering
program.
We've
got
a
master's
degree
that
has
in
information
technology,
so
we've
got
somewhere
between
100
to
200
master's
students
in
information
technology
already
are
I
share
this
because
it's
important
to
understand
sort
of
the
where.
E
Because
of
that
strong
undergraduate
engineering,
population
and
astronomy,
meteorology
program
and
some
other
things,
we
have
a
really
large
mathematics,
department
and
soon
after
got
to
topple
they
actually
split
into.
They
were
combined
and
split
into
two
different
departments.
You
can
see
the
numbers
up
there
are
math
for
our
seismic
university
is
huge.
A
computer.
E
A
E
E
E
A
E
E
Similarly,
we've
had
a
graduate
certificate
in
data
science,
sorts
of
things
for
at
least
five
years,
and
so
soon
after
Paul's
presentation
at
sixty
in
2015,
I
came
back
and
started.
Work
on
proposing
I,
think
a
science
degree,
and
we
actually
proposed
a
statistics
degree
in
same
year,
because
we
sort
of
were
offering
at
many
courses.
Part
of
the
challenge
with
this
also,
though,
is
that
all
of
these
programs
are
not
housed
in
the
same
place.
E
Although
we
have
close
times
the
computer
science
Oh,
it's
obviously
in
the
college
of
business
and
then
the
graduate
degrees
are
officially
housed
under
graduate
school.
So,
even
though
we
sort
of
control
them
in
the
maths
that
they're
through
a
whole
bunch
of
different
centers
of
paperwork,
which
is
always
a
fun
thing,
so
part
of
what
we
did
is
we
were
working
on
proposing
this
was
it
we
were
looking
and
thinking
about
how
they.
E
E
E
And
it's
already
hard
enough
to
articulate
what
business
analytics
is,
but
alone
with
data
Sciences,
and
this
is
the
same
kind
of
question
you're
always
raising
about
you
know
unknown
any.
No
one
knows
it's
so
for
us.
Our
data
science
degree
has
a
much
deeper
and
stronger
rigor
required
in
the
math
in
the
statistics
and
computer
science
curriculum
and
that's
partly
because
it's
hosted
in
the
math
and
statistics
world.
It's
also
because
it's
hosted
outside
the
College
of
Business,
the
general
education
for
students
taking.
It
is
much
broader.
E
So,
instead
of
taking
a
whole,
almost
40
or
50
credits
of
business
general
education,
they
get
to
just
take
broad
general
education,
which
lets
them
get
skills
for
different
for
applications
and
other
domains.
There's
also
makes
it
a
lot
easier
for
students
who
are
interested
in
double
majoring,
because
it
means
that
a
lot
of
those
are
not
education
credits
connection
to
count
towards
your
major.
E
So
they
take
it.
Oh
of
course
in
SAS-
and
we
just
recently
are
gonna-
add
a
one
credit
program,
of
course,
at
our.
If
they
just
do,
it
is
in
the
major
they're
not
coming
up
with
very
strong
coding
skills,
necessarily
similarly,
there's
zero
need
or
requirement
for
the
sort
of
other
end
of
working
with
putting
data
into
what
they're
doing,
which
is
not
saying
Stephen
couldn't
choose
to
get
into
extra
classes,
but
they're
not
required
to
so.
E
We
felt
like
this
is
really
a
distinction
between
a
data
science
degree,
but
also
that
it's
not
really
creating
something
that
big
data
science
per
se
without
some
of
those
other
deeper
skills.
Some
early,
they
don't
get
a
lot
of
exposure
to
machine
learning
here
and
I
thought.
That's
a
little
getting
there's
minimal
exposure
in
some
places,
something
else
we
didn't
do-
and
this
is
so
I'll
be
interested
to
hear
how
people
did
theirs
their
data
science
minor
and
people
keep
asking
about
it.
E
E
E
E
We
decided
to
sort
of
focus
on
some
of
the
things
that
already
does
really
well
and
they're
gonna
get
out
and
try
to
make
sure
that
that's
sort
of
something
that
makes
sense
for
our
program
to
be
good
at
and
so
three
of
the
strengths
that
Valpo
really
already
does
is.
Even
though
we're
a
full-fledged
masters
university.
We
still
have
a
strong
liberal
arts
tradition,
which
means
that
all
the
students
that
graduate
from
our
general
College
of
Arts
and
Sciences
have
really
strong
communication
skills.
E
Both
in
presentation
right,
we
also
read
the
disaffiliate
University
and
so
as
a
whole.
Our
body
tends
to
be
very
service
oriented
and
have
a
lot
of
service
interests.
