H2O World 2015 - Jessica Lanford and Chen Huang

1. Calling
Your
Shots
with
Data
How
to
Ask
Smarter
Questions
to
Make
Better
Business
Decisions
November
9,
2015
Jessica
LanfordChen
Huang

2. Need
a
handy
conference
guide?
Download
our
app,
“H2O
World
2015”

3. What
do
these
stickers
mean?
I have H2O
Installed
I have Python
installed
I have R
installed
I have the H2O
World data
sets
Pick
up
stickers
or
get
install
help
at
the
information
booth

4. Agenda
• Introduction
• Business
Decision
Process
• Tools
&
Resources
• Bridging
the
Communications
Gap
• Q&A

5. Business
Decision
Process
Make
data-­‐
informed
business
decisions
Ask
business
questions
Define
business
problems
Analysis
Process

6. Motivators
that
Influence
Business
Decisions
Business
decisions
will
impact:
Customer
product
interaction
Customer
and
company
engagement
Product
development
???
1
2
3
4
Your%
customers%
Your%products%
/%services%/%
offerings
Your%business
1
2 3
4

7. Asking
the
“Right”
Questions
The
answers
to
the
business
questions
will
ultimately
provide
business
context
for
the
“Analysis
Process”.
Make
data-­‐
informed
business
decisions
Ask
business
questions
Define
business
problems
Analysis
Process

8. Data$
Scientist
Analysis
Process
Analysis(Process
Analyze(Data(&(Find(Insights
1.(Frame(the(
question
2.(Collect(raw(data
3.(Prepare(and(
explore(data
4.(Develop(model
5.(Evaluate,(
validate,(and(
interpret(results
6.(Communicate(
and(visualize(
results
Discussion(to(reach(
agreement(in(problem(
statement
Translate(business(questions(
and(context(into(a(problem(
statement

9. Complete
Business
Decision
Process
Analysis(Process
Analyze(Data(&(Find(Insights
Define(business(problems Ask(business(questions
Make(data?Informed(
business(decisions
1.(Frame(the(
question
2.(Collect(raw(data
3.(Prepare(and(
explore(data
4.(Develop(model
5.(Evaluate,(
validate,(and(
interpret(results
6.(Communicate(
and(visualize(
results
Business(
decision(
maker
Data(
Scientist
Discussion(to(reach(
agreement(in(problem(
statement
Translate(business(questions(
and(context(into(a(problem(
statement

10. Tools
and
Resources
Data$Science$Team
R,$Python,$Scala,$
Java,$CoffeeScript /$
JavaScript,$SQL,$Julia
Languages
Jupyter$(IPython),$
H2O$Flow,$…$
Notebooks$+$IDEs
Skillsets:
• Domain$knowledge
• Math$and$statistics
• Programming$ skills
• Databases
• Machine$learning
• Communication$and$visualization

11. Bridging
the
Communication
Gap

12. What
is
Machine
Learning?
• Machine
reads
the
data,
learns
from
the
data,
uses
it
to
make
predictions
• Can
show
you
correlation
but
not
necessarily
causation
• Can
find
relationships
and
patterns
within
volumes
of
data
that
the
human
mind
is
incapable
of
processing
Note:
There
is
no
“right”
or
“best”
model
that
a
data
scientist
can
use.
The
model
used
is
dependent
on
the
data,
problem,
and
the
data
scientist.

13. Supervised
Learning
Business
Applications:
• Classification
• Twitter
sentiments:
Rant
-­‐>
negative,
Rave
-­‐>
positive
• Coffee
vs.
tea
vs.
soda
drinker
• Recommender
systems
• Netflix’s
“More
Like
This”
• Amazon’s
“Customers
Who
Bought
This
Item
Also
Bought”
• Fraud
detection
• Authorizing
transactions
• Known
right
answer,
using
model
to
verify
• Algorithm
tries
to
predict
results
• Based
on
its
training
data,
the
program
can
make
accurate
decisions
when
given
new
data
• Examples
of
algorithms
and
models:
GLM,
DRF,
GBM,
Deep
Learning
Data
Science
Concept:

