►
Description
Get Involved!
This webinar will highlight report outcomes from the Keeping Data Science Broad Series as well as next steps, open projects, programs, and opportunities for you to get involved in the Data Science Education Network today. Some deadlines close soon so come be a part of the discussion.
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.
http://bit.ly/KeepingDataScienceBroad_Report
http://bit.ly/KeepingDataScienceBroad_Workshop
http://bit.ly/KeepingDataScienceBroad_Webinars
To Apply to the DataUp Program Please Visit: www.southbigdatahub.org
For more information on the South Hub Data Science Education and Workforce Working Group visit: http://bit.ly/SouthHub_EducationWG
B
All
right
so
hello
and
welcome
to
the
keeping
data
science
rod,
webinar,
3,
I'm,
Renata,
Rawlings
gas,
the
co-executive
director
for
the
south,
big
data
innovation
hub,
as
well
as
the
chair
for
data
science,
education
and
workforce
working
group
and
the
pH
of
this
series.
In
a
science
Rob.
Today
we
will
be
discussing
a
series
of
a
whole
which
has
consisted
of
six
months
of
community
input,
three
webinars,
as
well
as
a
in-person
workshop
and
a
report
which
will
be
released
today
immediately
following
the
webinars,
so
the
links
will
be
in
the
video
notes.
B
In
addition,
there's
been
so
much
enthusiasm
and
support
for
the
series
that
we
have
a
fantastic
lineup
of
panelists
today
to
talk
about
next
steps,
as
well
as
potential
projects
that
people
can
get
involved
in
nationally,
as
well
as
internationally
directly
in
to
continue
this
effort.
So
we've
started.
We
will
also
start
a
collaborative
document
to
note
these
projects
and
others
that
may
come
in,
and
people's
interest
in
participating
also
we'll
be
at
the
South.
B
B
All
right
so
keeping
data
science
fraud
also
has
included
negotiating
the
digital,
and
they
will
divide
a
little
about
the
South
data
hub.
There
are
four
hubs
for
the
nation
that
are
dedicated
mission
of
advancing
projects
that
include
industry,
academia
and
government,
as
well
as
my
profit
partners
around
data
science
and
big
data.
The
south
hub
is
sixteen
feet
from
Delaware
to
Texas
and
we
have
eight
different
beam
areas
as
well
as
working
groups,
so
this
was
under
our
education
and
work
force.
B
The
second
webinar
in
this
series
was
alternative
avenues
for
development
of
data
science,
education,
which
included
liam's,
as
well
as
programs
and
boot
camps,
data
science
for
social
good,
also
incorporating
data
science
into
non-stem
programs
such
as
arts
and
humanities
classes
and
mentorship
programs.
Both
of
these
are
available
on
our
youtube
channel
as
well,
and
will
be
linked
to
this
one
in
a
playlist.
B
So
we
spent
time
at
the
workshop
clustering.
These
challenges
and
visions
into
the
areas
we
see
below
and
finding
common
areas.
So
then,
there
was
a
core
group
that
went
back
and
created
a
report
where
we
looked
at
all
29
areas
and
came
up
with
these
eight
that
really
represented
all
of
them
and
put
them
into
the
report
which
will
be
coming
at
the
end
of
today.
B
B
So
for
the
South
hub.
The
next
step
in
this
series-
this
is
the
last
webinar,
but
we're
launching
a
program
today,
which
is
called
data
up
where
you
can
apply
to
burn
data
files
to
your
campus.
This
is
a
hands-on
training
or
instructor
team
at
minority-serving
institutions,
community
college
or
four-year
liberal
arts
colleges.
B
The
idea
that
she
would
be
able
to
have
a
training
and
a
data
science
workshop
hosted
on
your
campus
and
the
application
includes
all
three
steps:
all
together
and
accepted
teams
that
be
teams
of
staff
or
faculty
or
research
scientific
research
sector
that
would
receive
that
would
be
hosting
this
workshop.
It
would
be
funded
through
the
South
hub,
in
conjunction
with
software
and
data
carpentry,
on
fundamental
data
science
skills
for
either
faculty
or
students.
B
So
today,
this
webinar
is
the
big
picture
or
big
data
science,
education,
Network,
and
we
have
fantastic
speakers.
As
I
said,
as
I
had
Nick
Nicholas
Horton
Amherst
College
Lillian
Cassell
from
Villanova
University
yuri-chan
go
from
the
University
of
Amsterdam
and
Melissa
Craigan
was
the
executive
director
for
the
Midwest
big
data
hub
and
Kyle
Schmidt
was
at
Valparaiso
University,
so
we're
going
to
start
off
with
Rick
and
welcome
great.
C
Renata
well,
thank
you
very
much
and
thanks
for
your
leadership,
vision
and
energy
and
really
making
this
report
and
bringing
this
community
together,
it's
been
really
exciting
to
be
part
of
it
and
I'm
really
excited
to
hear
about
the
data
up
and
they
should
have
been
other
things.
But
let
me
jump
in
or
right
now
and
share
my
screen,
then
I'm
going
to
go
ahead
and
do
that
for
the
Google
hangout,
no
I
don't
want
to
do
I
want
to
do
it.
C
D
C
So
the
the
big
picture
today-
hey
Education,
Network
and
I,
was
asked
to
talk
a
little
bit
about
some
related
data
science
initiatives
that
I
think
are
relevant
for
the
community.
These
slides
will
be
posted
with
the
links
and
I
really
want
to
kind
of
go
through
a
number
of
these,
because
they're
they're
mentioned
in
the
report,
but
I
think
give
us
a
good
foundation
moving
forward.
C
The
first
one
are
the
American
Statistical
Association
guidelines
for
statistics,
major
and
minor
programs,
which
really
enunciated
back
in
2014,
the
need
for
an
integration
of
computational
skills,
statistical
theory,
practice
and
communication,
and
with
that
with
that,
the
community
in
statistics
and
other
related
disciplines
needed
to
restructure
the
curriculum
to
help
students
really
think
with
data,
and
this
report
really
stresses
the
key
role
of
data
science.
More
recently
in
the
Park
City
group
of
put
together
guidelines
for
data
science
programs,
and
they
really
call
for
do
now.
C
Weekly
introductory
computer
science
they've
described
the
learning
outcomes
for
a
series
of
data
science,
1
&
2
courses
that
are
different
from
the
learning
outcomes
for
a
CS,
1,
2
or
3,
but
really
bring
in
the
parts
that
are
important
for
people
to
be
having
as
a
foundation.
Similarly,
we
can't
afford
to
have
a
long
list.
