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From YouTube: Education and Workforce WG: Data Science for Social Justice: An Approach to Broaden Participation
Description
Dr. R.N. Uma
Talk Title: Data Science for Social Justice: An Approach to Broaden Participation
Bio: Dr. R. N. Uma is a Professor of Computer Science in the Department of Mathematics and Physics at NC Central University, Durham, NC, USA. Her research interests include data science, scheduling and resource allocation with applications to cloud computing, robotics, wireless sensor networks, multimedia networking, and large logistics problems. Her research has spanned from purely theoretical to experimental and simulations. She has published several papers in leading conferences and journals, and a book, in her areas of expertise. On the education front, she is interested in increasing the enrollment and retention of minority students, particularly in the Mathematical Sciences including Computer Science, and towards this end has implemented several federally funded projects. She has served her professional community in a variety of roles including guest editor, TPC Co-Chair, and Publications Chair in addition to routine roles such as TPC Member, Reviewer and Session Chair. She received her BSc degree in Mathematics from the University of Madras, Chennai, India, her ME degree in Computer Science from the Indian Institute of Science, Bangalore, India, and her PhD degree in Computer Science from NYU Tandon School of Engineering, New York. She is a senior member of IEEE.
A
And
our
department
of
education
grant
to
get
more
faculty
exposed
to
data
science
and
to
get
them
to
incorporate
projects
using
data
science
in
their
disciplines
in
their
respective
disciplines.
And
finally,
we
are
also
using
it
as
a
tool
for
broadening
participation,
and
this
will
be
the
focus
of
today's
talk.
A
A
So
we
looked
at
how
we
can
use
social
justice
to
alleviate
this
problem,
and
research
has
shown
that
minority
students
are
motivated
by
what
is
referred
to
as
an
equity
ethic,
rather
than
a
big
paycheck.
So
they
want
to
pursue
stem
if
they
can
utilize
their
stem
expertise
to
address
the
social
injustices
and
there
was
a
northeast
scientific
training
retreat.
A
I
think
this
was
back
in
2014,
where
they
invited
stakeholders
from
across
the
academic
spectrum,
all
the
way
from
undergraduate
students
all
the
way
up
to
faculty
and
tried
to
get
them
to
identify
the
ways
in
which
we
could
improve
the
participation
and
the
first
of
their
eight
actionable
items.
Out
of
this
retreat
was
to
adapt
stem
training
to
involve
a
social
justice
component
and
the
final
motivating
factor
is.
We
all
know
that
by
connecting
stem
to
real
world
problems
that
will
help
us
retain
a
diverse
stem
student
body.
A
So
with
all
of
this,
we
decided
to
use
social
justice
to
get
more
students
involved
in
stem
and
we
are
using
data
science
as
a
vehicle
to
deliver
that.
So
we
incorporated
it
into
our
first
year,
seminar
course
and
as
part
of
the
course,
students
were
anyway
expected
to
do
a
group
project
on
a
social
justice
issue,
so
they
had
to
identify
the
issue,
describe
the
issue
and
say
why
that
issue
was
important
and
possibly,
if
possible,
provide
solutions
or
recommendations
as
to
how
they
would
go
about
addressing
the
issue.
A
A
This
is
actually
verbatim
from
a
student
report,
so
this
particular
student
looked
at
police
brutality
issue
and
a
statement
in
the
report
said:
black
people
were
24
of
people
killed
by
cops,
and
the
student
clearly
got
this
number
from
some
article
on
the
internet
or
somewhere,
and
so
what
we
want
them
to
do
is
to
we
want
them
to
visualize
the
data
themselves
and
come
up
with
these
numbers
by
analyzing.
The
data
they
are
given
so
towards
that
end
for
identified
social
justice
issue.
A
We
provide
an
overarching
research
question
and
in
the
case
of
the
fatal
shootings
by
police
officers,
the
question
could
be
what
was
the
impact
of
the
black
lives
matter
movement
and
are
other
police
departments
doing
anything
with
the
data
being
collected?
How
are
they
addressing
it?
So
this
would
be
an
overarching
research
quest
and
we,
of
course,
don't
expect
the
students
to
address
this
question
in
a
first
year
seminar
course,
but
they
have
an
idea
of
what
they
can,
how
far
they
can
reach
with
that
data.
A
So
this
is
just
a
snapshot
of
the
data
that
was
downloaded
from
washington
post,
which
is
maintaining
this
data
set.