14. Unsupervised
Learning
Business
Applications:
• Anomaly
detection
• outliers:
detecting
irregular
heartbeats
• computer
security
with
unauthorized
access
• Clustering
• Grouping
users
by
salary
• Grouping
users
by
behavior
• No
“known”
answer,
using
algorithms
to
determine
answer
• Algorithm
tries
to
identify
patterns
in
the
data
• General
understanding
of
input
data
where
no
prediction
is
needed
• Examples
of
algorithms
and
models:
K-­‐means,
PCA
Data
Science
Concept:

15. Classification
(Supervised)
Business
Applications:
• Will
customers
upgrade
to
new
software?
• What
age
groups
tested
well
for
this
new
TV
show?
(marketing
campaigns)
• Nigerian
419
(spam
classification)
• Will
the
real
Barack
Obama
please
stand
up?
(fraud
detection)
• Classification
is
the
process
of
taking
an
input
and
assigning
a
label
to
it.
• The
labels
could
be
binomial
(Yes,
No)
or
multinomial
(High,
Medium,
Low).
• Examples
of
algorithms
and
models:
Random
Forest
Data
Science
Concept:

16. Regression
(Supervised)
Business
Applications:
• How
much
money
would
a
user
who
has
reached
level
200
in
CandyCrush
spend
on
in-­‐app
purchases?
(forecasting)
• How
much
would
a
customer
expect
to
pay
for
car
insurance
based
on
age,
gender,
and
car
type?
(prediction)
• How
many
registered
meetup.com
attendees
will
actually
show
up
based
on
past
event
registration
and
attendance?
(prediction)
• Regression
predict
a
continuous
numerical
value
output
• Examples
of
algorithms
and
models:
Linear
Regression,
Random
Forest
Data
Science
Concept:

17. Deep
Learning
(Supervised
and
Unsupervised)
Business
Applications:
• Scanning
mug
shots
of
suspects
against
FBI
database
(scanning
image
classification)
• Siri
(language
processing)
• Early
detection
of
frustrated
customers
who
call
into
call
centers
(audio
processing)
• Uses
“features”
(multiple
variables
impacting
a
result)
to
identify
patterns
• Uses
results
to
iteratively
improve
predictions
for
new
data
Data
Science
Concept:

18. Clustering
(Unsupervised)
Business
Applications:
• Identify
different
types
of
shoppers
based
on
purchasing
history
to
create
exclusive
promotions
(market
segmentation)
• Identifying
groups
of
products
people
like
to
buy
online
• Identify
geographic
locations
where
a
national
mobile
carrier
should
install
its
next
cellular
tower
to
optimize
for
its
user
base
• Grouping
a
set
of
objects
in
the
same
group
that
are
more
similar
to
each
other
than
other
groups
• Examples
of
algorithms
and
models:
K-­‐means
clustering,
hierarchical
clustering,
DBSCAN
Data
Science
Concept:

19. Business
Examples:
Types
of
Machine
Learning:
Machine
Learning
Summary
Supervised
• Calculating
estimated
lifetime
value
• Forecasting
and
prediction
• Recommendation
engine
• Fraud
detection
Unsupervised
Data
Science
Concepts:
• Anomaly
detection
• Determining
customer
behavior
• Imagine,
text,
and
audio
processing
• Classification
• Regression
• Deep
Learning
• Deep
Learning
• Clustering

20. If
you
Want
to
Learn
More…
• StackExchange:
stats.stackexchange.com
• Quora:
quora.com/Machine-­‐Learning
• Data
Science
in
H2O:
http://docs.h2o.ai/
h2oclassic/datascience/top.html
• Visualization
Introduction
to
Machine
Learning:
r2d3.us/visual-­‐intro-­‐to-­‐machine-­‐learning-­‐part-­‐1
• Machine
Learning
Map:
http://scikit-­‐learn.org/
stable/tutorial/machine_learning_map/

21. Questions
and
Answers