We
spend
thousands
and
thousands
of
service
hours
every
year
doing
projects
and
things
like
that,
and
so
we
wanted
to
sort
of
capitalize
on
that
also
because
we're
still
not
a
really
large
university,
there's
still
a
lot
of
strong
different
departments
that
have
close
ties.
E
E
E
That
was
we
get
the
advantage
of
having
people
enter
a
class,
so
I
can
match
under-skilled
students
with
more
skilled
students
in
terms
of
achieving
success
and
what
we
did
will
be
focused
on
having
them
be
projects
and
track
a
project
under
course,
and
I
modeli
after
into
the
in
miniature,
the
university
should
come
as
data
science
or
social
good
summer,
fellowship
thinking
that
I'd
like
to
see
our
further
the
whole
pull
in
projects
that
are
nonprofits
social,
good
projects.
Things
like
this,
and
so.
E
With
child
services,
social
services
in
Texas-
and
we
did
some
faculty
projects
on
campus-
that
students
were
able
to
do
you
know
disciplinary
work
with
other
faculty
didn't
need
to
help
with
their
data,
and
this
also
sort
of
centers
around
this
idea
of
having
really
application
heavy
intro
classes,
which
is
the
best
practice
from
the
science
and
then
tied
with
that.
Inside
of
our
program,
we've
got
a
junior
senior
year,
sort
of
cast
and
sequence.
E
I
thought
up
the
courses
we've
got
very
equal
smattering
across
all
the
different
disciplines,
where
they're
taking
your
science
credits,
ten
math
credits
and
then
some
some
data,
specific
courses-
and
this
goes
back
to
how
we
differentiate
ourselves
from
any
of
the
late
math
or
science
or
statistics
or
business
analytics,
where
in
some
sense,
students
are
getting
minors
and
three
different
disciplines.
With
a
little
bit
of
a
specialization
in
data,
but
it's
all
collected
together-
and
you
can
also
see
here
now
why
we
felt
like
a
minor.
E
B
You
Karen,
yes,
it's
a
very
interesting
program
and
it
actually
is
a
lead-in
to.
We
have
a
webinar
to
that.
We'll
talk
about
later,
but
it's
alternative
mechanisms
or
a
data
science
and
the
data
science
was
social.
Good
is
something
that
we
actually
just
sponsored
the
one
in
Atlanta
this
year,
so
for
the
day,
our
social
good.
B
G
G
G
I'm
gonna
talk
about
some
of
the
challenges
we
are
facing
now
and
we
found
it
in
2013
with
very
precious
support
from
the
major
administration,
because
before
that,
our
ding
envisioned
a
setting
up
a
business
analyst
program
in
business
school
and
he
actually
wrote
several
articles
about
business
analytics
and
with
such
support
from
their
major
administrations,
and
actually
it
helps
us
a
lot
in
spending
our
program.
Just
wanna
give
you
a
general
idea
on
what
programs
who
are
doing
now
related
to
business
analytics.
G
There
are
eight
major
code
of
seven
major
courses,
and
you
see
that
the
courses
are
set
up
around
what
we
around,
how
we're
solving
analytics
programs.
So
everything
start
with
making
sense
or
problems
understanding
opportunities
and
formalize
the
problems.
So
we
have
a
course
called
communicating
quantitative
results
and
those
cost
has
been
offered
around
the
very
beginning
of
business
analytics
and
also
we
we
have
this
cost
of
F
around
the
very
last
step
of
businesses,
which
is
delivering
the
results
with
presentation
and
we're
saying
the
beginning
and
the
last
step
of
analytics.
G
We
also
have
data
preparation
step
and
for
this
step
we
have
two
courses
offered,
and
one
is
business.
Big
data,
one
and
big
data
to
the
two
causes.
Help
us
help.
Students
learn
to
clean
emerge,
attract
useful
information
for
further
analysis,
and
then
we
have
two
steps,
one
after
that.
One
is
modeling
process,
the
other
one
is
analysis
for
the
modern
processes.
We
have
students
to
form
an
empirical
model
around
the
programs
that
we
are
asking
here.
G
So
we
have
actually
two
courses
offered
around
it:
business
analytics
one
and
predictive
modeling
one
and
also
for
the
analysis
part.
We
have
using
specific
methods
or
regression
machine
learning
methods
to
deal
with
the
models
we
set
up
and
we
have
business
in
the
list
too,
and
pretty
good
predictive
modeling
to
offer.
So
this
is
a
general
idea
of
our
curriculum
next
Paige
here.
G
This
is
just
wanna.