So
you
had
the
name
of
the
victim.
The
date
the
victim
was
killed,
the
manner
of
death,
whether
the
victim
was
armed
or
not,
the
age,
gender
race
city,
state,
etc,
whether
they
showed
any
signs
of
mental
illness
threat,
level,
etc.
A
So
this
is
the
data
washington
post
is
collecting,
and
so
we
clean
this
data
set
so
that
it
is
manageable
by
students
because
remember
our
target
audience
in
this
case
is
first
year
freshman
students
and
from
not
necessarily
a
stem
major.
So
they
could
be
any
major
because
all
students
are
in
our
university
are
required
to
take
that
course,
and
we
provide
guiding
questions
in
a
differentiated
fashion.
So
the
easy
questions
we
expect
all
students
to
be
able
to
address
the
intermediate
questions-
probably
not
this
first
year,
seminar
course.
A
Whereas
an
intermediate
question
could
require
them
to
involve
extra
data,
sets
that
were
not
provided
to
them,
such
as
demographics
of
the
state,
and
see
how
that
impacts,
the
race
of
the
victim
and
for
the
truly
advanced
students
or
for
an
advanced
course.
The
question
could
be
to
predict
the
outcome,
given
the
characteristics
of
a
potential
victim.
So
if
we
could
predict
the
outcome,
we
could
utilize
that
to
provide
training,
because
we
can
identify
the
characteristics
and
say:
okay,
this
set
of
characteristics
is
likely
to
lead
to
an
adverse
effect.
A
A
Students
in
this
course
who
were
participating
in
this
were
able
to
create
a
chart
which
now
shows
victims
by
ways,
and
if
you
recall,
prior
to
this
in
an
earlier
year,
your
student
had
said
24
of
the
victims
were
blacks,
and
now
they
can
see
that
they
are
able
to
see
that
firsthand
from
the
data
from
the
raw
data,
and
one
interesting
observation
that
I
encountered
a
couple
of
semesters
ago
is
when
the
student
generated
this
graph
and
they
saw
that
about
46
of
the
victims
were
white.
His
immediate
reaction
was
no.
A
A
So
what
we
are
doing
through
this
project
is
we
identify
a
social
justice
issue
and
we
acquire
data
related
to
that
issue.
For
the
most
part,
our
data
set
is
publicly
available,
and
then
we
clean
the
data
and
transform
it
and,
if
needed,
either
create
the
data
dictionary
or
update
the
data
dictionary
and
also
we
provide
a
research
question
and
guiding
questions,
and
by
the
end
of
this
we
have
created
a
curated
data
set,
and
we
have
done
this
for
several
different
social
justice
issues.
A
So
now,
along
with
the
curator
data
sets,
we
have
also
made
tools
available,
so
code
app
is
a
tool
that
is
an
online
tool
for
analytic
data
analysis.
So
we
provide
a
basic
demo
tutorial,
so
students
get
familiar
with
using
that
tool
and
also
some
sample
r
code.
If
someone
wants
to
take
a
deeper
dive
using
r,
because
there
are
some
limitations
with
code
app
and
our
future
work
is
to
create
lesson
plans
so
that
it
makes
it
easier
even
for
middle
school
teachers
to
incorporate
this
in
their
classes.
A
A
So
if
I
go
into
the
projects,
so
we
have
data
provided
for
all
of
these
projects,
a
couple
of
them,
the
child
abuse
data.
I
cannot
make
it
publicly
available
because
I
do
need
students
in
dealing
with
that
project
to
sign
a
confidential
agreement,
so
they
can
contact
me
to
get
the
data
and
this
one
cost
of
college
education.
We
are
still
working
on,
but,
for
instance,
if
we
look
at
the
fatal
police
shootings
data,
the
data
is
made
available
through
fixed
share,
and
so
you
can
see
there
is.
A
So
we
took
the
data
and
split
it
into
sub
data
sets,
because
this
also
encourages
group
work.
The
different
students
in
a
team
can
focus
on
the
different
parts
of
the
data
and
then
they
can
get
together
and
see
put
their
results
together
to
get
a
bigger,
fuller
picture
of
the
underlying
data.
So
we
also
have
the
code
book.
A
In
some
cases
it
may
be
very
minimal,
just
saying
what
the
variables
are
and
in
this
example
the
variables
are
self-explanatory.