B
C
D
D
The
workshop
that
we
had
in
October
identified
a
number
of
areas
as
being
critical
to
anything
related
to
data
science
and
included
machine
learning,
not
surprisingly,
statistics,
knowledge
of
algorithms
programming
software
engineering,
it
emphasized
a
lot
communication
with
very
diverse
audience.
The
fact
that
oral
and
written
skills
are
essential.
They
ability
to
work
in
team,
evidence-based
decision-making.
D
This
is
decision,
support,
pattern,
recognition,
data
shaping
visualization,
privacy,
security
and
integrity.
So
that's
an
important
component
that
comes
up
a
lot
and
I
think
it's
really
critical
that
we
make
sure
that
this
never
gets
lost,
that
anytime.
We're
talking
about
data
that
we're
making
people
more
able
to
get
get
hold
of
and
work
with
large
quantities
of
data
that
understanding
the
responsibility
that
goes
with.
That
is
really
critical.
Also
so
I
guess
that's
the
the
most
important
parts
of
that.
The
things
that
this
group
decided.
D
You
need
to
be
included,
but
one
aspect:
it's
a
little
unnerving
because
I
keep
seeing
other
people
on
the
screen.
So
I
don't
know
if
anybody's
seeing
me
or
not,
but
this
group
was
asked
one
important
question
at
the
end
of
the
workshop,
and
that
was:
is
it
necessary?
Is
it
at
this
point
important
to
have
a
curriculum
recommendation?
D
Science
and
the
anonymous
vote
was
yes,
but
it
was
a
guess
but
and
the,
but
was
it
yes,
but
it
can't
be
too
rigid.
It
can't
be
too
specific
and
that
there
had
to
be
a
lot
of
flexibility,
because,
given
the
nature
of
the
many
kinds
of
data
that
would
work
with
of
many
kinds
of
groups
that
will
be
doing
data
science
related
activities
that
it's
it's
going
to
be
important
to
have
flexibility
so
there.
D
Yes,
there
are
core
things
that
have
to
be
there
for
everybody,
but
the
way
they're
presented
the
way,
they're
integrated
the
way
they're
combined
will
vary
depending
on
the
group.
There
is
also
a
there
is
a
website
that
has
a
copy
of
the
report
from
that
group,
but
I
just
put
it
in
the
chat
box
they
so
that
report
is
available,
and
it
would
be
interesting
to
combine
that
with
the
report.
D
That's
coming
out
of
this
group,
I
think
you'll
see
a
lot
of
overlap
and
some
some
differences
there's
also
coming
up
in
Baltimore
in
February
the
ACM
special
interest
group
with
computer
science
education.
There
will
be
a
birds
of
a
feather
session
on
data
science,
education
on
in
the
Baltimore
Convention
Center
Thursday,
the
end
of
February,
so
I
guess.
That's
all
I
really
wanted
to
say
that
there
are
some
echo
at
the
fact
that,
in
response
to
the
workshop
and
the
workshop
recommendations,
ACM
has
formed
a
committee
to
begin
the
process
of
a
curriculum
recommendation.
D
Yes,
we
know
there
are
lots
of
others
of
them
out
there,
and
one
of
the
interesting
things
going
forward
will
be
looking
at
how
the
information
from
various
groups
compares
and
how
we
we
bring
something
out
that
that
is
useful
across
the
board.
So
I
don't
want
to
take
up
any
more
time,
but
I'd
be
happy
to
address
questions,
and
you
know
contact
me
for
further
information
on
these
things.
Thank.
B
C
Sorry
for
the
complications-
and
let
me
kind
of
jump
back
in
to
what
I
now
think
is
a
full-screen
mode.
So
let
me
know
if
that's
not
the
case.
I
was
great
your
Boots's
comments
and
it
really
dovetails
and
what
some
of
the
other
things
out
there.
The
statistics,
major
and
minor
programs
really
this
importance
of.
We
need
to
be
descriptive,
but
not
prescriptive,
and
that
was
really
a
big
part
of
those
guidelines.
C
The
part
Citigroup
kind
of
tipped
that
to
the
next
level,
though
I,
do
think
we
need
to
continue
to
be
refining
this
and
to
see
how
these
approaches
and
guidelines
from
Park
City
can
be
brought
forward
and
then
augmented
as
we
proceed.
This
really
has
a
multi-year
sequence
to
it
and
I
think
the
main
implication
for
a
lot
of
this
was
we
needed
new
approaches
and
new
structures
curricularly
to
be
effective,
to
really
be
doing
data
science
and
so
I'm
glad
to
hear
about
the
ACM
efforts
in
those
areas.
C
I'm
part
of
two
initiatives
through
the
National
Academies
one
is
the
consensus
study,
the
envisioning
data
science
discipline,
the
undergraduate
perspective,
which
is
really
seeking
to
identify
what
some
of
the
core
underlying
principles,
intellectual
content
and
pedagogical
issues
that
really
discipline
data
science
from
neighboring
disciplines.
The
interim
report
is
available.
It's
actually
cited
by
Renata's
report
that
should
be
unveiling
later
today,
but
there's
also
going
to
be
a
full
report
at
a
final
court
coming
out
in
the
spring
in
the
summer.
C
What's
nice
about?
That
is
that
the
meetings
are
advertised,
live,
streamed,
archived
and
summarized.
You
can
actually
see
the
details.
We've
had
a
fairly
large
number
of
viewers
for
those
past
meetings
of
the
foundations
of
data
science,
the
main
expertise
and
data
science
and
how
those
intersect
thinking
about
data
science
in
the
workplace,
kind
of
thinking,
continuing
education,
alternative
mechanisms
to
deliver
data,
science,
education
and
our
most
recent
bit
in
last
month
on
integrating
ethics
and
privacy
really
important
issues.
C
Future
meetings
are
going
to
be
talking
about
teaching
data
science
as
a
scientific
process
and
I
think
this
is
an
area
where
it's
different
from
the
ways.
Additionally,
the
statistics
or
the
computational
sciences
have
approached
they're
at
they're
teaching,
there's
a
follow-up
meeting
on
your
colleges
in
June,
looking
at
representation
and
diversity,
issues
in
September
and
then
balancing
general
education
and
in
Washington
DC
at
the
end
of
2018.
C
Another
thing
that's
I
think
relevant.
That
will
be
interesting.
Looking
over
the
coming
months
is
an
initiative.
That's
been
funded
by
the
NSF
organized
by
the
American
Statistical
Association,
to
bring
together
a
two-year
the
science
summit-
and
this
is
this
workshop-
will
really
think
about
what
our
model
programs
and
how
can
we
really
be
defining
those
in
a
flexible
manner
that
can
really
help
train
people
at
the
associates
level.
