►
Description
Presenter: Rachel Levy
Institution: North Carolina State University
A
A
Okay,
so
public
science,
maybe
getting
out
there
and
people
in
all
walks
of
life
data
Literacy
for
communities,
everyday
people.
So
that's
so
interesting
right.
So
I
I
asked
a
question
and
we
had
two
answers
that
were
almost
exactly
the
same:
we're
in
the
same
flavor
and
did
you
hear
that
wait
time
that
pause
and
then
four
more
popped
up
that
had
this
additional
level
of
inclusivity
of
additional
types
of
people
that
also
have
very
like
very
similar
resonance
with
them.
A
So
one
kind
of
accessibility
is
this
wait
time
that
I
just
did
so?
I
just
gave
you
an
example
of
making
data
science
for
everyone.
There's.
Those
of
us
like
me,
who
are
the
really
quick
speakers.
I,
always
got
something
to
say
really
fast,
there's
also
deep
thinkers
that
think
more
deeply
than
me
that
if
we
wait
a
little
while
you
get
those
additional
thoughts
that
are
so
important
in
conversations,
so
I
also
see
have
tools
and
techniques
available
to
a
broader
range
of
people.
A
So
now
we're
talking
about
making
sure
that
the
tools
of
data
science
are
accessible
or
maybe
that
they
you.
You
know
the
usability
of
those
things
are
going
to
work
for
people.
So
thank
you
for
those
ideas
and
if
you
have
more
as
we're
talking
in
particular,
if
I
say
something
that
we're
doing
at
NC,
State
and
you're
doing
it
either
the
same
way
or
a
little
bit
a
different
flavor
of
it.
A
It
would
be
really
wonderful
if
you
continue
to
put
things
in
the
chat
so
that
this
isn't
just
me
telling
you
what
we're
doing
but
really
a
conversation
about
how
how
first
of
all
in
our
different
organizations
you
have
a
different
context.
So
you
may
need
to
do
things
in
different
ways,
but
also
we
want
to
give
each
other
ideas
and
I
just
have
one
set
of
ideas
about
what
you
might
might
do.
So
here's
my
set
of
ideas
that
I
tried
to
think
about
ahead
of
time
that
I
thought
you
all
might
bring
up.
A
So
one
thing,
I
think
a
lot
about
is
accessibility,
I,
think
about
the
colors
that
I'm
using
I
had
a
colleague
that
doesn't
see
any
color
at
all.
So
I
always
think
you
know.
Can
you
understand
what
I'm
trying
to
communicate
with
my
data
if
you
do
not
see
any
color
at
all
everything's?
Basically,
if
you
converted
to
grayscale,
is
this
going
to
be
readable,
as
well
as
all
the
other
forms
of
accessibility,
captioning
and
those
kinds
of
things
Learners
with
different
prior
experiences
and
goals?
A
B
A
Are
you
trying
to
get
to
with
this
data
science
experience?
You
all
mentioned.
Multiple
disciplines,
I,
think
another
aspect
of
making
things
for
everyone
is
not
having
a
one-size-fits-all
pathway
of
learning.
So
what
are
different
ways
to
create
Pathways
that
people
could
follow
to
get
where
they
want
to
go?
What,
if
you
really
mess
up
like
if
you
really
mess
up
because
of
whatever?
Is
that
going
to
be
a
fatal
problem
for
you
and
you're,
going
to
exit
or
you're
going
to
be
like,
oh
well,
I
fell
down.
A
I
gotta
stand
up
right,
you,
you
know
you're
on
a
team,
you
lose
one
game,
you
win.
The
next
game
you
know
is:
are
there
ways
to
help
people
and
we,
as
we
always
say
in
engineering,
fail?
Well,
we
don't
really
always
mean
that
right
in
Academia,
because
we
have
a
transcript
and
that
maybe
always
stays
there,
and
so
how
do
we
have
ways
that
people
can
try
things
out
and
also
have
a
lot
of
choices,
choices,
not
only
in
what
class
or
what
kinds
of
data?
A
Now
we
have
two
more
academies,
so
I
have
colleagues
to
talk
about,
but
still
pretty
much
we're
doing
all
the
things
first,
so
we
have
to
create
infrastructure
and
ways
of
doing
things
in
contracts
and
I,
don't
know
ways
of
getting
through
curriculum
committees
when
you're,
not
a
department.