So
we
didn't
have
to
give
any
detailed
explanation,
but
we
do
have
the
link
where
the
data
was
downloaded
from
and
the
date
the
data
was
downloaded
and
we
provide
the
list
of
guiding
questions.
This
is
just
to
help
the
students
get
started
so
that
they
have
a
kind
of
a
path
laid
out
for
them,
and
this
is
one
of
the
data
sets.
So
you
can
see
the
name
date
some.
A
A
And
if
you
look
at
the
tutorials
page,
we
do
have
us
people
who
are
totally
new
to
data
science.
We
have
collected
a
few
introductory
videos
that
they
can
watch
to
gain
an
understanding
of
data
science
and
the
issues
with
big
data,
and
we
have
a
kodak
tutorial
that
gives
us
a
youtube
demo,
video
along
and
it
allows
the
student
to
work
along
with
the
with
a
sample
data
set
and
additionally
with
r.
A
If
someone
wants
to
do
a
deeper
dive
with
r,
we
are
also
providing
sample
r
code
and
the
idea
with
the
sample
r
code
is
to
kind
of
do
a
cut
and
paste.
So
if
a
student
wants
to
create,
say
this
kind
of
a
bar
chart
for
the
heart
disease
data,
it
was
number
of
patients
by
chest
pain
type.
Here
we
will
want
number
of
victims
by
race,
so
they
just
need
to
copy
this
block
of
code
here
and
wherever
they
have
chest
pain.
A
They
need
to
change
it
to
race,
and
so,
if
they
change
patients
to
victims
by
phase
and
race,
they
will
get
the
graph
that
we
looked
at
earlier.
So
we
were
trying
to
make
it
easy
for
the
student
to
manipulate
the
r
code,
so
we
provided
our
code
for
different
kinds
of
graphs,
histograms,
stacked
and
grouped
bar
charts,
etc
so
that
they
can
decide.
A
So
that
was
the
idea
in
providing
the
sample
r
code,
but
for
freshman
year
our
experience
was
the
sample.
R
code
was
a
little
too
much
because
they
were
new
to
programming,
but
code
app
worked
well
the
following
year.
We
introduced
code
app
and
that
worked
well,
because
code
app
allows
them
to
generate
a
graph
by
just
dragging
and
dropping
the
variables
onto
it.
A
And,
additionally,
with
data
up
and
the
department
of
education
grant.
My
colleague
and
I
we
are
getting
trained
to
become
trainers
through
carpentries,
so
that
we
can
conduct
the
data
science
workshops
on
our
campus
and
elsewhere
to
get
more
faculty
introduced
to
data
science
and
get
them
ready
to
the
point
where
they
can
incorporate
data
science
projects
into
their
various
disciplines.
A
Along
with
the
social
justice.
Curator
data
sets
that
we
provide.
We
are
hoping
that
that
will
make
it
easy
for
instructors
to
implement
it
at
maybe
as
just
a
single
project
in
their
course
or
even
as
a
project
based
course
at
a
freshman
level,
and
even
possibly
middle
school
and
high
school
levels.
B
C
A
Yeah
we
did
discuss
excel,
but
then
with
excel
two
for
our
students,
we
would
have
had
to
do
a
training
for
that.
So
initially,
when
we
started,
we
probably
naively
thought
that
if
we
provide
sample
r
code,
it
makes
it
easier
and
we
have
a
wider
array
of
functionality
available
through
our
packages.
A
D
Thank
you
for
that
wonderful
presentation,
I'm
so
excited
to
be
able
to
use
those
resources
that
you've
provided
in
some
of
our
classes.
So
thank
you.
One
thing
that
we
have
been
trying
to
do
with
data
science
and
public
affairs.
Schools
of
public
affairs
is
share
courses
in
a
consortium
model
so
that
we
can
first
maybe
co-teach
a
design
course
that
another
institution
has
has
created,
and
we
have
one
that
I
taught
I'll
put
it
in
a
chat.
D
It's
just
basic
data
coding
and
r,
using
public
sector
examples,
sort
of
an
intro
to
data
data,
science
type,
of
course.
But
what
was
really
great
about
this
is
a
colleague
of
mine,
who's.
Now
at
arizona
state
university
essentially
built
this
out,
I
was
able
to
as
teach
it
alongside.