Give
you
a
general
idea
how
those
causes
being
covered.
So
in
the
very
beginning,
the
very
first
year
they
did
not
get
really
get
to
run
any
costs
related
to
business
and
headaches.
But
later
in
the
second
year,
they
gonna
be
offered
with
the
basic
business
courses
like
have
a
general
understanding
of
accounting
suppression,
there's
different
courses
to
get
prepared
to
understanding
business
questions,
and
then
they
have
business
and
the
next
one
and
business.
Then
that
will
be
over.
During
the
second
year
insert
a
year.
G
You've
got
they're
going
to
play
with
real
data.
They
have
big
data,
one
big
data
to
pretty
modeling
and
also
in
the
stud
in
the
fourth
year
the
senior
year.
They
have
more
related
of
courses
and
they
get
to
choose
several
business
analytics
electives.
So
that's
a
general
idea
of
how
we
set
up
the
curriculum,
and
this
is:
why
is
it
attractive?
G
The
program
is
attractive.
We
have
several
tools
offered
and
this
actually
going
to
be.
One
of
the
challenges
are
gonna
mention
in
the
later
slides,
and
we
one
thing
we
are
very
products.
We
have
100%
placement
rates
actually
up
gradually
graduation.
Here
at
ovum,
we
try
to
foster
a
very
good
relationship
with
different
organizations,
and
you
see
that
we
hold
corporate
information
sessions
each
semester.
This
is
a
list
of
what
we
are
offering
this
semester.
G
We
started
in
2017
a
2013
when
we
first
started.
We
only
have
two
full
faculty
members
in
members
and
one
instructor
and
in
2014
we
hired
two
new
faculty
15.
We
had
another
two
and
16.
We
have
a
dad
hired
an
attitude,
so
you
see
that
how
we
actually
expand
it
from
a
big
small
group
to
a
big
soup
group,
with
a
big
number
of
faculty
members
devoted
to
the
business
analytics
area.
G
The
hiring
process
is
that
challenge
for
us.
Another
thing
is
how
to
I
think
this.
This
is
something
that
Paul
had
mentioned
before:
how
to
recruit
students.
Most
of
students
are
dotted
away
by
the
quantitative
nature
of
the
materials
when
I
first
learned
that
that
they're
going
to
be
computer
science
technologies
and
such
as
the
programming
language
Python
and
are
gonna
offer
in
the
class
and
also
some
statistic
tools,
gonna
be
offering
class.
G
They
a
little
bit
worried
about
whether
they
gonna
understand
me
have
a
good
understanding
of
the
materials,
and
this
one
is
much
relieved
by
shifting
our
emphasize
on
how
we
make
sense
of
the
business
analytics.
We
are
very
high,
very
project
oriented.
Let
me
give
you
example
of
what
we
are
doing
in
my
class.
G
I'm
teaching
predicts
predictive
modeling
to
this
year
and
we
are
working
on
projects
that
having
students
looking
at
the
renting
market
and
because
Obon
is
a
small
college
term,
we
only
have
sixty
thousand
sixty
thousand
population
and
I
think
among
them
more
than
twenty
thousand
of
students.
So
the
ranking
market
is
very
demanding
here.
So
are
we
having
student
looking
at
a
brand
new
market
and
also
having
them
looking
at
housing,
values?
G
Look
at
what
what
are
those,
but
what
are
other
attributes
gonna
inference
those
and
also
having
students
predict
the
traffic
during
the
game
days.
You
know
for
boys
a
big
deal
here
at
Auburn.
We
also
have
people
to
look
at
the
text:
information,
Facebook
pages
of
album
football
and
we're
having
them
to
understanding
the
sentiment
of
those
intestine
messages,
those
messages
on
Twitter
and
Facebook,
so
they
actually
have
fun.
G
G
I
think
those
schools
who
are
who
are
planning
on
offering
a
business
in
the
news
forum
should
have
a
good
discussion
on
which
part
of
the
material
should
be
covered
in
which
has
to
afford
certain
overlap,
and
the
last
one
is
the
choice
of
analytics
tools:
they're
going
to
be
covered
in
class.
You
know
we
have
a
server
a
server
academia
program
representative
from
different
companies
such
as
SAS
SAS,
Enterprise.
Miner,
that
is
one
tool.
G
One
of
the
tools
were
using
class
and
we
also
have
representative
coming
and
training
faculty
member
on
using
SAS
cloud
analytics,
and
we
also
have
training
sessions
from
IBM
Watson
bottom
and
we
also
have
several
other
ones.
So
there
is
a
debate
among
faculty
members
on
what
tools
should
we
should
we
cover?