C
Plated
projects
I
just
wanted
to
mention
was
the
collection
on
Pier
J
of
some
practical
data
science,
which
much
of
which
is
really
thought
of
it
at
a
level
that
could
be
accessible,
the
undergraduate
level
and
finally,
David
Donoho's
excellent
and
insightful
paper
about
50
years
of
data
science,
with
an
accompanying
set
of
discussions
and
focus
on
have
educational
implications
and
all
that
those
papers
and
discussion
are
available
freely
at
this
website.
So
thank
you,
I'll.
B
Thank
You
Nick
thank
you
know
so,
there's
a
lot
of
good
connections
there,
so
people
can
be
able
to
plug
into
different
efforts
in
this
area.
I'd
like
to
introduce
our
next
speaker,
which
is
Yuri
Timoshenko
from
the
universitat
in
Amsterdam,
and
he
has
been
working
with
the
European
big
data
value
association,
which
is
similar
but
very
different
to
the
big
data
hubs
here
in
the
US
and
talking
about
their
programs
and
projects.
So
Yuri.
E
E
We
run
a
project
called
Edison
funded
by
European
Commission,
and
this
was
for
two
years.
It
finished
in
September,
and
actually
we
knew
that
product
will
not
continue
if
he
found
them
develop
a
couple
of
generation
tactics
how
to
continue
our
activity,
mostly
moving
to
community
support
and
community
engagement.
But
in
this
presentation,
I
want
to
show
you
what
we
actually
achieved
to
the
project.
So
I
was
fine
quite
funny
to
see
the
50
years
of
data
science.
E
E
Sexual
is
saying
that
pure
data
scientists
are
only
some
part,
part
of
the
market
requirement
and
majority
of
other
demand
for
data
science.
Skills
is
from
the
so
called
data
science
enabled
data
science
and
analytics
enabled
jobs.
So
we
actually
put
all
this
reporting
there
as
a
basis
for
building
their
final.
Our
products
in
LA
in
the
second
year
of
the
project
and
we
created
a
number
of
I,
would
say
products
or
definitions.
E
This
also
slide
just
to
show
that
extremely
important
part
of
requirement
to
new
specialists
called
data
science,
scientists,
wisdom
and
whatever
profession
is
a
so-called
professional
skills
is
not
directly
related
to
the
technical
skills
or
say
analytics
or
computer
skills.
So
this
also
was
a
something
that
we
revisited
in
the
project
and
this
slide
shows
actually
what
we
created
in
a
project
first
of
all
is
the
edges
of
data
science.
E
We
also
identified
that
companies,
then
they
built
their
data.
Science
teams
and
data
science,
I
would
say
a
excuse.
They
also
require
a
data
management
data
governance
as
one
of
their
main,
but
an
important
part
of
the
data
science.
A
common
set
of
skills
and
also
research
methods
makes
the
data
science
different
from
data
analytics
statistics
and
so
on
and
say.
If
you
look
at
different
standards,
the
Dharma
data
management
association
that
produced
it,
the
data
management
body
of
knowledge
is
a
600-page.
E
They
include
a
chapter
about
data
science
that
specifically
mentioned
that
data
science
data
scientists
need
to
is
focused
on
discovering
some
new
relations
new,
not
known
before
the
starting
of
analysis
of
vast
amount
of
data.
This,
how
it
looks
like
this
more
in
bigger
format,
the
data
science,
a
competence,
free
block,
visualization,
you
see
three
types
of
skills,
plus
data
management
and
research,
method,
training
or
research
methods
or
business
method
for
the
general
background
and
I
would
say,
professional
orientation
of
the
data
scientists.
E
E
We
defined
that
a
data
scientists
need
to
be
specifically
education
for
data
scientists,
specifically
targeted
for
developing
specific
skills,
academics,
professional
skills
or
works
workplace
skills,
and
what's
interesting
that
universities
lost
and
up
stood
on
even
started
yeah,
they
included
this
kind
of
skills
or
training
for
students
started
from
the
first
year
and
data
science.
Computers
framework
is
a
document.
Okay,
all
data
science
blog
is
a
set
of
four
documents,
each
of
them
from
40
to
70
pages.
E
It
defines
many
many
things
that
partly
formalized
and
can
be
used
directly
into
design
of
definition
of
professional
profiles,
defining
job
job
profile.
Also
created
tests
and
questions,
and
so
on
so
this
what
has
a
quite
practical
implementation-
and
he
already
has
helps-
had
some
implementation
dude
in
the
project.
I'm
important
part
of
this
data
science
provoke.
You
said
we
defined
the
data
science
profession
of
professions,
family,
and
this
include
managers,
professional
professional
database
data,
handing
and
so
on.
E
E
E
Individual
or
tailored
curriculum,
so
if
you
start
from
the
professional
profiles
that
we
need
to
educate
and
OS
from
competences
that
we
need
to
actually
reach,
as
they
define
the
learning
outcome
and
based
on
this,
they
define
learning
units
had
knowledge
units
from
body
of
knowledge,
and
this
will
create
a
number
of
specific
topics.
Educational
topics
that
we
need
to
put
in
this
program
and
based
on
this,
the
choice
to
create
the
appropriate
program,
curriculum.
E
Based
on
available
a
stuff
available
competences
and
also
lab
a
lab
facilities
because
for
specific
skills
and
training,
the
heaven
facility
for
hands-on
experience
is
essential,
and
we,
if
we
next
we'll,
define
the
how
to
use
this
framework.
So
we
also
plan
to
make
a
recommendation
for
which
kind
of
software,
which
kind
of
hardware
should
be
included
into
the
educational
facility
post-hoc
its
competence
rule
can
be
used
for
a
session
there.
A
this
is
a
red.
Is
the
the
position
or
required
profile
of
data.
E
Scientist
and
green
is
a
candidate,
so
company
can
assess
this,
see
the
difference
and
after
that
decide
either
a
decline.
This
candidate
or
give
them
opportunity
to
say
skills
and
competences
and
for
candidates
is
a
very
good
act
wise
what
they
need
to
learn
to
become
a
lesser
say:
ideal
data
scientist
this
example
house
can
be
created
a
team,
and
this
is
what
we're
currently
working
on.
E
Defining
the
fully
a
formalized
body
of
knowledge.
Competence
will
rock
and
make
them
in
a
four
of
API
still
in
the
progress,
but
the
next
step
will
be
creating
github
project
and
starting
developed
in
API,
and
this
is
example
how
we
can
use
this
tool
still
not
in
the
fully
automatic
way.
