►
From YouTube: Education and Workforce WG: Keeping Academic Data Science On-Track: What to Assess and How
Description
Dr. Karl Schmiudt
Talk Title: Keeping Academic Data Science On-Track: What to Assess and How
Bio: Karl is engaged in a variety of Data Science Educational, most recently by serving as a member of the ACM Data Science Task Force to develop their curricular recommendations for Data Science. He was the founding director of Valparaiso University's undergraduate Data Science degree and ran Valpo's Masters in Analytics and Modeling program for 5 years. Karl is now the Program Coordinator for Data Analytics at Trinity Christian College and is part of implementing the new program there, alongside a new Data Center. Karl specializes in data science as applied to networks and graphs. He’s done work with applying network algorithms to improve genome assembly and published fundamental work in understanding K-Dense graphs. He’s also very interested in finding ways to connect data science with social good, especially through the classroom and experiential learning. Karl has taught designed courses in Optimization, Data Mining, and both Introductory and Advanced Data Visualization. He’s also designed and implemented an Introduction to Data Science course targeted at students with minimal programming experience that centers around a data-driven service-learning project.
A
B
Okay,
here
we
go
okay,
I
had
to
find
our
place
to
unmute
myself,
hi
hi,
I'm
carl
schmidt,
I'm
currently
at
trinity
christian
college.
I
started
there
in
the
fall
and
they
hired
me
to
do
program,
coordination
and
design
for
a
new
data
analytics
program
there.
I've
also
been
at
valparaiso
university,
hi
john.
B
How,
besides
giving
you
some
pointers
about
stuff
that,
I
think
is
really
important
to
make
sure
we're
thinking
about
as
we're
implementing
these
programs
and
courses
so
that
we
are
continuing
to
keep
the
diversity
that's
already
existing
in
our
classes,
successful.
So
with
that,
I
will
go
in
so
to
summarize,
for
anything,
if
you,
if
you
stop
paying
attention,
pay
attention
to
this
slide,
and
then
you
can,
you
can
tune
in
and
out
a
little
bit
more.
B
Here
are
your
two
sort
of
quick
takeaways
right
now,
the
most
comprehensive
set
of
of
competencies
or
learning
outcomes
and
stuff
is
still
the
edison
data
science
comp
or
the
edison
project's
competency
framework
for
data
science.
This
is
something
that
came
out
of
the
eu
in
2017
and
I'm
still
referring
to
it
as
the
best
most
comprehensive
thing.
B
This
the
fact
that
there's
been
a
couple
of
things
published
since
then,
as
far
as
I
can
tell
nothing
else,
seems
to
have
the
comprehensibility
of
this,
although
there
are
some
things
that
are
coming
out
now,
that
are
sort
of
addressing
some
of
these
pieces
and
sort
of
that
follow-up
is,
if
you're,
only
looking
at
computing
competencies.
So
this
does
not
include
math
and
stat
issues
and
and
minimally
contains
domain
stuff.
The
acm
guidelines
is
more
complete
than
edison
and
a
lot
more
detailed,
but
you
can't
use
it
for
every
single
thing.
B
You
to
some
of
those
places
that
say
those
are
stuff
that
really
disenfranchises
people
that
are
perhaps
coming
from
a
slightly
weaker
background.
So
the
fact
that
they've
got
a
mastery-based
model
means
that
those
c
plus
students
coming
in
are
not
going
to
be
dis
advantaged.
Besides
the
general
knowledge
that
they
don't
have
in
terms
of
the
assessment,
as
opposed
to
simply
saying
oh
well,
they
didn't
achieve
things.
Well,
it's
not
being
assessed
against
a
cheat,
it's
not
being
a
sense
against
the
other
students
that
did
achieve
things.
B
That's
when
you
guess
obsessed
against
did
they
meet
the
goals
that
we
want
them
to
take
all
right.
So
that's
the
quick
takeaways,
here's
a
little
bit
more,
a
dive
in.
So,
if
you're
not
aware,
there's
about
counties
there's
about
six
curricular
level,
guidelines
that
are
in
existence
in
publication
that
you
can
go
find
someplace.
B
The
edison
project
is
probably
the
earliest
one.
That's
out
there
and
most
complete
lots
of
people
refer
to
the
park
city
report,
and
I
have
that
wrong.
That
citation
wrote
is
d-vox
at
all
there's
also
some
public
there's
also
a
public
at
least
one.
I
think.
There's
actually
multiple
now,
but
I
only
reference
one
that
are
sort
of
assessing
deployed
programs,
there's
one
from
the
business
education
side.
That
is
still
talking
about
data
science
and
analytics.
B
That
paper
came
out
in
harvard
data
science
review
and
it
right
now
it's
at
the
high
level,
although
presumably
it'll
continue
to
come
out
and
then
the
last
is
acm
just
approved
a
week
or
two
ago
the
the
education
board
approved
the
final
draft
of
the
computing
competencies
for
undergraduate
data.
Science
curriculum
I've
linked
here
where
the
task
force
website
is,
although
I
just
checked,
and
this
morning
we
still
don't
have
the
actual
final
draft
up.
