TWO DECADES OF TEACHING DATA MINING AND ADVANCED DATA MINING - Olivia Sheng Olivia Sheng - Simon Business School
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TWO DECADES OF TEACHING DATA
MINING AND ADVANCED DATA MINING
Olivia Sheng
Operations and Information Systems © Olivia ShengEARLIER DAYS
▪ Data mining (DM) before 2012
▪ Only one class in business school
▪ Only two to three classes on campus
▪ Advanced data mining (ADM) before 2016
▪ Only additional data mining course in business school
▪ Overlaps with other courses in business school
▪ Decision analysis - decision trees
▪ MBA IS core – overview of the DM methods
Operations and Information Systems © Olivia ShengCHANGING LANDSCAPE (BIZ SCHOOL)
▪ A sea of data mining, business analytics, case
oriented ML, text analytics, regression, case
oriented, data analysis, big data
▪ Mostly in R
▪ Overlaps in methods and related content
▪ More practices maybe beneficial for students
▪ Increasing concerns
Operations and Information Systems © Olivia ShengAVOIDING OVERLAPS
▪ Moving targets or difficult for pre-recorded online
content
▪ What I have done
▪ Big Data – Learning from data at scale (2018)
▪ First adoption of Databricks cloud for teaching data
processing, DM, structured streaming (twitter, e.g.) and a
big deep learning with pre-trained image models in
pyspark
▪ Advanced Data Mining – avoid text analytics and re-
prep different topics in python
Operations and Information Systems © Olivia ShengDM LEARNING OBJECTIVES
▪ Apply ML/AI methods to address common business
questions
▪ Data selection, preparation and exploration
▪ Method understanding
▪ Classification, numeric prediction, association rule mining
and clustering
▪ Concepts and how a method works
▪ Extremely limited math required
Operations and Information Systems © Olivia ShengDM LEARNING OBJECTIVES
▪ Evaluate, understand, improve, and explain models
and performance
▪ Select evaluation metrics
▪ Select features
▪ Select hyper-parameter settings
▪ Feature importance and inherent glassbox explanation
▪ rstudio.cloud (since 2020)
Operations and Information Systems © Olivia ShengADM LEARNING OBJECTIVES
▪ Topics – to fit evolving cybersecurity programs
▪ Mainly mining rare but interesting data
▪ Other topics
▪ graph mining and temporal data patterns
▪ Insufficient time and practices
▪ Better to be removed
▪ Select hyper-parameter settings
▪ Feature importance and inherent glassbox explanation
Operations and Information Systems © Olivia ShengADM LEARNING OBJECTIVES
▪ Enhance preparation for tackling real world data
mining tasks
▪ Topics – to fit evolving cybersecurity programs
▪ Mainly mining rare but interesting data
▪ Other topics
▪ graph mining and temporal data patterns
▪ Insufficient time and practices
▪ Better to be removed
▪ Python and scikit-learn with Google Colab
Operations and Information Systems © Olivia ShengDELIVERY AND PEDAGOGY
▪ Online and cloud IDE
▪ Very helpful for learning technical content
▪ Any place any time
▪ Very hands-on
▪ Relative to other ML/AI courses in business school
▪ No quizzes and final exam in ADM
Operations and Information Systems © Olivia ShengDELIVERY AND PEDAGOGY
▪ Project-oriented learning
▪ A firm believer
▪ Not a fan of group projects
▪ Cannot accommodate projects in DM courses with a
number number of students
▪ Need to know the project and data well
▪ Assign optional data sets in ADM
Operations and Information Systems © Olivia ShengEVOLVING IDEAS
▪ DM
▪ Change from R to Python
▪ Some students need personalized requirements
▪ Still not sure about how to accommodate projects
▪ ADM
▪ Optional presentations to benefit students’ projects
▪ additional topics, methods and studies
▪ Research-oriented guidance for students who plan to
apply for a Ph.D. study
Operations and Information Systems © Olivia ShengINDUSTRY EXPECTATIONS FOR TRANSFORMING IS EDUCATION—
DISCUSSION ON AACSB MACUDE IS TASK FORCE FINDING
Industry Expectations for Transforming IS
Education—Discussion on AACSB MaCuDE IS Task
Force Finding
The
sec
▪
ond
web
inar
in a
seri
es
The second webinar in a series of seminars organized by AIS and AACSB MaCuDE
of
sem
inar
s
org
aniz
ed
by
AIS
and
AAC
SB
Ma
CuD
E
proj
ect’
s IS
task
forc
e
pro
vide
s
the
me
mb
ers
of
the
IS
com
mu
nity
project’s IS task force provides the members of the IS community a preliminary
a
preli
min
ary
ove
rvie
w
of
the
findi
ngs
of
Sta
ge
II of
the
proj
ect.
The
disc
ussi
on
will
part
icul
arly
focu
s on
the
role
of
adv
anc
ed
anal
ytic
s
and
AI
in
futu
re
busi
nes
s
sch
overview of the findings of Stage II of the project. The discussion will particularly focus
ool
curri
cula
and
the
way
s in
whi
ch
dev
elop
me
nts
in
thes
e
are
as
of
stud
y
and
prac
tice
are
imp
acti
ng
the
field
of
IS
in
the
busi
nes
s
sch
ool
cont
on the role of advanced analytics and AI in future business school curricula and the
ext.
The
outc
om
es
of
the
con
vers
atio
n
will
info
rm
the
Ma
CuD
E IS
task
forc
e’s
Sta
ge
II
rep
ort
to
this
AAC
SB
proj
ect
as a
who
le.
The
sem
inar
will
also
ways in which developments in these areas of study and practice are impacting the field
incl
ude
com
me
ntar
y
not
es
fro
m
som
e
emi
nen
t
indu
stry
exp
erts
.
Plea
se
join
us
to
help
ens
ure
that
the
voic
e of
the
AIS
com
mu
nity
is
hea
rd
of IS in the business school context. The outcomes of the conversation will inform the
in
this
imp
orta
nt
disc
ussi
on!
Wh
en:
Jun
e 9,
202
1 at
10
a.m
.
East
ern
Tim
eRS
VP:
http
s://
us0
2we
b.zo
om.
us/
web
inar
/reg
ister
/W
MaCuDE IS task force’s Stage II report to this AACSB project as a whole. The seminar
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LiZy
Q-
SR8
ixVJ
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KFn
3g
will also include commentary notes from some eminent industry experts. Please join us
to help ensure that the voice of the AIS community is heard in this important discussion!
▪ When: June 9, 2021 at 10 a.m. Eastern Time
▪ RSVP: https://us02web.zoom.us/webinar/register/WN_sLiZyQ-SR8ixVJre8KFn3g
Operations and Information Systems © Olivia ShengOperations and Information Systems © Olivia Sheng
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