So
we
can
run
all
this
set
of
scripts
and
define
the
curriculum
content
for
the
data
scientist
you
see.
E
Beside
this.
They
also
need
to
be
a
kind
of
replica,
has
a
features
of
so-called
21st
century
skills
with
white,
also
different
and
wider
recognized
recognizing
the
last
recent
time,
our
next
step
step.
So
we
are
planning
to
make
release
of
the
data
science
wave
walk
number
three,
you
see
what
will
be
quality
and
also
have
some
simple
tools
for
self
assessment
and
curriculum
design
and.
E
This
certification,
Authority
will
be
successful.
We
will
develop
a
certification,
free
book
and
we'll
start
running
certification,
training
and
courses,
and
one
is
items,
but
we
have
very
much
expect
cooperation
this
a
internationally
and
specifically
the
United
States
on
defining.
There
are
the
components
to
make
their
data
science
profession
defined
more
formally,
widely
recognized
and
using
what
was
done
in
a
former
time
by
I,
Triple,
E
and
ACM
in
particularly
what
was
put
we're
very
active
and
actually
key
person,
and
we
will
run
a
first
champion
conference
in
ND
born
in
May.
E
So
we
will
start
posting
very
soon.
They're
called
four
PI
percent
would
like
to
invite
people
to
come.
Unfortunately,
we
cannot
support
travel
or
conference
first,
three
conferences
which
were
supported
by
project
now.
If
we
don't
have
this
money,
the
most
audience
will
be
present
and
my
presentation
will
contain
contains
a
number
of
links
that
you
can
use
to
learn.
E
Number
of
example,
of
already
teaching
implementing
this
I'm
personally
developing
a
couple
of
courses
already
doing
and
University
of
Saddam
live,
has
a
five
data
science
programs,
but
they
actually
adopt
him.
A
couple
of
common
components
like
data
science,
management,
professional
profile,
professional
skills,
training
and
also
they're,
very,
very
high
demand
for
data
science.
E
Engineering
in
particularly
data
sign,
big
data
infrastructure,
because
then
even
business
people
they
become
familiar.
This
data
science
methods
and
tools.
They
are
started
asking
how
to
do
this
practically
have
deployment
in
our
organizations
and
major
approaches.
Actually
they
use
in
cloud
and
if
ever
demand
from
all
programs,
five
programs
for
programs,
because
one
of
the
specifically
data
science
transaction
demand
to
add
courses
on
big
data,
a
data
science
strategy
to
sent
and
solutions.
Okay.
So
this
is
what
I
wanted
to
say:
doughnuts,
but.
B
Siri,
yes
I,
so
thank
you
for
all
of
your
work.
I
definitely
wanted
to
connect
it
with
this
group
and
on
the
webinar
for
the
for
the
US
audience
to
get
connected
with
specifically
I
know.
We
talked
about
the
API
effort,
if
that's
something
that
people
can
be
connected
with
as
well,
we'll
get
that
up
on
the
site
and
also
looking
at
how
those
frameworks
could
be
used
in
the
US
as
well
as
incorporated
into
some
of
the
things
we've
talked
about
throughout
this
series.
F
Ever
lotta,
thank
you,
hi
everyone.
It's
a
real
pleasure
to
be
here
today,
I'm
going
to
just
turn
on
my
screen
sharing
here
and
my
presentation
so
bear
with
me
we'll
get
that
loaded
up
so
I
want
to
echo
some
thoughts
as
I
get
started
here.
So
this
is.
This
has
been
a
great,
a
great
webinar
series
and
just
a
great
initiative.
I
think
highly
impactful.
F
Everyone
that's
been
participating
so
far,
and
I
am
hopeful
and
and
I'm
pretty
sure
that
the
audience
over
the
over
the
course
of
the
webinars
is
getting
a
lot
out
of
this
as
well.
So
let
me
I
won't
spend
a
lot
of
time
on
the
first
couple.
Slides,
but
I
I
wanted
to
just
show
the
states
where
the
Midwest
hub
is
here
in
green,
so
we're
12
states,
Upper
Midwest.
F
Of
course
we
have
collaborators
and
partners
from
other
states
in
the
country,
and
we
we
embrace
that
and
welcome
that,
and
when
we
talk
about
a
couple
of
our
initiatives,
you'll
see
how
we're
how
people
are
participating
in
those
from
around
the
country
as
well.
So,
very
briefly,
just
for
people
who
might
not
have
been
to
the
other
webinars
and
maybe
potentially
are
new
to
the
hubs,
all
four
of
the
hubs.
F
While
we
articulate
our
mission
just
a
little
bit
differently,
we
actually
have
a
common
mission
and
really
it's
about
building
partnerships
and
collaborations,
but
but
to
be
able
to
build
capacity
and
data
science
and
big
data
across
our
regions
and
across
the
nation
to
address
not
only
national
but
regional
issues
as
well,
scientific
and
societal,
and
and
help
doubt
that
the
National
Big
Data
ecosystem,
which
is
what
this
initiative
you
know
addresses
full-on
in
the
Midwest.
We
also
have
priority
areas
and
we
have
some
underlying
ones.
F
Besides
these
primary
areas
or
sectors
that
were
engaged
in
here
and
the
reason
I
show
these
is.
It
has
significance
for
the
kinds
of
training
programs
we
have
across
small
institutions
in
the
u.s.,
both
two
and
four-year
schools,
as
well
as
as
well
as
minority
serving
institutions
that
you
know
the
idea
or
the
opportunity
to
teach
data
science
across
the
curriculum
is
very
high.
We
have
opportunities
there.
These
smaller
institutions
are
really
providing
a
large
proportion
of
the
u.s.
F
So
what
are
we
doing
in
the
Midwest
just
a
little
bit
of
background
here
and
then
I'll
talk
about
the
current
larger
initiative
and
where
we're
headed
for
that.
So
we've
been
we've
been
supporting
direct
activities
to
engage
data
science
and
build
capacity
both
for
training
and
development
for
early
career
researchers,
as
well
as
for
graduate
students.
F
So
we
provided
some
direct
dollars
for
people
to
go
out
and
have
direct
engagement
with
industry,
around
data
sharing
to
go
to
research
methods,
training
that
involve
data
science
and
areas
that
that
a
graduate
student
might
not
otherwise
train
in
and
also
to
provide
professional
engagement.
So
one
of
the
things
that
we've
done
is
help
support
people's
travel
to
go
and
interact
with
new
kinds
of
communities
to
help
build
capacity,
whether
it's
across
cross-domain
capacity
or
moving
into
more
technical
areas
and
help
support
interactions
of
faculty
with
data
science
kinds
of
activities
in
their
class.