You
know
him
being
sort
of
my
mentor
as
I
was
teaching
it,
but
it
was
all
ready
to
go.
D
You
know
I
was
just
basically
putting
it
into
our
lms
and
it
it
was
just
so
easy
for
me
to
learn
and
teach
at
the
same
time,
but
have
the
whole
course
you
know
built
out
in
15-week
plan
or
whatever.
So
this
is
actually
a
seven
week
plan,
so
I
was
wondering
if
you
have
you
know.
I
know
that
you
have
your
your
the
site
that
the
google
sites
that
you've
showed
us,
but
a
plan
to
this
is
built
out
and
github,
for
example,
to
do
something
similar.
A
Yeah
so,
as
I
mentioned
the
lesson
plans,
we
will
be
working
on
it
this
coming
year
to
go
with
each
of
the
data
sets
so
that
it
makes
it
easier
for
others
to
incorporate
it
apart
from
I
mean
so,
they
would
rather
be
individual
modules,
but
we
can
also
think
of
putting
together
a
semester-long
course
with
all
of
these
projects.
Yes,
thanks
for
that
suggestion,
I'll
check
out
your
link.
B
So
question
right
now:
do
you
implement
them
a
different
one?
Every
time
you
run
it
or
do
you
actually
implement
them
all
over
the
course
of
this
course
that
you
said
everyone
has
to
take
so
you
have
it
in
a
general
education
course,
and
do
you
rotate
it
three
years
which
project
you
do
or
how
do
you
incorporate
the
different
modules.
A
So
it's
a
general
education
course,
so
we
offer
about
30
sections
of
that
course
so
about
two
or
three
sections
work
with
us
on
this
format.
So
for
those
two
or
three
sections
we
do
make
available
for
whatever
reason
the
students
want
as
many
choices.
So
we
have
made
available
all
of
the
data
sets.
We've
had
up
to
that
time
so
that
the
students
could
chew
have
a
wider
choice
to
select
from,
and
in
fact
several
of
these
topics,
barring
possibly
covert
19.
A
So
we
had
a
rough
idea
based
on
what
they
had
been
doing
in
the
past,
but
prior
to
starting.
We
also
surveyed
the
students
and
they
were
the
ones
who
listed
cost
of
college
education,
child
abuse.
They
were
very
particular
about
child
abuse
because
it
took
me
a
while
to
get
that
data
with
all
the
permissions
etc.
A
So
they
were
particular
about
some
of
the
datasets
because
they
really
wanted
to
work
on
that
and
one
other
dimension
that
we
will
be
adding
moving
forward,
which
was
a
recommendation
from
our
advisory
board.
Members
is
to
also
incorporate
qualitative
data
or
collect
their
personal
experiences
in
that
subject
and
to
incorporate
that
in
some
fashion
into
this
mix,
because
the
data
set
we
have
is
just
quantitative
which
is
coming
from
an
outside
entity,
and
the
students
will
have
a
better
buy-in
when
they
are
able
to
share
their
experiences
related
to
that
topic.
A
So
so
that
I
mean
we
need
to
think
more
as
to
how
we
would
implement
that,
because
it
does
get
sensitive.
And
so
we
need
to
work
with
other
colleagues
who
have
been
in
this
field
to
see
how
to
collect
that
qualitative
data
without
violating
the
student's
privacy,
as
well
as
without
any
other
legal
validification.
E
Thank
you
so
much
for
the
great
talk
that
was
that
was
really
really
great,
and
so
I
I
do
a
fair
bit
of
of
of
work
with
middle
schools
and
high
schools
that
I
think
I
I
yeah.
I
I'm
not
sure
how
to
work
that
in
but
but
yeah
I
would
love
to.
I
is,
is
this
on
the
web
anywhere?
I
I
know
that
we'll
post
your
video
here.
A
B
Yeah
and
I
we
should
record
you-
know
these
ideas
about
co-teaching
or
and
also
about
modules
for
younger
younger
age
groups
too.
You
know
these
are
things
that
you
know
shameless
plug.
You
can
put
in
to
do
as
part
of
the
working
group
and
the
volunteer
forum.
I
think
it's
like.
I
think
these
would
be
very
valuable
and
it
sounds
like
there's
there's
interest.
Sorry
adam,
go
ahead,
no.
E
F
E
E
Yeah
we
have
a
we
developed,
a
short
introductory
jupiter
notebook
that
that
seemed
to
to
do
what.