You
know
always
for
the
first
time
and
then
we
we're
sort
of
groundbreakers
and
then
other
academies
can
follow,
which
is
great,
I
mean
we're
all
sitting
in
the
Provost
office
under
the
university
interdisciplinary
programs.
A
So
one
big
category
of
things
that
academies
do
now
that
we've
defined
as
research
enablement
and
what
this
means
is
we
we
might
bring
in
a
big
research
Grant,
but
more
likely
we
would
convince
in
a
group
that
would
try
to
bring
in
a
large
research
Grant
or
we
would
give
seed
grants
to
create
new
interdisciplinary
teams
across
the
university
and
I
know:
South
Big,
Data
Hub
also
does
this
kind
of
work,
so
it's
nice,
you
know
for
us
to
think
together,
like
how
are
our
seed
grants
working?
How
do
we
assess
them?
A
I
think
there's
some
really
creative
ways
of
assessing
proposals
that
are
also
more
inclusive.
For
example,
I
learned
of
one
person
in
the
Netherlands
that
they,
when
they
look
at
seed
grants
or
other
proposals,
they
decide
whether
they're
over
the
bar
or
not.
And
if
those
proposals
Are
Over
the
Bar
and
considered
fundable,
then
it's
a
lottery
and
they
just
do
the
lottery
until
all
the
money's
gone
and
I.
A
Think
that
that's
encouraging,
because
what
it
means,
especially
in
interdisciplinary
work,
is,
if
you
don't
have
someone
on
that
panel
that
can
especially
Champion
your
work
because
they're
in
your
discipline
or
don't
especially
hate
your
work
because
they're
in
your
discipline,
it
can
go
both
ways
right.
It
can
be
more
inclusive,
so
we're
just
thinking
a
lot
about.
How
can
we
run
our
processes
even
within
something
like
a
seed
Grant
to
be
more
welcoming
of
data
science
for
everyone?
A
We
help
people
find
each
other
and
we
also
have
a
big
Consulting
arm.
So
we
have
graduate
research
assistants.
This
is
a
program
with
our
libraries,
so
traditionally
you
think
of
going
to
a
library
to
borrow
a
book.
If
you
come
to
NC,
State
and
you're
part
of
our
community,
you
can
come
to
the
library
and
get
data
science
help.
You
can
say.
Why
is
my
code
broken?
You
can
say
how
can
I
make
these
six
spaghetti
codes
work
together
from
my
six
grad
students
or
undergraduate
students?
A
I,
don't
understand,
can
you
make
a
workflow
for
me?
I
can
say
I'm
trying
to
study
this
and
I,
don't
know
which
algorithm
I
should
be
thinking
about
for
this
type
of
data,
so
we
have
hourly
Library
employees,
we
have
joint
employees
and
then
we
have
Consultants
that
can
work
on
longer
term
projects.
So
this
is
really
exciting
and
we've
been
funded
by
the
Sloan
Foundation
to
think
about
this
more
broadly
through
a
national
group
of
people
that
run
Consulting
arms.
Like
this,
we
also
have
courses,
so
we
started
thinking.
B
A
They
feel
like
oh
I'm,
really
new
or
yes
I'm
into
it,
I'm
learning
it
or
I'm,
practicing
it
or
I'm,
even
innovating
it
like.
How
do
we
create
courses
at
all
of
these
levels
for
people,
so
we
we
have
three
course
levels,
and
these
are
just
some
of
the
names
of
the
courses
that
we've
stood
up
so
far.
All
of
these
courses
are
one
credit
classes,
they're
all
Project
based
they
have
a
rule,
no
quizzes,
no
tests,
which
is
another
way
that
we
think
we're
making
this
learning
more
accessible
to
Learners.
A
Who
may
be
more
shy?
We
also
think
it's
just
more
workplace
relevant
to
do
projects
and
homework
and
group
work
rather
than
quizzes
and
tests,
so
the
National
Science
Foundation
has
funded
four
postdocs,
so
we
are
hiring
right
now.