B
We
still
have
second,
the
second
draft,
which
is
close,
but
we've
we've
definitely
made
some
improvements
since
then,
so
keep
your
eyes
peeled.
I
know
that
we've
got
a
talk
at
the
symposium
for
data
science
and
statistics
in
june,
and
we've
got
a
talk
at
sixty,
so
the
computer
science
education
conference
coming
up
in
march
both
announcing
and
sort
of
presenting
some
of
the
stuff
from
there.
I'm
not
going
to
talk
about
that
in
detail.
Come
to
those
talks.
B
If
you
want
to
hear
more
detail
about
the
curriculum
guidelines,
but
they
are
out
there
they're
there
and
if
you're
familiar
with
the
cs,
2013
they're
at
that
level
of
detail,
so
they're
much
more
detailed
in
terms
of
what
you
can
pull
out
for
particular
courses
or
even
thinking
about
high-level
stuff.
But
again
I'll
put
the
caveat.
It's
only
for
computing
things.
Please
do
not
take
offense
all
you
mathematicians
and
statisticians.
We
know
we
didn't
cover
math
and
stats.
It's
coming
we're
trying
to
get
everyone
on
board
for
it.
B
Is
this
one,
this
open
data
and
I
think
that's
an
artifact
of
the
us-centric
curriculums
versus
the
european
centric
edison
project,
but
it's
sort
of
interesting
to
say
that
none
of
the
other
things
included
anything
about
this
okay
and
then
sort
of
recognizing
the
issue
about
domain.
The
addison
actually
has
so
the
main
knowledge
directly
embedded
in
their
curriculum
competencies,
and
most
of
these
other
curriculums
don't
have
any
explicit
domain
things
included.
So
that's
the
econometrics,
the
data,
different
marketing
technologies
and
the
operations.
B
So
this
is
not
me
being
like
hey
or
any.
These
are
the
one
one
or
two
people
that
do
it,
but
both
nc
wit
and
the
american
association
of
university
women
have
fantastic
collections
of
resources
that
talk
about
all
the
issues
and
if
you're
only
going
to
go,
read
one
thing
about
this:
go
read
that
aauw
solving
the
equation.
B
B
B
If
they
have
improved
more,
maybe
they
should
be
getting
a
better
grade.
Okay,
the
other
piece
is-
and
I
find
this
challenging
and
I'm
sure
other
faculty
do
as
well
establishing
your
expectations
before
you
give
out
assignments
and
before
early
in
the
course.
So
this
is
this
idea
of
like
presenting
a
rubric
when
you
assign
something
not
when
you
go
into
grade
it,
and
what
this
does
is
help.
B
There
is
no
paper
out
yet
successfully.
It
got
rejected
from
harvard
data
science
review.
So
I
have
a
white
paper.
If
you
want.
You
know
a
draft
of
some
of
this
stuff
with
the
detailed
literature
analysis
and
statistical
analysis
survey
that
I'm
happy
to
share
individually,
but
right
now,
there's
not
a
published
citation
that
I
can
point
you
to
thinking
about
that.
D
A
B
Yes,
I
can
probably
share
that
with
you.
Let
me
check
with
my
collaborators
first
because,
like
I
said
we
don't
have
it
actually,
only
one
journal
has
seen
a
pre-copy
of
it,
and
so
it's
not
necessarily
out
for
confirming
so
and
we're
in
the
process
of
resubmitting
it.
So
we're
hoping
it'll
be
out
soon,
because
we
know
people
want
it,
but
I
will
either
share
it
directly
with
you
or
if
individual
people
know
that
they
want
it.
A
Sounds
good,
so
we
have
a
little
time
for
questions
here
and
this
one
we
really
wanted
to
get
people's
experience
or
where
you
are
maybe
in
assessing
or
how
you're
thinking
about
assessment
program.
This
day
has
been
all
about.
You
know
really
looking
at
courses
but
also
looking
at
how
you
might
assess
these
courses.
So
thank
you,
carl
for
a
lot
of
that.
A
B
A
Yeah
they're
very
fundamental
fundamentals,
up
type
of
thing
and
way
back.
They
were
thinking
of
you
know
implementing
an
api
for
this.
I
don't
know
how
far
they
the
project
ended
before
they
did
the
api,
so
someone
would
have
had
to
volunteer
so
but,
and
still
it
was
a
very
comprehensive,
they
did
all
the
things
that
they
were
looking
for.
So
I
wanted
to
go
around
and
usually
do
that
at
the
beginning,
but
I
figured
this
time.
A
A
E
Hi
everyone
I'm
cynthia
searcy,
I'm
associate
dean
for
academic
innovation
and
strategy
in
policy
study
school.
So
we're
not.
You
know:
data
science
in
a
business
school
or
in
arts
and
sciences
and
computer
science.
So
we
have
built
recently
or
building
out
a
curricula
for
our
students,
who,
I
would
say,
is
like
data
science
lite,
but
we
have
used
this
framework
for
the
edison
framework
for
identifying.
E
You
know
the
knowledge
and
skills
that
students
would
need
to.