F
We
also
provide
support,
sometimes
direct
direct
training,
but
also
support
for
group
training.
So
we
send
entire
groups
to
software
carpentry,
for
example,
but
we
now
also
have
an
initiative
similar,
but
not
not
exactly
like
the
data
up
program
being
running
South
weave,
we
are
partnering
with
data
carpentry
to
provide
a
train-the-trainer
series
of
trainings
across
the
Midwest.
F
Won't
I,
won't
spend
any
time
here,
but,
of
course,
we're
engaged
in
a
number
of
other
areas,
and
sometimes
it's
me.
The
reason
I
want
to
bring
this
up
is.
We
also
provide
sometimes
support
for
other
people
to
go
and
engage
in
these
other
kinds
of
regional
and
national
meetings
around
data
science.
So
again
it's
about
this
is
about
supporting
professional
engagement
to
help
build
capacity.
F
So
what
we,
what
we
started
to
think
about
and
learn,
and
I
and
I,
have
to
call
out
Lee
or
shmear
here
who
is
one
with
one
on
one
of
the
earlier
web?
Webinars
Lior
is
a
faculty
member
at
Lawrence,
Technical,
University
and
I
believe
he's
also
associate
or
acting
research
administration
at
ltu
and
Lior
and
I
started
talking
about
the
fact
that
large,
as
I
mentioned
a
large
number
of
our
workforce,
large
percentage
of
the
workforce
across
the
u.s.
F
So
we
in
in
the
context
of
thinking
about
how
to
do
this
in
the
Midwest,
but
then
also
think
about
how
do
we
scale
it
up
across
and
provide
access
to
these
resources
across
the
u.s.
we've
put
together?
A
sort
of
planning
grant
activity
we've
invested
about
eighty
seven
thousand
dollars
into
this
activity
over
the
course
of
the
year,
which
will
lead
to
a
larger
proposal,
potentially
a
spoke
proposal
and
and
related
proposals.
Out
of
this
Lior
is
leading
that
effort.
F
But
we've
also
done
some
of
our
own
background
work
here
with
some
institutions
in
the
Midwest
looking
at
existing
resources
and
opportunities,
and
so
we
got
to
what
do
we?
What
do
we
do
next
and-
and
so
as
part
of
that,
we
launched
this
effort.
So
the
first
meeting
the
kickoff
meeting
was
October
3rd
as
part
of
the
Midwest
big
data
hub
All
Hands
meeting,
and
there
was
a
set
of
objectives
so
really
figuring
out.
What
do
we
do
and
I'm
sorry
that
this
photo
is
a
little
bit
blurry?
F
F
I've
given
a
very
truncated,
a
set
of
summary
points
here,
but
but
really
one
of
the
underlying
themes
is
that,
in
addition
to
thinking
about
data
science
across
the
curriculum
that,
in
fact
the
cross-disciplinary
nature
data
science
makes
this
unique
and
potentially
is
a
barrier
for
smaller
institutions.
Where
often
there
are
very
small
numbers
of
people
around
a
particular
domain,
and
maybe
maybe
someone
or
no
one
around
a
particular
topic
area.
F
Those
sorts
of
opportunities
to
show
competency
and
self-efficacy
really
encouraged.
People
to
then
be
are
creative
and
interested
in
in
going
after
new
opportunities.
So
we,
you
know
we
spent
some
time
thinking
about
that
and
that
that's
part
of
the
conversation
that's
ongoing
in
terms
of
how
to
approach
some
of
data
science
across
the
curriculum
and
finally,
just
a
point
also
just
in
terms
of
technology.
F
So
what's
next
for
this?
Well,
there's
a
public
website
up
now
the
the
URL
is
here
and,
of
course,
we
will
also
post
these
slides.
We're
really
happy
that
Franklin
Marshall
University
is
also
from
Pennsylvania
is
also
participating,
as
are
some
schools
from
other
other
states
outside
the
region,
but
they've
also
provided
us
with
supporting
some
of
the
communication
and
ongoing
planning
for
the
for
the
team
and
we're
grateful
for
that.
F
There
is
because
it
would
afford
the
faculty
and
administrators
at
the
small
schools
opportunity
to
start
to
interact
and
potentially
build
collaborations
around
getting
access
to
resources
and
services.
They
don't
have
access
to
on
their
local
campuses
and
then
we'll
move
it
to
putting
together
a
full
proposal
for
the
virtual
resource
center,
which
will
focus
on
virtual
organization,
distributed
Center
resources
and
those
resources
will
include
best
practices,
curriculum,
potentially
teaching
modules
and
other
things,
and
then
really
thinking
about
sustainability
and
scale.
F
B
You
Melissa
and
for
all
the
things
that
were
doing
in
the
Midwest
I
think
that
we're
gonna
follow
up
and
we're
gonna
have
a
talk
with
all
of
the
hubs
about.
You
know
education
and
workforce
and
an
omission
on
my
part,
which
is
purely
put
it
up
to
my
mind
and
not
my
heart,
because
she's
definitely
a
very
important
part
of
this
particular
effort
and
has
been
here
from
the
beginning.
It's
a
Kathryn,
Cramer,
miss
banking
melissa
is
been
there
from
getting
as
well
and
also
Kathryn.
B
Cramer
who's
gonna
be
next,
our
next
speaker,
and
she
is
the
senior
program
developer
for
the
New
York
Hall
of
Science
Museum,
who
works
at
the
intersection
of
data-driven
science
and
learning,
specifically
as
it
pertains
to
understanding
of
the
complexity
and
the
application
to
data
and
network
sciences,
particularly
with
a
focus
on
underrepresented
communities.
I
mean
in
this
role.
She
has
focused
on
developing
tools
for
teaching
and
learning
of
complex
networks
and
data
science,
and
is
focus
on
data
literacy.
B
G
So
much
for
nada
and
thank
you
for
the
nice
introduction
and
I'm
really
happy
to
be
here
with
everybody
all
of
the
co-presenters
and,
as
others
have
mentioned,
the
workshop
a
few
months
ago
was
really
exemplary
and
really
a
nod
to
u
Renata
for
pulling
that
off
and
pulling
off
the
writing
of
this
great
report.
I
know
I
was
very
happy
to
be
a
part
of
it.
So
I'm
gonna
do
a
sher
here
and
hope
that
you
can
see
my
screen
full
size.
Does
that
look
right,
perfect,
great
terrific?
G
This
isn't
news
to
you
that
we're
in
a
data
revolution
we're
literally
drowning
in
data
we're
gathering
data.
It's
such
a
fantastic
rate.