If
you
know
of
somebody
who
might
like
to
come
into
a
postdoc,
your
PhD
can
be
any
age.
A
So
you
we've
had
Department
chairs
inquire
about
our
post-doc
to
come
in
and
study
our
adapt
learning
model,
which
is
all
campus
data,
science,
accessible
project-based
teaching
and
learning,
which
has
three
elements
that
it's
all
Project
based.
I
already
mentioned:
identity,
conscious
choices,
and
then
we
have
10
Common
learning
elements,
and
those
are
things
like
wrestle
with
an
ethical
issue
show
an
open
problem
in
data
science.
So
we
have
10
things
that
we'd
like
to
have
happening,
no
matter
which
class
it
is
that
you're
taking
so
we're
super
excited.
A
The
the
applications
are
still
open
to
come
in
and
be
part
of
this
diverse
cohort
of
people
studying
our
learning
and
in
our
first
three
semesters,
we
already
Drew
from
all
the
colleges
of
the
University
and
and
we
had
faculty
in
our
very
beginning
classes.
We've
had
staff
in
our
classes,
we've
had
alumni
in
our
classes
and
they've
drawn
from
100
majors
and
over
50
programs
and
a
program
might
be
a
department
or
a
center.
A
A
So
we're
super
excited
that
our
Career
Services
Center
has
a
grant
to
put
people
in
20
internships
in
rural
North
Carolina,
and
we
will
be
for
the
first
time
introducing
a
data
component
into
these
internships.
So
super
excited
to
get
that
going,
and
we
also
have
some
funding
from
comap
to
bring
data
science
into
high
schools
that
have
never
done
anything
with
mathematical,
modeling
or
data
science.
A
A
Another
thing
we've
been
asked
to
do
on
campus
is
some
data
Jam,
so
this
AG
dated
science
jam
on
the
right
was
actually
for
more
senior
people
like
for
faculty
and
for
grad
students,
and
then
we
had
a
undergraduate
ASA
data
Fest
that
we're
doing
again
this
year
that
we've
opened
to
students
from
other
institutions.
So
if
you're
nearby-
and
you
want
to
bring
some
students
to
our
data
Fest,
some
of
these
are
very
competitive.
Ours
is
super
collaborative.
A
We
will
have
grad
students
around
helping
the
teams,
and
last
year
we
had
the
teams
helping
each
other.
So
we're
really
trying
to
create
a
community
of
fun
and
challenges
and
really
for
every
team
we're
going
to
try
to
recognize
something.
We
thought
they
did
really
well.
So
it's
not
just
about
winners
and
losers,
but
about
recognizing
what
you
did.
A
Another
thing
we
did
was
our
campus
has
career
fairs
that
are
very
subject
based
and
we
created
an
all-campus
career,
fair
for
data
science,
with
the
cooperation
actually
of
some
of
the
larger
career
fair.
So,
rather
than
being
viewed
as
competition,
they
actually
sent
some
of
their
employers
to
our
career.
Fair
because
it
was
smaller,
it
was
more
focused
and
so
again
in
a
very
collaborative
way,
building
that
up,
and
so
we
were
assisting
in
our
first
year
to
bring
in
four
different
grants.
A
We
actually
just
stopped
grant
writing
because
I
was
like
we
don't
even
like
have
the
staff
up
to
speed
on
being
able
to
you
know,
do
it
so
we
actually
had
to
pause
for
a
minute.
We
did
have
another
recent
award
from
the
North
Carolina
Department
of
Health
and
Human
Services,
but
but
I
mentioned
I.
Think
all
of
these
things
too
so
far,
so
data.org
is
super
interested
in
building
these
internships
and
capacity
and
financial
inclusion
and
other
kinds
of
aspects
of
bringing
a
diverse
group
of
Learners.
A
We
have
a
Consortium
that
are
almost
all
hbcus
and
msis,
except
NC,
State
and
University
of
Chicago.
So
we
feel
very
fortunate
to
be
included
in
that
group
of
really
excellent
institutions
that
can
teach
us
a
lot
and
then
color
map
does
the
K-12
the
slim
Foundation
does
the
Consulting
NSF
has
the
postdocs
and
then
in
Health
and
Human
Services
we're
doing
some
upskilling
for
them
of
their
employees.