Shall
we
teach
in
class?
G
G
Which
how
do
you
emphasize
that
the
tool
actually
is
not
that
a
big
is
not
a
very
big
deal
over
there?
The
the
concept
of
metadata
behind
that
is
the
thing
that
really
important
and
again.
We
also
emphasize
that
to
make
sense
of
the
business
program
to
learn
to
ask
questions
what
kind
of
question
we
can
ask
and
how
we
deliver
the
results
to
as
a
reporter
to
managers
and
what
kind
of
business
implication
you
can
draw.
B
You
pay
yeah,
it's
a
very
interesting
program
and
the
fact
that
you've
thought,
through
you
know
all
of
these
different
layers
that
you're
talking
about
as
well
and
we'll
get
to
a
question.
Ever
you
have
an
agenda
at
after
the
neck
and
our
last
speaker
about
prerequisite
and-
and
it
was
very
interested
in
your
coming
about
faculty
and
how
you
give
you
comfort
faculty
for
training,
so
our
last
but
definitely
not
least,
a
presenter
hold
on.
Let
me
make
sure
that
I'm
sharing
my
screen.
B
In
Jean,
Rey
who's
associate
professor
of
statistics
and
the
director
for
the
center
of
statistics
and
analytics
research
at
Kennesaw,
State
University,
where
he's
going
to
be
talking
about
their
minor
and
applied
statistics
and
his
work
with
a
challenge.
The
challenge
in
data
science,
education
of
looking
at
experimental
learning
through
partnerships
with
industry.
So
gene
welcome,
hi.
F
F
F
So
today
we're
gonna
do
a
little
background.
Many
people
are
not
very
familiar
with
Kennesaw
State
and
where
we
are
so
I
talked
about
that
how
our
department
grew
up
and
then
the
program,
and
then
from
that
there's
really
two
types
of
experiments:
experiencial
learning
that
we're
kind
of
experimenting
with
as
I
thought
I
would
focus
specifically
on
kind
of
that
model
and
then
the
benefits
and
issues
is
it's
kind
of
a
really
specific
thing:
we're
a
really
large
University.
We
have
about
35,000
students
we're
located
in
a
northern
suburb
of
Atlanta.
F
So
because
of
that
we
have
access
to
a
huge
kind
of
tech
hub
here
in
Atlanta
we
offer
about
150
degree
programs.
We
also
have
a
master's
in
applied
statistics,
and
then,
two
years
ago
we
started
a
PhD
in
analytics
and
data
science
and
I
teach
in
both
of
these
programs,
as
well
as
our
minor
program.
That's
more
so.
The
emphasis
for
this
talk.
F
F
So
we
first
formed
an
advisory
board
with
some
of
the
executives
that
we
thought
would
hire
our
graduate
students
and
out
of
that
kind
of
devised,
our
curriculum.
Also
because
of
our
history.
We
have
a
really
diverse
set
of
quantitative
backgrounds,
so
we've
got
people
from
statistics,
biostatistics,
epidemiology,
Industrial,
Engineering
decision
science,
and
we
just
recently
hired
a
faculty
member
with
educational
psychology,
primarily
because
of
the
structural
equation
model.
So
we've
got
a
very
diverse
set
of
backgrounds.
F
It
could
be
challenging
to
make
curriculum
decisions,
so
the
advisory
board
definitely
informed
some
of
those
early
decisions,
and
this
was
started
before
kind
of
the
buzzwords
of
data
science
came
out
and
like
bleep
mid
to
late
2000s.
So
that's
also
why
it
still
is
that
more
you
applied
statistics
name.
So
it's
it's
really
with
the
minor
we're
focusing
on
applying
statistics
within
some
sort
of
domain
area.
So
we
end
up
with
people
from
psychology,
business,
math,
biology
coming
to
us
to
kind
of
augment
their
skills
with
some
sort
of
quantitative
techniques.
F
All
of
our
courses
have
a
heavy
applied
emphasis.
Even
stat
methods,
1
&
2.
We
primarily
teach
SAS
in
the
computer
applications
of
statistics
we
introduce
students
to
Python
SAS
are
a
little
bit
of
Excel,
basically
that
those
tools
you'd,
expect
to
find
kind
of
an
in
business
environment,
and
then
all
of
our
courses
are
really
geared
towards.
How
do
you
apply
statistics?
F
Not
so
much
focused
on
the
mathematics,
given
our
diverse
background
of
students
in
the
minor
program
now
something
that
we
identified
early
on
was
you
have
to
have
some
sort
of
experience
or
learning
environment
for
this
to
work?