Science
and
engineering
have
been
transformed
and
we're
facing
a
kind
of
Moore's
law
for
data
driven
science
exponential
growth.
What
Jim
Gray
is
called
the
fourth
paradigm,
and
this
is
an
era
hours
that
we're
living
in
right
now
in
which
computer
users
worldwide
generate
enough
digital
data.
Every
15
minutes
to
fill
the
u.s.
G
The
degree
of
complexity
at
all
scales
and
the
complexity
of
the
problems
that
policy
makers,
the
public
citizens
industry,
are
called
upon
to
address
our
increasing
the
interdisciplinary
and
complex
and
those
routine
manual
and
repetitive
tasks
of
the
past.
Are
there
either
completely
automated
or
in
fact,
no
longer
relevant?
G
So
what
of
Education
approach
is
done
about
this?
Well,
our
education
approaches
have
are
literally.
They
have
not
done
much
the
skills
that
are
needed.
Our
emphasis
is
on
design
construction,
visualization
of
large-scale
data,
the
Federation
of
multivariate
data
streams,
the
exploratory
stochastic
Patterson,
patterning,
seeking
interdisciplinary
approaches,
machine
learning,
semantic
databases
and
ontology
is
more
available
and
interoperative
operable
large
data
structures.
But,
as
I
just
said,
science,
and
in
fact
most
subjects
are
still
taught
in
the
same
way
as
they
were
in
the
1950s.
G
But
so
here
we
are
and
we
have
an
opportunity
and
we
have
an
opportunity
to
close
the
gap,
to
transform
human
knowledge
with
data
science
and
to
bring
the
public
along
with
us
and
which
is
not
only
the
most
ethically
responsible
thing
to
do.
It's
really
the
only
way
that
this
process
can
can
continue
at
such
a
rapid
pace.
We
need
data
literate
public.
G
We
need
data,
literate
policy
and
education
sectors,
and
this
is
the
impetus
that
for
us
to
start
this
process
to
build
capacity
for
data
literacy
that
can
scale
and
to
close
the
gap
between
all
of
us
in
here
with
everybody
out
there.
So
just
a
little
bit
of
background
of
so
what
do
we
mean
by
data
literacy
per
se
and
by
the
way?
G
This
is
one
of
the
things
up
for
much
discussion,
but
although
I
am
as
they
as
Renata
mentioned,
a
member
of
the
Northeast
big
data
hub,
we
have
found
that
the
term
big
data
confuses
people,
so
we've
kind
of
simplified
it
to
data
literacy
as
opposed
to
big
data
literacy.
But
we
use
those
terms.
A
little
interchangeably
and
I
know
there.
Many
who
may
object
to
that.
But,
as
I
said,
that's
open
to
discussion
earlier,
literacy
efforts
could
be
based
on
a
process
we've
used
in
the
past
to
gain
consensus
across
a
science.
G
Discipline
such
as
oceanography
or
a
little
area
of
Lyra
see
concerned
concern.
This
grassroots
process
was
used
to
develop
the
literacy
principles
a
little
over
a
decade
ago.
I
was
involved
in
that
and
quickly
followed
by
other
complex
science
literacies
such
as
climate,
atmospheric
and
earth
science,
literacies
and
then
most
recently,
some
of
us
spearheaded
this
work
on
developing
networked
literacy,
essential
concepts
and
core
ideas
that
was
begun
at
UC
Berkeley
in
June
of
2014,
and
there,
where
you
can
take
a
look
at
that,
and
it
tells
a
little
bit
more
of
the
story.
G
G
That's
focused
on
data
literacy
and
is
now
ongoing,
and
that
brings
us
to
the
report.
The
data
literacy
goals,
as
stated
in
the
report,
a
commonly
understood
definition
of
data
literacy,
a
published
handbook,
inclusion
of
a
data
literacy
component
in
every
introductory
course
ensuring
that
data
literacy
is
pervasive
throughout
the
curriculum
teacher
training
for
using
data
letters,
literacy,
approaches
and
that
all
students,
starting
in
pre-k,
will
receive
data
literacy
through
curriculum
and
using
some
backward
design
from
career
pathways
figuring
out.
G
The
skills
needed
device
defining
the
relationship
that
data
literacy
has
the
domain
science,
as
well
as
a
related
set
of
skills
and
education
and
educational
experiences
in
other
areas,
such
as
business,
intelligence,
machine
learning
and
so
forth,
and,
of
course,
the
hope
that
data
science
will
be
seen
as
an
engaging
creative
and
imperative
body
of
knowledge
and
the
foundation
of
fact-based
critical
thinking.
As
far
as
our
stakeholders.
G
Well,
that
simple
all
citizens,
all
citizens
of
the
21st
century,
should
be
data
literate
by
the
time
they
graduate
from
high
school
and
as
far
as
our
next
steps
well
to
develop.
A
globally
accepted
definition
of
data
literacy
create
a
critical
thinking
course
coupled
data
literacy,
with
a
discussion
of
ethics,
which
so
important
develop
a
portfolio
of
work,
offer
teacher
workshops
at
all
levels,
develop
critical
thinking
and
computational
thinking.
Curricula
strengthen
foundational
math
skills
and
strengths,
an
application
based
problem-solving
skills.
So
I'm
simple
right.
Well
we're
aiming
to
get
there
we're
growing
capacity.
G
As
a
couple
of
people
have
mentioned.
There
are
now
these
cross
hub
collaborations
and
specifically
on
data
literacy.
We're
looking
forward
to
really
ramping
up
that
work
as
well
as
collaborations
with
the
EU,
as
you
heard,
from
URI,
there's
so
much
fantastic
work
going
over
there
with
our
EU
partners
and
we're
really
looking
forward
to
working
with
them.
And
we
invite
everyone
to.
Please
join
us
and
thank
you.
B
Thank
you.
Thank
you
Katherine.
Yes,
so
this
data
literacy
is
something
that
we
definitely
has
a
crossover
with
losing
with
the
EU
as
well
as
what's
happening
in
the
US
and
what
we're
trying
to
incorporate
a
broad
audience.
You
know,
so
that's
the
real
foundation
of
keeping
it
broad
is
bringing
it
to
everyone.
B
H
Thank
you
Renata,
as
everyone's
been
saying,
you've
organised,
a
whole
slew
of
fantastic
things,
and
just
listening
today,
I
found
out
that
there's
a
whole
bunch
of
more
things
than
apparently
I
needed
to
be
aware
of,
and
then
I'm
a.
Perhaps
some
of
my
slides
are
even
out
of
date,
so
I'm
gonna
tell
you
what
I've
got
and
hopefully
mine
are
much
more
sort
of
like
potential
things
and
sharing
of
information.