So
we're
super
excited
to
partner.
A
We
would
love
to
have.
We
always
have
a
little
extra
room
in
our
one
credit
classes,
and
some
of
them
are
online.
So
there's
no
reason
your
students
couldn't
come
in
and
take
some
of
our
online
one
credit
Project
based
classes,
if
you
would
like,
we've
had
students
from
Meredith
College
already
taking
in
we're
working
with
Morehouse
on
having
some
students
take
some
internship
Focus
classes
in
the
future.
A
So
you
know
please
reach
out
if,
if
you'd
like
to
sign
students
up
for
a
credit
or
two
there's
probably
ways
to
do
that
and
I
look
forward
to
hearing
your
thoughts
on
how
we
can
make
good
on
our
promises
thanks.
So
much
for
the
opportunity
and
I
don't
know
if
we
have
a
little
time
but
I'd
just
love
to
hear
what
people
are
up
to.
A
D
D
D
But
in
your
process
of
looking
at
facilitating
research
as
well
as
teaching,
do
you
see
where
you
sit
in
the
university
to
be
a
benefit
for
that
for
so,
for
instance,
if
you're
in
an
office
of
research,
is
there
a
more
clear
focus
on
Research
or
on
teaching
or
on
this
Consulting
piece,
I
see
really
kind
of
three
arms,
and
we
have
another
question
too.
So
do
you
have.
A
E
A
Know,
I,
don't
think
so.
I
think
I
mean
there
are
there
in
any
large
Institution
for
sustainability.
You
have
to
think
about
which
pieces
bring
in
resources
and
what's
the
source
and
what
to
think
and
undergraduate.
Education
is
generally
not
a
source
right
in
our
institutions,
because,
especially
in
data
science,
the
amount
you
pay,
somebody
to
teach
data
science
is
greater
than
what
you
can
bring
in
right
through
any
kind
of
disbursement
of
funds,
and
so
that's
a
huge
challenge.
A
So,
if
you're
going
to
be
really
robust
and
wonderful
with
your
undergraduate
education
and
do
all
these
things,
you
have
to
think
about
what
other
kinds
of
things
you
can
do
to
support
them,
or
they
can
be
strategically
funded
right
as
as
part
of
just
the
University's
Mission,
and
so
these
are
the
things
that
we
think
about
a
lot
and
the
balances.
Well,
it's
actually
good
to
have
a
portfolio,
that's
mixed,
so
that
you
know
some
aspects
of
things
may
be
able
to
support
other
aspects.
A
So
that's
been
super
fun,
so
we
have
faculty,
we
have
postdocs,
we
have
grad
students,
we
have
alumni,
we
have
people
in
industry
and
government
and
yeah,
and
so
really
every
everybody
that
I
can
meet
I'm,
always
in
recruitment
credit
class.
Everyone
I
meet
I,
pretty
much
say
you
know
that
I
think
is
an
excellent
communicator
and
that
has
serious
data
science,
chops,
you're,
probably
going
to
get
an
offer
from
me
to
teach
you
one
credit
class
and
not
just
teach
one
but
design
ones,
and
for
the
people
in
Industry.
A
You
know
it's
really
fun
to
say
you
know,
what
do
you
need
the
most?
What
are
you
missing
in
the
people
that
you're
hiring
come
teach
one
credit
you
know,
and
then
you
can
scout
people
and,
and
you
can,
you
can
hire
from
there
and
that's
been.
That's
been
really
super
fun.
We
are
offering
right
now,
20
credits,
each
semester,
so
40
a
year,
so
it's
pretty
many
pretty
many
units
and
it's
all
online.
A
What
the
names
of
those
classes
are
and
then
I
am
reading
the
long
comment
which
I
really
appreciate
communicating,
successes
and
failures.
A
school
was
reporting
that
women
weren't
enrolling
in
certain
grad
programs
because
of
being
scared
of
tech,
and
they
looked
at
the
people
who
didn't
choose
to
enroll.
Instead
of
focusing
on
the
people
who
had
enrolled
like
the
majors.
A
And
it
says,
unfortunately,
the
stats
are
not
always
maybe
always
reported
or
collected
in
that
way.