So
what
we
were
able
to
do
is
create
two
sort
of
types
of
classroom
environments.
What
is
regularly
offered
so
we've
got
applied,
binary
classification
and
design
of
analysis,
design
and
analysis
of
human
studies.
F
Both
of
these
courses
are
direct
partnerships
with
industry,
where
industry
gave
us
their
data
sets,
and
then
let
us
use
it
in
this
course.
In
this
classroom
setting
so
applied
binary
classification,
it's
really
credit
scoring.
So
it's
teaching
the
students
how
to
clean
a
massive
data
set
been
variables.
Analyzed
variables
actually
develop
their
own
credit
scoring
model
oftentimes.
F
This
word
gets
presented
at
our
own
sass
conference
and
then
some
of
those
go
on
to
present
at
Global
Forum
or
some
of
the
other
conferences
and
recently
have
won
some
of
the
first-place
posters
just
based
off
of
that
class.
The
design
announces
of
human
studies
is
designed
by
our
epidemiologist
and
so
she's
partnered,
with
a
company
that
owns
many
of
the
neonatology
practices
around
the
nation
and
so
she's
able
to
collect
data
and
develop
growth
curves.
So
hers
is
more
like
an
intensive
research
environment
for
students.
F
Typically
there's
a
graduate
student,
that's
over
a
research
team
and
then
the
research
team
is
comprised
of
undergrads,
and
so
they
work
together
to
figure
out
how
to
solve
problems.
Do
the
analysis
and
then
actually
get
publications
that
the
students
are
listed
on,
so
that
those
are
kind
of
our
more
traditional
course
offerings,
and
then
out
of
that
corporation
started
coming
to
us
and
asking?
Could
you
help
us
solve
problems?
F
They
have
a
huge
amount
of
data
and
they're
trying
to
figure
out
how
do
you
transition
into
more
of
a
data
science,
space
and
so
they're,
using
our
class
to
solve
problems
that
are
of
priority
to
them,
but
they
either
don't
and
staff
or
can't
resource
and
then
they're
also
recruiting
interns
out
of
that
class.
So
that's
just
one
example
of
this
idea
that
we've
sort
of
brought
up.
F
So
obviously
the
benefits
the
students
get
some
rumor
and
exposure.
Part
of
the
class,
especially
those
corporate
sponsored
projects,
is
that
the
students
have
to
go
back
and
present
for
the
executives
and
then
oftentimes,
there's
prize
money,
that's
awarded,
and
so
the
executives
get
a
decide
who's.
The
winning
team
versus
the
non
winning
team.
The
idea
of
a
competition
built
within
a
class
also
sort
of
invigorates
the
students
in
some
regarding
limits,
kind
of
the
traditional
collegiate
environment.
F
They
even
hope
students
would
have,
but
in
other
ways
it
really
pushes
the
analytics
irena
a
click
through
like
web
click
through
class
for
the
Home
Depot,
and
the
amount
of
analytics
that
the
students
were
able
to
produce
over
a
summer
semester
was
amazing
primarily
because
of
this
competitive
environment.
So
it's
really
a
neat
concept:
oftentimes,
the
top
teams
that
are
exposed
to
the
executives
they're
offered
internships
and
jobs.
The
more
we
do.
This,
the
more
reputation
grows
and
we're
able
to
then
develop
deeper
relationships
with
industry.
F
We
actually
have
a
waiting
list
now
of
company
partners
wanting
to
do
this
sort
of
class
with
our
students.
So
now
the
issues,
if
you're
trying
to
do
this,
it
takes
a
really
concerted
effort
to
develop
those
relationships
with
industry.
There
are
all
sorts
of
risk
that
the
faculty
member
and
the
department
sort
of
have
to
mitigate.
F
So
it
also
requires
a
lot
of
kind
of
client
management
and
team
management
on
the
faculty
member
on
the
faculty
member
to
make
sure
that
you're
meeting
expectations
of
the
partner,
while
also
kind
of
controlling
the
teams
and
making
sure
that
the
teams
are
able
to
function,
because
if
a
team
gets
overwhelmed
too
soon
they're,
it's
really
difficult
to
help
them
move
forward.
And
with
that,
it's
all
sorts
of
time
required
of
the
faculty
member
meeting
with
the
team
members
and
making
sure
that
the
presentations
are
high-quality.
The
analytics
are
appropriate.
F
Luckily,
several
ER
faculty
members
have
came
from
industry
like
I
used
to
run
research
teams
for
Thomson
Reuters,
so
I'm
very
used
to
this
and
used
to
that
time,
intensity,
but
I,
guess
the
new
faculty
member.