H
So
one
I've
been
running
a
blog
called
from
the
directors
desk
and
so
I've
been
trying
there
to
highlight
different
data
science
education
things
specifically.
So
it's
not
really
about
data
science,
the
field
per
se,
lots
of
blogs
about
data
science.
If
you
want
to
read
about
machine
learning
or
other
things,
but
I'm
really
there
trying
to
talk
about
things
related
to
curriculum
planning,
and
course
planning
and
educational
stuff.
H
So
I've
included
things
like
summaries
from
some
of
the
early
webinars
I'm
gonna
run
a
series
of
summarizing
the
National
Academy
of
Science
webinars
over
the
next
couple
weeks
and
some
other
things.
I
would
welcome
anyone
that
would
like
to
do
a
guest
post
on
there,
because
I
would
love
to
be
able
to
make
sure
that
we're
not
reinventing
the
wheel
over
and
over
again,
no
matter
where
you're
writing
curriculum
or
designing
things
a
little
bit
about
what
I've
got
there.
I've
got
a
couple
of
links
to
several
open-source
curriculum
examples
and
curriculum
guide.
H
Is
it,
for
example,
boots
mentioned
earlier
and
I
think
Nick
mentioned
earlier
as
well.
I've
got
summaries
of
some
of
the
webinars
I've
got
some
sketches
of
coding
language
and
things
like
this
and
I'm
open
to
whatever
people
want
to
see
you
there.
The
other
thing
I
wanted
to
at
least
let
people
know
and
hopefully
we'll
see
what
happens
me.
A
collaborator
have
submitted
a
proposal
to
the
date
to
the
NSF
I
use,
funding
stream
for
to
develop
a
data
science
concept
inventory
now.
H
But
just
from
seeing
URIs
presentation,
I
know
we'll
have
to
do
some
adjust,
adjust
Minh
to
our
plan,
because
part
of
what
we
wanted
to
make
sure
we
were
doing
was
facilitating
community
consensus
building,
and
so
some
of
this
is
going
on
through
the
National
Academy
of
Sciences
I've
read
their
interim
report.
It's
a
fantastic
report.
H
H
We
are
starting
from
the
better
points
that
we
already
know
about
and
making
sure
that
we've
got
the
tools
in
place,
including
something
like
a
concept
inventory,
so
that
faculty
aren't
just
trying
to
rediscover
the
things
that
other
disciplines
have
already
figured
out,
and
part
of
that
is
to
have
something
in
place.
That
lets
us
assess
how
well
students
are
actually
learning
things,
the
particular
collaboration
opportunities
that
will
occur
if
things
are
funded.
H
We
intend
to
hold
several
townhall
and
dissemination
events
at
disciplinary
conferences,
and
so
those
will
go
on
throughout
several
different
years
of
the
program
and
that'll
be
a
chance
for
people
to
give
direct
input
on
things
that
we're
discovering
and
talking
about
we're.
Also
gonna
hold
two
different
workshops.
H
The
first
workshop
will
would,
if
funded
would
occur,
ideally
in
mid-august
of
this
year
and
there
we
plan
to
look
at
really
trying
to
identify
misconceptions,
scope
out
what
those
intro
classes
should
really
cover
in
terms
of
the
various
kinds
of
things
that
one
can
learn
about
data
science
and
we
are
really
gonna
make
sure
that
just
like
Renata
did
when
she
put
together
the
keeping
data
science
broad
workshop.
We
want
to
make
sure
that
we
brought
in
educators
from
all
kinds
of
institutions
and
educational
types.
B
Thank
you
so
much
Carl
yeah.
So
far,
so
we
encouraged
that
Carl
has
also
already
already
crossed
posted
some
things
from
the
blog
from
the
original
workshop,
from
his
blog
and
to
the
south
big
data
hub
blog
too.
So
we
would
love
to
keep
that
collaboration
going
as
things
happen
around
education,
so
that
we
have
a
place
where
these
things
can
be
housed
separately,
all
right.
So,
lastly,
I
wanted
to
again
reach
out
to
the
people
on
the
livestream
and
feel
free
to
ask
any
questions
of
any
of
the
panelists.
B
We
did
get
some
questions
that
came
in,
but
specifically
I
had
one
for
the
panel
as
a
whole,
and
that
is
as
we
move
forward.
All
of
these
initiatives
are
around
either
data
literacy.
Looking
at
skills
for
data
science,
what
the
program
should
really
be
is
there
I
heard
Melissa
say
something
about
a
collaborative
space
and
Carl
just
mentioned
a
collaborative
inventory
and
URI
has
done
a
lot
of
work
in
this
leads
for
the
European.
B
What
would
be
the
best
way
that
you
can
see
a
form
happening
or
before
doing
this?
So
we're
gonna
have
a
just
a
document,
but
what
people
be
able
to
contact
you
directly
for
your
individual
initiatives
and
can
we
put
those
into
a
project
for
them
as
well?
No,
just
it's
a
response
to
the
group.
Would
you
want
a
community
forum
for
this,
or
would
you
want
people
to
reach
out
to
you
directly.
E
G
E
E
So
I
think
they're
a
so
it's
better.
If
somebody
have
opportunity
to
support
the
community
forum,
it
should
be
established,
but
the
individual
contact
will
definitely
happens.
Then
discussion
will
go
just
so.
We
didn't
realize
this.
Essentially,
during
the
Edison
project
we
have
a
portal
say
it
probably
will
not
be
capable
to
support
active
discussion.
So
the
issue
that
if
it
would
be
good
that
somebody
will
take
efforts
or
resources,
they
devote
resources
to
support
discussion.
G
We
are,
of
course,
looking
for
funding
to
have
another
in-person
workshop,
but
I
would
love
to
hear
directly
from
anyone
who
is
interested
in
working
on
data
literacy,
because
we
will
certainly
be
setting
up
something
like
a
prozac
or
some
kind
of
online
forum
people
to
work
on,
because,
of
course,
this
isn't
just
us
to
issue
its
global.
And
since
we
already
have
context
with
certainly
setting
up
some
kind
of
way
to
work
globally
and
we
welcome
any
input.
G
D
B
E
Let's,
let
me
explain,
and
yes,
you
knew
about
really
no
big
operate,
this
European
data
science,
if
the
European
Academy
of
data
science,
where
is
another
initiative?
That
is
what
do
not
I
mention
big
data
where
you
Association
in
their
activity,
but
a
European
data
science
like
a
eunuch
atom
of
data
Sciences,
actually.
E
Developed
more
practical
courses
that
were
developed
were
targeted
for
universities
and
by
member
universities.