Yeah
and
and
yeah
definitely
not
a
critique.
We
have
to
talk
about
our
struggles
right,
but
if
I,
if
my
intonation
didn't
emphasize
the
right
things,
please
correct
me,
because
sometimes
your
intonation
might
make
what
you
said
sound
different
than
what
you
intended
to
say.
B
Oh
all
I
was
actually
saying
is
that
the
focus
of
some
things
that
weren't
going
well
was
men
versus
women
and
people
who
were
who
had
been
accepted
to
a
program
but
did
not
enroll,
and
they
were
not
also
looking
at
things
like
what
were
the
majors
of
these
people,
so
they
were
identifying
women
as
being
scared
of
technology
and
not
perhaps
liberal
arts
Majors
being
scared
of
Technology.
They
didn't
keep
those
kind
of
stats.
B
So
what
language
reported
back
was
women
aren't
rolling
in
our
program
because
they're
scared
of
tech
I
teach
a
lot
of
students
who
are
in
a
master's
program
in
analytics,
and
there
are
lots
of
lots
of
women
there
so
that
that
was
an
overstatement
and
it
was
an
attempt
to
to
bring
women
into
the
program
actually,
but
what
it
did
for
future
women
that
may
be
looking
at
the
program
is
going.
Oh
well,
this
school
reports
back
that
that
women
don't
come
that
women
may
even
feel
that
they're
not
welcome
yeah
barrier
there.
B
A
Know
that
wow,
thank
you.
So
much
I
really
appreciate
that,
and
it
has
such
a
wonderful
data
component,
like
you
know
why
we
need
statisticians
in
the
room
in
particular
kind
of
component
there.
The
other
thing
you
made
me
think
is
that
maybe
the
women
got
better
offers
elsewhere,
because
they're
in
great
demand.
B
A
B
You
know
again,
don't
know
exactly,
but
the
stats
as
it
was
reported
to
a
meeting,
was
just
women
don't
come
because
they're
scared
of
technology
and
so
a
very
broad
over
generalization
there
and
again
yeah.
My
concern
really
was
future
women
who
might
hear
that
yeah,
it's
very
Tech
oriented
you
know,
might
get
a
very
unfortunate
outcome
from
that.
So
again,
thank
you
for
all
your
time,
yeah.
A
And
it's
interesting
to
think
like
how
you
might
get
some
of
the
stories
of
your
women
students
that
out
there
you
know
what
I
mean
like
what
could
account
with
messaging
proposed
sort
of
campaign.
Look
like
for
you
to
get
some
positive
stories
out
there,
that
students
could
see
about
the
awesome
things
your
women
are
doing
in
Tech
and
how
not
scared
of
it.
They
are,
and.
B
I
will
say
on
a
very
positive
note
for
Georgia
Tech
Georgia
Tech
really
does
attempt
to
get
that
positive
message
out
there,
and
so
it
was
very
odd
to
also
hear
some
reporting
that
that
seemed
to
be
from
a
slightly
different
perspective.
But
but
Georgia
Tech
really
does
try
to
get
that
positive
messaging.
B
Oh
certainly
during
that
meeting
I
was
was
asking
the
person
to
break
down
those
statistics
and
that's
how
I
found
out
that?
Oh
so
these
are
women
who
didn't
come
to
a
particular
program
but
were
accepted,
which
hadn't
been
part
of
the
message
originally,
but
also
that
they
didn't
know
if
these
were
liberal
arts
Majors
that
were
making
these
statements
or
if
these
were
people
that
had
a
degree
in
math.
You
know
or
engineering
undergrad.
So.
A
Yeah
I
love,
I,
love,
I,
love,
your
thoughtfulness
and
I.
Also,
just
it's
just
a
good
cautionary
tale
for
all
of
us
who
care
about
data.
You
know
to
work
with
the
folks
that
are
working
with
our
data
that
we
really
care
about
to
think
with
them
about.
You
know
how
to
because
you
know
some
of
these
things
are
done
with
the
best
of
intentions.
As
you
said
this,
this
was
with
really
seriously
good
intentions
right
and
then
you
know
if
there
could
be
potentially
unintentional
consequences.