Without
that
experience
being
completely
overwhelmed,
the
other
important
factor
is
identifying
the
appropriate
skills
amongst
your
faculty,
who
could
manage
the
clients
and
manage
with
the
students?
That's
also
very
challenging,
so
I
really
went
through
that
it
based
off
of
this
concept,
that
you
do
data
science
or
to
learn
how
to
do
data
science.
F
A
A
There
we
go
thanks
very
much.
Jeanne
Grenada
may
be
having
a
little
bit
of
trouble.
So
how
that's
why
you
didn't
come
Friday,
but
in
the
meantime
we
can
try
to
go
to
questions
we
ran
out
comes
back.
She
cannot
write
that.
Thank
you
very
much
excellent
presentations.
Thank
you
form
all
our
speakers
and
I'm
gonna
ask
for
here.
While
we
wait
for
Renata
is
if
my
assistant
here
can
plug
in
a
few
questions,
just
write
on
the
Twitter
to
get
us
started
and
we'll
go
from
there
and
for
the
speakers.
A
E
So
from
my
perspective,
I
students
are
looking
to
get
in
our
program.
I
am
really
just
looking
for
them
to
have
a
basic
course
and
some
kind
of
programming
experience
if
they
come
in
with
that
they're
they're
ready
to
hit
the
ground
running
with
our
program,
obviously
the
more
of
either
of
those
they
come
in
with
the
stronger
their
starts
going
to
be,
but
for
us,
I
built
the
program
intending
for
students
to
be
able
to
come
in
without
sort
of
needing
any
pre
knowledge
and
still
be
able
to
succeed.
I.
C
Guess
from
I
guess,
from
my
perspective,
one
of
the
things
that
is
most
important
is
the
ability,
like
think
critically,
about
a
problem
and
figure
out
like
how
to
approach
it.
I
feel
pretty
strongly
there
like
I,
can
overcome
any
shortcomings.
You
don't
know
how
to
program
a
program.
If
you
don't
know
that
SATs,
are
you
teaching
stats?
C
This
idea
that,
like
it
presented
a
difficult
problem,
you
have
to
somehow
decompose
it.
It's
intangible
parts
that
you
can
solve
and
somehow
evaluate
I
feel
like
that's
something.
The
high
schools
could
really
help
us
tackle,
just
getting
that
going
really
in
before
high
school
and
all
the
way
through.
E
You
know
got
another
two
cents.
If
anything
I
want
you,
someone
in
the
high
schools
making
sure
that
students
knew
what
data
science
or
Dale
was
as
opposed
to
math
versus
the
physics
or
computer
science.
They
think
at
the
end
of
the
day,
I
suppose
that
we
can
teach
them
whatever
they
need
for.
This
songs
are
coming
with
a
reasonable.
E
F
So
in
our
minor
program
that
that's
a
big
struggle
that
we
have
is
is
because
you
spend
so
much
time
teaching
you
how
to
do
the
analysis
and
how
to
bring
in
data
and
clean
data
that
the
presentation
almost
becomes
an
afterthought.
This
we're
kind
of
those
experience,
real
learning,
classroom
experiences
come
in
because
it's
really
really
develop
their
skills.
We
forced
them
to
rehearse
and
then
kind
of
that
leads
to
other
conference
presentations
and
those
sorts
of
things.
It's
really
how
we
approached
it
was
kind
of
extracurricular.
D
Suppose
it
depends
on.
It
depends
on
what
kind
of
an
outcome
you're
looking
for
for
usually
for
for
community
colleges.
You
are
you're
really
dealing
with
the
students
who
don't
have
much
of
a
background
at
all
so
and
that's
part
of
it
I
mean
really.
If
a
student
doesn't
have
any
background
in
stats
and
doesn't
have
any
background
in
programming,
it's
really
almost
impossible
to
accomplish
an
introduction
to
here's.
D
What
data
is
here's,
what
a
data
distribution
is
and
and
here's
how
to
understand
what
data
is
trying
to
tell
you,
oh
and
by
the
way
you
have
to
program
at
the
same
time,
and
we
found
that
that
when
we
tried
that
at
Great
Bay,
we
tried
sort
of
incorporating
all
of
those
things
in
what,
in
that
first
intro
to
data
science
course
and
students
were
just
overwhelmed.
So
it
depends
on
the
background
of
the
student
really,
but
if
you're
dealing
with
students
that
are
that
are
just
coming
in
cold
off
the
street,
it's
it's.