I
think,
your
your
pain
point
of
trying
to
figure
out
how
to
you
know
the
the
on-ramp
for
younger
students
to
get
working
with
this.
We
may
have
something
that
will
help
but
yeah.
I
can't
wait
to
look
at
this
more
deeply.
This
is
really
cool.
B
Yeah,
it
would
be
really
interesting
to
get
your
you
know
comparing
like
how
to
get
students
into
coding
at
that
very
young
age
of
middle
school
and
then
how
the
dragon
drops.
You
know
where
what
that
level
is
we're
very
interested
in
that
best
practice.
You
know
where
to
the
on
ramps,
how
to
on-ramp
people
into
those
into
these
different
types
and
whether
the
drag
and
drop
and
there's
a
lot
of
different
ways
that
people
on-ramp
and
some
of
them
may
all
work.
You
know
thinking
about
that.
B
F
Yeah,
I've
used
code
app
a
lot
and
we
use
it
with
younger
students
in
high
school
students
a
lot.
It
works
in
the
browser
which
is
great
when
schools
only
have
chromebooks
and
things
like
that.
It
is
also
built
by
some
of
the
same
people
who
did
fathom
if
you,
if
you
know
that
software
from
statistics
educate
the
statistics
education
world,
and
so
it's
built
on
kind
of
a
whole
bunch
of
educational
principles,
actually
about
getting
students
and
younger
students
to
interact
with
technology
and
statistics.
B
So
anybody
anyone
else.
Thank
you
so
much.
This
has
actually
been.
You
know
really
interesting.
Also
for
you
know
as
we're
thinking
about
other
participants
in
the
data
up,
it
may
be
good
to
talk
about
you're,
making
me
think
that
we
might
want
to
think
talk
about
what
you're
doing
and
how
you're,
using
it
for
faculty
development
and
maybe
even
in
the
in
the
group
in
the
booths
or
the
for
the
all
hands.
B
But
if
there
are
particular
topics
that
are
of
interest
to
you
now
that
are
whether
you
want
to
work
on
them
yourself
or
not,
but
that
are
burning
questions
for
you
or
things
that
are,
you
think
could
be,
can
help
scale
up
or
help
elevate,
the
things
you're
doing
or
just
work
that
you
think
is
critical.
That
should
be
done
in
this
space,
so
for
one
it's
just
going
around
and
seeing
where
you
are
now
so
I
have
people
on
my
screen
and
I'm
sure
it's
mixed
up
on
everyone's,
but
monica
you're.
H
I
So,
as
I
mentioned
sort
of
at
the
beginning,
I'm
on
wrapping
up
my
sabbatical,
which
was
I
I'm
my
phd,
is
in
math.
So
I
I
mean
I
learned
python,
maybe
a
couple
years
ago,
just
for
fun,
I
guess
never
really
had
any
intention
of
doing
data
science,
but
carl
schmidt
left
valparaiso
university
and
now
we
have
a
hole.
So
luckily
my
sabbatical
came
up,
so
I
could
plug
that
hole.
I
I
I
B
Yeah,
that's
interesting,
yeah
how
to
make
these
short
shorter
pieces,
where
you
don't
have
the
whole
semester
for
a
group
project
or
you
don't
have
a
whole
course.
You
just
have
a
module
within
a
smaller
within
a
different
course.
Yeah!
That's
that's
interesting.
I
know
adam
yours
is
very
it's
like
a
module
within
a
middle
school
class,
so
it's
even
it's
shorter,
but
doing
it
for
like
lab
style,
and
that
may
be
one
topic.
B
You
know
if
somebody's
like
practical
teaching,
but
in
that
modular
form
yeah
so
mark
you
made
me
think
carl
schmidt.
He
just
sent
me
an
email.
He
was
saying
he
wanted
to
be
here
today.
He
had
a
meeting
and
he's
going
to
fill
out
the
volunteer
form.
So
I'm
sure
we
will
hear
what
he
is.
His
ideas
too.
You
just
mentioned
his
name
and
he
said
to
tell
everybody
hi
so
mark
you're.
Next
time.
C
C
With
the
rise
of
the
temperature,
so
there
are
some
dc's
in
the
water
and
the
coral
tend
to
bleach.
So
I've
been
working
on
the
environmental
data
science
for
coral
bleaching
prediction,
yes,
a
problem
and
also
is
to
decide
if
we
teach
the
breadth
of
the
death
of
data
science.