A
How
do
we
just
build
those
bridges
so
that
that
people
will
come
and
talk
to
us
about
things
in
the
future
and
view
us
as
a
resource
for
these
kinds
of
things
we're
doing
a
lot
of
interviewing
of
students?
And
so
that's
what
led
me
to
like
thinking
about
how
rich
the
information
we're
getting
when
you
kind
of
go
beyond
the
stats
and
then
start
asking
some
questions
about?
Why
did
you
take
this
class
or
why
did
you
take
this
class
instead
of
that
class?.
B
In
that
particular
meeting
and
I
think
this
is
true
in
a
lot
of
different
spheres.
Is
why
I
bring
it
up,
but
the
particular
person
who
was
reporting
back
these
stats
I,
don't
think
particularly
had
a
stem
background
and
so
was
reporting.
You
know
back
something
and
then
realizing
that
he
actually
had
more
information,
and
so
it
became
a
very
insightful
thing
during
this
discussion.
B
But
again,
if
that
one
original
message
had
gotten
other
women
don't
come
because
they're
scared
of
Technology,
it
would
have
been
very
unfortunate
so
again
having
more
of
a
discussion
instead
of
trying
to
just
have
a
sound
bite
and
if
it's
a
sound
bite
being
very
careful
what
the
sound
bite
is
and
again.
Thank
you
for
all
the
work
that
you
do.
D
C
C
The
second
question
is,
for
all
those
one
credit
courses.
Are
you
getting
people
to
make
them
in
in
models
or
methods
that
are
shareable
or
or
sort
of
like
transferable
to
other
places?
So
at
Trinity,
we've
been
looking
to
sort
of
implement
some
small
some
of
these
sort
of
small
courses
in
much
of
the
same
way
that
low
level
where
we,
we
can't
run
the
Opera
ones,
but
it
would
be
awesome
if
we
were
able
to
be
pulling
materials
for
some
of
those
one
credit
classes
and
reusing
them
in.
A
A
A
teaching
community
that
meets
every
other
week
and
you
know,
has
support
for
teaching
in
in
this
way,
and
we
would
welcome
that
sort
of
broadening
the
the
use
of
the
the
courses
and
the
materials
so
just
reach
out
anybody.
If
you
want
to
think
through
what
that
would
look
like
and-
and
we
can
talk
about
it-
okay.
E
So
thanks
for
answering
my
question
earlier,
but
I
had
another
question
about
the
the
program
itself,
because
when
I
left,
NC
State
I
think
they
had
or
were
building
a
data
science
College
already
like
a
separate
one.
Did
the
Provost
office
like
initiate
creating
this
Academy
or
this?
How
we're
trying
to
create
more
of
a
data
science
thing
here
at
Fresno,
State
as
well.
A
Well,
first
of
all,
I'm
happy
I.
Think
a
good
way
to
do
it
is
like
I
was
I,
definitely
definitely
been
a
lever
for
change
at
other
institutions
by
just
coming
in
because
you
get
to
talk
to
the
upper
Administration,
sometimes
on
those
report
outs
at
the
end,
and
so
that
can
be
a
way
to
reach
upper
Administration
with
a
different
voice
than
the
inside
voices.
That
are
always
saying
things,
and
sometimes
you
know
definitely
in
program
review.
A
Had
upper
Administration
say:
oh,
you
know
I
think
they've
been
saying
that,
but
I
never
quite
heard
it
before
the
you
know
in
the
way
that
you're
saying
it.
So,
let's
talk
about
you
know
like
how
how
how
we
could
exchange
ideas.
I
think
San
Antonio
is
the
University
of
San.
Antonio
is
also
doing
some
interesting
things
through
the
Provost
level.
I,
don't
think
NC,
State
ever
well,
I,
don't
know,
but
I
haven't
been
aware
of
it.
Starting
a
new
school
of
data
science.
We
have
very
rich
programs.
A
A
So
it's
another
way
of
sort
of
building,
credentialing
that
that
leans
on
these
one
credit
classes,
but
then
folds
in
with
departmental
initiatives,
and
then
the
department
gets
to
to
name
the
certificate
and
and
sort
of
own
it
in
that
way,
but
we
will
administer
it,
and
so
this
is.
This
is
just
something
where
we're
starting
up
right
now,.