B
A
B
B
D
We
we
have
a
huge
imbalance
of
bachelor's
and
master's
degree
recipients
that
it's
just
it
does
not.
It's
not
representative
of
our
population
and
so
I
think
we
need
to
do
what
we
need
to
all
come
together
and
have
a
conversation
and
have
a
discussion
about
how
we
can
rethink
where
we
put
students
into
when
they
come
to
our
colleges
and
universities
and
not
just
not
just
put
them
into
whatever
major.
D
C
So
I
can
I
can
try
them
a
little
bit
about
our
program
and
about
how
how
I've
been
kind
of
monitoring
some
of
the
diversity.
Some
of
the
encouraging
things
that
I've
seen
is
a
you
know.
Big
thing
in
computer
science
is
a
lack
of
female
participation
and
I.
Don't
really
see
the
same
in
my
data
science
program,
so
you
know
the
College
of
Charleston.
C
We
actually
have
about
60%
female
across
the
entire
college,
but
in
computer
science
I
think
we're
doing
better
than
most
because
of
that
percentage,
but
we're
still
around,
like
20%
I,
think,
but
in
data
science
I'm
it
right
about
50%,
exciting
to
me
the
you
know,
there's
it's
really
kind
of
interesting
in
these
introductory
classes.
Ours
does
not
have
any
prereqs.
C
We
do
have
a
big
experience
or
learning
component
to
it
and
and
my
main
goal
for
that
is
just
for
students
to
kind
of
see
what
life
as
a
data
scientist
is
like
I
tell
them.
That's
kind
of
the
only
learning
outcome
from
the
first
course
so
that
we
can
maintain
as
approachability
and
I
gotta
say
like
some
of
my
most
exciting
and
fun,
students
really
came
and
they
didn't
know
what
the
field
was.
C
I
think
we
have
an
I
guess
I
just
closed
and
say,
like
I,
feel
like
it's
easier
to
do
it
there
than
my
other
computer
science
classes.
Okay,
you
know
both
our
we
need
to
solve
the
problem
in
a
lot
of
places,
but
I
feel
like
I,
have
a
greater
ability
to
solve
the
problem
in
the
data
science
field,
and
maybe
that's
because
you
know
I
can
really
try
to
motivate
with
this.
C
E
B
C
E
Twice
now
always
amazed
by
I'm
pretty
sure
that,
like
60
to
75
percent
of
the
often
with
a
much
higher
minority
content,
then
we
often
see
yet
other
things.
That's
said
when
I
go
to
some
of
the
more
traditionally
computer
science
conferences
that
might
have
some
strong
data
science,
but
it's
are
still
mostly
computer
science
conferences.
You
see.
E
E
E
G
That's
also
my
little
motivation
here
at
all
them
with
our
undergraduate
programs.
We
see
about
the
same
number
of
female
and
there
is
a
balance
between
number
of
female
students
and
male
students,
but
for
our
graduate
program
there
are
definitely
less
females
at
and
about
the
pre
required
courses.
I
think
I
agree
with
you
guys,
I,
don't
think
there
should
be
any
pre
required
skills.
I
think
one,
a
very
important
characteristic
is
as
long
as
you
are
curious.
B
Yeah
so
I
mean
this
is
something
that's
interesting.
The
question
of
prereqs
comes
up
a
lot
and
so
I
wanted
to
get
at
also
each
one
of
you
talked
about,
and
this
came
up
is
a
communications
being
key
and
in
some
of
other
work,
we've
done.
We
had
maths,
that's
you
know.
Computer
science
coding
communication
has
come
up
with
a
lot
of
our
industry
partners
as
well
as
the
idea
of
ethics
of
how
do
you
take
the
data
and
use
it
in
an
ethical
manner.
B
G
Yes,
we
actually
have
a
course
offered
around
this
one.
It's
about
communicating
the
quantitative
results
with
Stowers
understand
me,
making
sense
of
business
program.
That's
communication
between
the
company,
the
plate,
the
parties
that
have
in
the
question
between
the
students
and
then
later
the
last
step
is
delivery.
Results
yeah!
That's
what
we
mean
by
communication
here
in
one
important
feature:
extrema
in
related
to
business
analytics
is
how
you
present
through
PowerPoint,
slides
right
how
you
copy
those
graphs.
Do
you
get
in
here
and
open
highway
training
students?
We
have
students
when
they
finish
their
analysis.
G
We
have
student
writing
as
your
aportfolio
a
certain
template.
It's
like
writing,
like
a
general
article
or
having
Wistar
ways,
highlights
the
business
implication
and
introduction
and
describe
a
business
program
and
also
describe
your
data,
describe
the
model
you
used
it
before
and
also
what
the
kind
of
business
implications
you
are
drawing
here.