Like
john
said.
Yes,
it's
very
difficult
to
decide.
So
we
have
two
courses,
basically
one.
I
teach
the
breadth
first
and
then
you
have
a
more
advanced.
We
go
in
more
depth
and
we
use
some
projects
and
data
sets.
C
We
can
get
from
the
university
of
california
elvin
they
have
data
sets.
We
use
also
that
asset
from
kaggle
and
we
tried
also
to
use
it
asset
from
noaa,
since
we
have
buoys
in
the
caribbean.
So
we
try
to
do
relevant
applications
and
projects
for
the
students
that
they
can
see
direct
impact
in
the
in
their
environment,
yeah
in
which
they
do.
C
B
Yeah
so
yeah
thank
you
so
adam
you're
next
and
also
as
we
go
around
if
there's
something
you
know
about,
that's
that
someone
other
has
talked
about
like
if
you've
done
a
short
lab
course
or
something
like
that
at
breath
or
depth.
I
mean
we're
kind
of
doing
a
round
table,
but
also
you
know,
there's
a
lot
of
expertise
here
in
in
teaching
in
different
areas.
So
yeah.
E
Hey
thanks
yeah,
I
I
I
have
my
fingers
in
a
few
different
pots.
I
guess
a
middle
school
level
with
students
at
the
high
school
level,
with
teachers
primarily
through
the
corknet
high
energy
physics,
outreach
program
based
at
fermilab
in
notre
dame
and
that's
that's
really.
My
my
summer
priority
is
is
doing
teacher
workshops,
but
in
a
former
life
I
was
a
high
school
teacher
as
well,
and
so
we
use
google
collab
for
for
almost
all
of
that
stuff.
C
E
Having
a
monoculture
that
right
of
being
entirely
dependent
on
that
one
platform,
but
that
that
is
has
been
a
really
nice,
easily
implementable
platform
for
things
like
yeah,
for
middle
school
and
high
school
and
then
at
the
university
level.
Now
I
have
our
our
undergraduate
physics,
students
in
physics,
one
and
two
do
a
little
bit
of
this
stuff.
E
You
know
outreach
or
broader
impacts
activity
just
to
plug
in,
and
it
seems
pretty
it
so
far
it's
been
pretty
adaptable
because
we
can
just
take
any
sort
of
groups,
data
right,
whatever
the
research
is
and
then
now
we
package
that
as
a
as
sort
of
a
k-12
educational
module.
So
anyway
I
love
hearing
about
all
this
stuff.
You
all
are
brilliant
renata,
thanks
again
for
coordinating.
I
J
J
I
go
from
one
meeting
to
another
but
hi,
I'm
florence
woods,
I'm
actually
an
accounting
professor
at
stillman
college
in
tuscaloosa
alabama,
and
we
also
incorporated
data
analysis
into
our
business
programs
and
the
dean
of
our
department
also
is
the
dean
of
our
math
department.
So
we
have
we're
trying
to
make
sure
we
have
a
clear-cut
line
between
data
science
and
data
analysis,
and
so
I
am
not
a
data
scientist
I
am.
I
am
learning
more
than
I
thought
I'd
ever
want
to
learn
about
it.
J
But
analysis
is
very
important
in
the
business
world
and
of
course,
all
the
major
cpa
firms
are
looking
for
graduates
who
know
how
to
analyze
and
manipulate
data,
and
so
it's
very
important
to
my
students,
and
so
I'm
looking
and
I'm
glad
to
be
part
of
this
group,
because
I'm
glad
to
be
around
people
who
know
what
they're
doing
so.
I
can
share
that
with
my
students.
B
J
K
K
Most
recently,
one
is
to
try
to
understand
what
is
actually
being
taught
across
the
entire
university
in
data
science
and
data
analytics
and
we've
developed
a
software
package.
That,
in
fact,
was
just
sent
up
to
the
us
patent
office
last
week
that
we've
used
to
map,
if
you
want
to
create
an
eco
map
of
the
entire
over
40,
actually
5300
courses
across
the
university
to
dive
into
those
course
descriptions
and
from
that
produce.
K
What
looks
like
a
concept
map
for
those
that
are
familiar
with
that
showing
different
islands
or
modules
of
types
of
data,
analytic
tools
and
approaches,
etc
that
are
being
taught
by
every
professor
in
every
department
across
the
university.