What
Edison
did
Edison
is
a
coordination
to
support
action,
didn't
take
to
lis,
focused
on
specific
University,
facilitated
community.
What
we
call
champion
university
that
are
cognitively,
developed,
tested
and
contributed
to
the
product
of
the
project
of
Edison
product,
so
this
was
allowed
us
to
be
not
focused
on
delivering
a
couple
of
courses.
What
happens
it
in
a
yet
enter?
Science
Academy
correctly?
E
Yes,
but
we
are
focusing
on
the
foundational
issues
that
also
combine
in
specific
for
European
research
area
and
European
industry,
but
surely
not
limited
only
to
European
focus
of
scope,
because
you
seen
we
cooperated
this
American
associations,
the
bhf
business,
Higher,
Education
Forum,
and
we
actually
succeeded
in
delivering
together
the
competence
framework
and
also
recommendation
for
Asia
Pacific
area.
So
this
is
what
the
difference
so
we
created
for
the
foundational
framework
and
it's
implementing
them,
and
many
interest
is
also
doing.
E
The
partners,
this
I
would
not
say
that
we
did
specific
survey.
We
we
did
market
research
we
actually
based
on
our
initial
stage
of
the
project.
We
were
totally
based
on
the
what
market
demand,
how
they
defined
data
science,
what
is
required
competences
and
starting
from
the
first
delivery,
we
did
a
lot
of
consultation,
presentation,
workshop
and
so
on.
E
At
that
time
we
join
it.
The
study
that
done
by
bgf
turn
Wylie
really
contributed
to
shaping
their
questionary
study
and
also
the
reports.
Specifically,
we
did
in
Europe
to
survey.
One
is
based
focused
on
defined
and
what
competences
are
essential
for
data
scientist
and
second,
what
are
professional
profiles.
E
B
Yes,
No,
thank
you,
and
so
I'd
like
to
say
a
few
words
just
for
contact
information
for
me,
but
I
would
also
want
to
talk
about
the
working
groups,
so
we've
posted
that
a
community
link
look
out
for
it
actually
now
on
the
South
hub
site.
The
link
to
the
full
report
for
this
series,
as
well
as
the
data
program,
we're
going
to
continue
posting
things
from
the
presenters,
as
well
as
a
posting
link
and
check
in
kind
of
a
collaborative
way
as
we
go
along.
B
So
this
I
think
has
been
received
really
quite
enthusiastically
in
a
lot
of
sectors,
both
of
the
other
webinars
that
we've
had
so
far
have
had
115
to
over
200
users
and
participants
in
you
know
reaching
out.
So
if
there
are
I'm
gonna
do
quick
quickly,
just
looking.
If
people
want
to
reach
out
directly
to
me,
then
you
can
feel
free
to
do
so.
We.
G
B
But
I'll
add
it
to
the
survived
and
you
can
always
visit
publicly
like
job
I
like
to
give
it
an
opportunity,
have
six-minute
about
five
minutes
left
or
any
closing
statements
about
the
series
or
about
this
particular
issue
of
education
from
our
panelists.
Oh
just
like
a
quick
minute
from
each
person
kind
of
closing
out
your
views,
as
well
as
how
you've
seen
the
series
progressing
and
going
into
the
future.
So
I'm
gonna
start
with
Katherine.
G
Sure,
well
again,
thank
you
for
this
opportunity
and
or
not
I
have
to
say
that
the
workshop
I'm,
sorry
among
other
things,
is
in
addition
to
producing
this
really
great
and
important
and
useful
report.
It
really
managed
to
gather
us
into
what
I
would
call
a
really
active
community
practice.
So
for
me,
the
important
next
step
is
keeping
this
community
of
practice
together
and
I
know
for
you
as
the
organizer.
You
also
would
have
an
idea
of
the
online
participants
participant
from
other
webinars,
as
well
as
the
general
community
of
authors.
H
So
mostly
I'm,
just
I'm
glad
that
I
was
invited
to
participate
and
I
think
that
where
we've
got
a
lot
of
exciting
things
going
on
in
data
science,
education,
the
fall
was
really
busy
for
all
kinds
of
things
and
I
hope.
Everyone
stays
up-to-date
on.
What's
going
on,
I
look
forward
to
seeing
how,
as
data
science
and
the
hub's
all
start
interacting,
we
sort
of
see
this
environment
evolve
and
I
want
to
remind
everyone
that
we
don't
need
to
reinvent
the
wheel.
Every
time
we
need
to
teach
a
new
class
or
develop
something
new.
D
Did
you
just
yeah,
okay,
I
just
want
to
add
my
congratulations
for
an
outstanding
series
and
for
a
really
good
effort,
and
also
to
support
the
concept
that
we
don't
need
to
be
reinventing
and
doing
things
over
and
over
again.
There
are
a
lot
of
wonderful
things
happening
and
I
hope
we
can
build
on
each
other's
work
and
cooperate
and
collaborate
to
do
the
best
possible
kinds
of
things.
There
certainly
is
still
room
for
a
lot
of
work
and
I'm
glad
to
have
a
chance
to
be
part
of
it.
D
F
F
C
So,
thanks
again
for
what
I
thought
was
a
great
webinar
that
complemented
the
report.
There
are
a
lot
of
things
in
the
report
we
haven't
talked
about
as
much
today,
but
that's
why
they're
there
in
there
for
thinking
about
ethics,
diversity
and
inclusion,
some
more
with
two-year
colleges
but
I'm
excited
by
the
establishment
of
the
network
and
these
these
next
steps.
So
thank
you.
Thank.
E
Okay,
yeah
I
will
do
the
very
quickly
but
I
see
the
benefits
of
establishing
more
regular
cooperation
and
also
release
in
the
United
States
and
Europe
Initiative
Edison
is
not
Notley.
Only
one
initiative
is
related
to
data
science
or
data
technologies,
so
this
is
also
initiative.
This
goes
is
a
fair
initiative
was
very
popular
between
research
organization
and
and
industry,
and
also
research.
Data
Alliance
is
very
active
in
Europe
this
international
organization.
B
Well,
thank
you
all.
So
it
sounds
like
there's
a
lotta
there's,
a
lot
of
work
still
to
be
done,
and
hopefully
we'll
see
again.
I
said
the
webinar.
The
report
is
posted.
All
of
the
webinars
will
be
available
on
the
south.
Have
YouTube
channels
or
RIT
is
up
now
on
top
needy
hub,
our
South
big
data
hub
org,
as
well
as
the
program
announcement
and
we'll
take
some
time
in
getting
the
link
and
coordinating
those
together
so
that
those
can
also
be
made
available.