G
We
kind
of
follow
the
different
steps
having
students,
follow
the
template
and
deliver
their
results
and
about
specific
tools
we
use
to
to
visualize
and
we
are
using
SAS,
Enterprise
sense
and
price
visual
analytics
and
also
while
we
introduce
pass
and
are,
they
also
have
very,
very
useful
visual
packages.
Some
of
the
things
we
are
doing
here.
E
E
E
Using
statistical
data-
and
we
tackle
that
by
having
explicitly
talked
about
having
ethics
case
studies
in
our
capstone
sequence,
so
for
three
years,
they're
discussing
ethics
as
part
of
their
eyes
are
growing
about
maturity.
Things
like
this
about
data
and
is
this
a
chance
to
sort
of
really
engage
with
early
in
their
career,
but
then
also
as
they
move
through
their
curriculum,
they
sort
of
grow
in
their
understanding
of
health
data
can
be
used.
It
can
also
grow
in
their
understanding
of
how
data
can
be
misused
and.
E
B
F
F
B
D
B
D
It's
if
you
look
it
up,
it's
got
an
acronym
crisp
diem
and
that
addresses
a
lot
of
the
ethical
issues,
as
well
as
just
best
practices
for
how
to
how
to
manage
a
data
set
and
how
to
how
to
do
data
mining
in
an
effective
and
in
a
way
that
does
not
does
not
cause
you
to
like
to
put
your
own
ideas
into
that
data
set.
So
if
you've
never
done
data
mining
before
you
don't
know
what
I'm
talking
about.
D
B
Yeah,
this
is
very,
very
true.
You
know
so
before
this
in
my
iteration
of
working
with
the
government,
with
the
NSF
and
with
the
White
House
Office
3a
did
talk
a
lot
about
this
idea
of
data
being
such
the
new
gold
and
companies
and
how
that's
being
used,
but
also
in
the
social
and
political
sectors
as
well
as
academic
sectors
and
the
amount
of
people
that
are
actually
equipped
to
know.
B
You
know
when,
when
are
you
putting
your
own,
you
know
bias
in
some
ways
into
the
data
analysis
and
how
is
that
translated
when
it's
an
algorithm
or
a
rolled
up
process
or
a
machine
learning
thing?
There's
no
auditable
trail.
So
you
know
that's
something
that
has
come
up
in
other
settings
and
I
want
to
leave
it
open.
B
So
if
others
have
programs
or
input
on
these
topics,
you
can
go
there
and
fill
out
the
form
in
whole
or
in
part
as
many
times
as
you
would
like
as
new
things
come
up
after
you
view
these
webinars.
If
you
change
your
mind,
you
can
go
back.
We
will
definitely
wrap
those
up
into
a
report
that
we
want
to.
You
can
opt
into
getting
the
report
at
the
end
as
well
as
part
of
it.
One
section
shows
you
through
the
hubs:
I'm
collecting
data
science
programs.
B
We
have
about
500
right
now
across
the
country
and
looking
at
just
where
they
are
and
visualizing
that
for
the
south
and
for
the
US
and
what
departments
they're
in
so
I
think
it's
a
little
tiny
link
on.
Maybe
you
know
page
three
of
the
community
input
form
but
it'll
make
it
bigger
on
the
on
the
website
as
well,
where
you
can
see
what
that
visualization
looks
like
of
the
different
data
science
programs
across
the
country
and
if
you're
interested
in
being
a
case
study,
you
know
four
for
one
going
forward.
B
So
with
that
we're
exactly
one
minute
off
time.
I'd
like
to
thank
each
and
every
speaker
for
not
only
your
great
speech
today,
but
also
for
your
fantastic
program
and
hopefully
so
that
we'll
see
you
again
in
the
future
and
you
can
be
connected
with
the
hub
as
a
brief
background.
This
is
part
of
the
human
data
science
broad,
but
the
south
big
data
hub,
as
well
as
the
other
four,
are
really
about
making
this
bridge
around
data
science
between
industry,
academia
and
government
in
order
to
serve
society
at
large.
B
So
we
have
projects
that
are,
you
know
specifically
across
sector
that
saw
a
grand
challenge
for
the
nation,
and
we're
interested
in
education
is
one
of
those
grand
challenges
and
that's
where
we
would
love
to
have
people
join
and
also
get
involved
in
this
effort.
Specifically,
all
right
with
that.
Thank
you
and
thank
you
Costa
for
holding
us
all
together.
Even
when
I
I
don't
know
no.