So
it's
a
way
of
trying
to
understand.
If
you
look
at
the
university
as
a
maze
of
opportunity
with
someone
coming
in
what
is
it,
what
are
the
permutations
that
could
be
available
for
them
to
complete
their
undergraduate
or
masters
with
what
particular
type
types
of
techniques
and
obviously
it
identifies?
K
B
K
We
have
not
conducted
it
on
any
other
campus,
but
are
interested
in
doing
so,
and
we
can
certainly
demonstrate
it
in
this
form
or
if
anyone
on
the
call
would
like
to
see
it
and
think
about
it.
For
their
university
we'd
be
happy
to
do
a
demo
and
show
you
the
output,
it's
incredibly
powerful
and
fascinating,
because
it
basically
unearths
a
whole
hidden
knowledge
base
of
what's
going
on
actually
at
the
university.
B
G
B
Yeah,
that's
great
fascinating
work
and
the
workforce
component.
You
know
this.
This
group
is
education
and
workforce
and
we
focus
primarily
on
education
this
year,
but
you
know
thinking
about
you
know
how
that
connects
to
workforce
and
looking
at
at
some
of
that,
how
you're
connecting
for
even
your
capstones
with
companies,
your
local
companies
and
others.
A
G
My
name
is
right
there,
my
screen,
I
guess
my
my
first
name
happened
to
be
the
first
word
of
hong
kong.
My
last
name
qrn
pronounced
like
a
qing,
so
I
tell
my
student.
I
have
a
very
big
change,
so
that's
how
I
tell
my
students-
remember
my
name
so
I
I
was
trained
as
a
biophysicist
by
I
do
a
lot
of
computational
biology,
work
and
now
working
on
computer
science
teach
mostly
data
science.
G
Basically,
that's
my
background.
What
I'm
doing
right
now,
so
I
do
a
lot
of
so
so
I
my
I
I
currently.
Finally,
the
project
one
of
them
is
trying
to
develop
a
deep
learning
method
to
identify
the
species
based
on
the
herbarium
images.
That's
one
project,
another
project,
I'm
trying
to
develop
a
new
method
to
predict
the
lifespan.
G
A
G
So
I
run
a
computational
biology,
reu
program
and
each
year
we
host
a
10
students
because
of
the
pandemic
we're
doing
the
online.
So
the
price
the
cost
is
much
lower.
So
we
host
15
students
this
year
and
mainly
students
are
using
a
various
computational
projects
and
well
majority
of
them
now
use
the
machine
learning
deep
learning
to
do
public
health
related
projects-
and
it's
actually
quite
have
been
quite
a
learning
experience
for
the
faculty
ourselves,
because
the
pandemic
is
ongoing.
G
B
Yeah,
that's
good
and
also
that's
another,
your
question
of
how
to
work
with
like
real-time
data,
so
we'll
do
quickly
for
hana
and
karen
and
then
we'll
come
back
to.
I
think
a
lot
of
these
are
great
topics
you
know
to
explore
going
forward.
Thinking
about
who
to
nominate
for
speakers
as
well,
so
we'll
put
that
back
in
the
for
your
tongue
as
well.
It
couldn't
be
a
speaker.
Nomination,
hey,
go
ahead,
farana
and
introduce.
L
You
hi,
thank
you
renata!
Thank
you,
everyone.
It
was
a
great
presentation,
dr
rumah,
and
it's
a
privilege
to
be
here
it's
my
first
time
so
it
looks
like
I
am
on
the
workforce
side
of
the
group,
and
so
what
we
I
am,
I'm
the
founder
and
ceo
of
a
non-profit
organization,
a
social
enterprise
called
her
will
women
inspired
in
life
and
leadership.
So
what
we
have
done
is
built
a
digital
platform
where
we
highlight
women's
successes
all
over
the
world
and
also
another
on
our.
L
On
the
other
side,
we
also
created
a
hub
of
resources
for
women
to
be
economically
empowered,
so
one
of
our
pillars,
the
biggest
pillars
we're
working
on
right
now,
is
conducting
worldwide
data
funds
and
it's.
It
was
very
interesting
to
see
dr
uma's
program
on
social
justice
because
that's
pretty
much
an
identical
format,
we're
building
to
solve
u.n
sustainable
development
goals
to
contribute.