You
know
have
a
minor
in
and
policy
analytics
as
we're
calling
it,
and
so
it's
been
very
helpful
and
then,
of
course,
hearing
about
your
courses
a
particularly
earlier
hunter's
course
I'd
love
to
get
your
your
syllabus.
You
know
because
we
are
you
know
we
have.
We
have
economists,
we
have
con
matricians,
and
so
we
have
people
who
are
coding
and
doing
things.
But
you
know
our
students
aren't
necessarily
learning
python
and
data
science.
E
A
F
My
role
is
director
of
research
program,
so
I
work
mostly
with
with
faculty
on
their
grant
proposals,
but
I'm
also
work
with
corporate
sponsors
on
our
sourcing
projects
for
our
capstones,
because
our
students
do
you
know
experiential
learning
with
real
world
projects,
I'm
happy
to
answer
any
questions.
We
are
about
to
go
to
our
faculty
center
with
a
phd
program.
G
A
And
feel
free
anyone
that
hasn't
actually
questions
about
assessment.
You
can
put
it
on
the
chat
and
interrupt
my
calling
people
as
well,
so
that
you
know,
if
you
have
a
question
for
the
speakers
or
something
to
share
about
assessment,
feel
free
to
just
say:
hey.
I
want
have
something
to
say:
I'm
just
going
around
as
we
do
this
so
susie.
D
Hi,
I'm
from
the
university
of
tennessee,
and
I
am
associate
dean
of
research
for
our
college
of
communication
and
information
and
we're
one
of
four
colleges
that
are
in
the
process
of
putting
together
an
mouth
and
we're
establishing
an
under
a
interdisciplinary
undergraduate
data
science
program
that
is,
does
not
house
in
any
one
particular
college,
but
is
actually
a
partnership
of
four
lead
colleges
and
in
several
other
colleges
that
are
joining
us
on
this
journey.
So
these
have
been
great
sessions.
I
haven't
made
them
all,
but
the
ones
I've
made
have
been
awesome.
A
D
B
H
About
assessment,
not
to
the
level
of
depth
that
we
need-
and
so
I
think
these
conversations
are,
are
really
important
and,
more
generally,
I
think,
as
we
particularly
as
we
go
out
to
community
colleges
and
try
to
understand
that
transfer
from
from
associates
programs
to
four-year
colleges.
It's
gonna,
be
that's
gonna,
be
really
important.
H
The
other
part
that
I'm
wondering
about
is
ways
in
which
some
of
these
conversations
can
dovetail
with
other
ongoing
efforts
to
blow
up
the
stem
curriculum
more
generally,
to
make
sure
it's
much
more
inclusive,
welcoming
the
high
impact
practices
are
part
of
that.
It's
it's
difficult
to
be
teaching
the
kind
of
capstone-like
courses,
but
they're
so
critical
for
our
students.
How
do
we
kind
of
make
sure
that's
and
leverage
stuff
we
have
going
on
from
other
colleagues
outside
of
data
science.
A
But
looking
at
which
competencies
we
actually
broke
down,
which
types
of
school
mentioned,
what
types
of
of
challenges
and
which
types
of
opportunities
you
know
just
to
say
that
it's
not
symmetric
what
the
challenges
are
and
what
the
opportunities
are
in
different
places.
So
you
can
kind
of
get
that
sliding
scale
of
what
things
may
made
the
list
for
those
I'm.
B
Going
to
second
nick's
comments
about
the
the
national
academy
report
that
those
are
that's
a
really
great
place
to
think
about
the
like
some
of
the
big
picture,
how
leveled
at
the
curriculum
level
and
then
pulling
out
some
of
those
content.
The
like
the
topical
things
from
some
of
the
places
that
I
was
highlighting.
A
G
Hello.
Everyone,
yes,
can
you
know,
can
you
give
me
okay?
This
is
venkata
and
ludi
from
alabama
a
m
university
yeah,
it's
nice
to
get
insights
into.
You
know
the
assessment
side
of
it,
even
though
I've
been
part
of
as
a
faculty
and
as
well
as
the
chair
of
the
department
for
some
years
in
the
into
the
assessment
of
computer
science
courses,
but
I
think
the
data
science
is,
you
know
a
recent
thing
and
we
just
added
some
courses,
but
I've
been
for
the
last
10
years.
G
My
interest
in
big
data
is
mainly
of
the
cybersecurity
side
that
we've
been
doing.
We
had
a
concentration
in
it
and
my
master's
students
actually
do
this
related
research
into
you
know
using
python
and
machine
learning
techniques.
For
you
know
this
big
data
in
you
know
whether
it's
malware
or
email,
phishing
or
website
phishing
data,
so
mostly
cyber
security
related
domain.
G
It's
good.
Let
me
turn
my
camera
around
yes
good
morning.
My
name
is
mark
medina,
I'm
a
faculty
and
a
chair
of
the
computer
science
program
at
the
university
of
the
virgin
islands.
Yes,
so
we
launched
a
new
program,
a
new
minor
in
data
science
in
fallout
2020,
so
we've
not
started
assessment
yet
so
I
think
we're
going
to
use
a
lot
of
you
resources,
yeah.