L
And
then
we
get
the
select
these
girls
from
underrepresented
schools
and
partnering,
with
universities
and
moocs
to
help
us
curate
programs
where
we
actually
filter
out
the
best
of
the
best
and
then
put
them
in
in
a
work
study
program
and
then
that
we
have
built
with
with
with
help
of
an
employer
consortium
that
we're
slowly
building
and
they
have
committed
to
hiring
these
women
and
giving
them
the
business
domain.
Knowledge
that
differentiates
a
data
analyst
from
a
data
scientist.
L
So
so,
by
doing
all
of
this,
we're
bringing
balance
in
the
high
very
high
demand
demand
of
data
science
jobs
there
currently
there's
over
100
122
000
job
openings
around
the
world.
At
this
moment
and
an
average
job
on
the
the
any
job
site
is
45
days
long
so
trying
to
fill
that
gap
between
the
demand,
the
surplus
and
shortage
and
surplus
of
jobs
and
women.
So
we
are
doing
it
with
curating,
highly
competent,
brilliant
women
around
the
world,
and
it's
working
out
very
well,
I'm
very
excited
about
it.
L
B
Yeah
so,
and
we
may
be
partnering,
so
you
know
with
the
south
hub
we're
coming
to
help.
You
know
so,
if
you're
interested
in
that
next
year,
us
when
they
have
it
in
the
u.s
doing
work,
we
can
bring
you
back.
Hopefully,
you'll
come
back
and
come
back
to
the
group
as
well.
So
karen
you
want
to
close
us
out
hi.
M
Baby
grape,
I'm
the
associate
director
for
the
center
of
bioinformatics
and
computational
biology
at
ug
university
of
delaware,
and
I
also
am
the
director
for
workforce
development
for
the
data
science
institute
institute,
and
I
think
I
had
the
pleasure
of
attending
the
meeting.
I'm
trying
to
remember
the
summer
meeting
in
atlanta
was
that
two
years
ago
now,
yeah
yeah
you
and
I
attended
the
meeting
and
yeah.
M
We
have
a
graduate
level
program,
so
I
hope
to
administer
the
academic
program
for
the
masters
phds,
professional
science
masters
and
certificates,
one
of
the
things
that
we
continue
to
develop
as
a
certificate
program
for
the
workforce,
so
that
you
know
those
that
are
those
that
are
working.
Professionals
in
their
career
can
take
some
classes
to
gain
more
knowledge
in
data
science
and
computational
biology.
M
So
that's
an
initiative
that
we
continue
to
develop
and
expand,
and
now
we
are
moving
more
and
not
just
into
bioinformatics
but
into
data
science
with
with
the
courses
and
what
we're
doing
and
in
particular
our
latest
broadening
of
our
program
is
through
biomedical
informatics,
and
so
we
are
also
reapplying
for
a
t32
training
grant.
That
is
in
particular
to
an
initiative
that
is
across
campus
for
many
different
departments
incorporating
data
science
in,
and
one
of
the
initiatives
in
particular
includes
reaching
out
to
underrepresented
minorities.
M
We
are
continuing
to
try
to
develop
and
expand
our
platform
so
that
all
students,
regardless
of
their
background,
can
find
success
in
entering
into
a
graduate
level
course,
and
that
in
particular,
is
a
push
that
we're
doing
with
with
trying
to
offer
academic
courses
that
for
our
underrepresented
minorities,
so
that
we're
bringing
them
to
the
level
to
find
success
in
our
class
classes.
So
thank
you
for.
B
M
B
Really,
and
so
it
sounds
like
we
might
need
to
add
some
categories
to
the
speaker,
nomination
with
workforce
and
also
graduate
education.
So
yeah,
we,
you
know
if
you
refresh
those
things,
might
be
options
after
today
as
well,
because
it
you
know
to
bridge
out
and
just
listening
to
what
everyone's
saying.
B
So
it's
great
we're
we're
a
little
after
time,
but
it's
always
wonderful
to
hear
what
people
are
doing.
I
think
it's
very
valuable
just
to
get
a
sense
of
what
is
going
on
at
other
institutions
and
and
and
other
types
of
agencies
too,
so
we'll
talk
next
month
back
at
the
same
time
and
again,
I
think
we
are
going
to
update
the
speaker
nominations
with
some
of
these
topics
that
are
really
relevant
great
to
see.
Everybody
have
a
good
friday
good
weekend,
end
of.