What technical, statistical and communication skills a manager should know to manage a data science project

1. What
Managers
Need
to
Know
about
Data
Science
Annie
Flippo

2. Outline
• What is data science
• Industry trends
• What is data
• The Optimal Data Scientist
• The Optimal Manager
• Topics in Data Science
• Topics in Cloud Computing

3. Who
am
I?
Annie Flippo
Data Scientist
Software Engineer
Product Manager
Database Developer

4. What
is
Data
Science?

5. Usage
of
Data
Science
Finance:
fraud
detecAon,
score
buying
habits,
calculate
risks
Insurance:
inspect
driving
habits,
assess
risks,
determine
premiums

6. Usage
of
Data
Science
Biometrics:
wearable
devices
to
monitor
and
improve
health
Digital
MarkeAng:
recommender
systems,
audience
segmentaAon,
retargeAng,
churn
predicAon

7. Usage
of
Data
Science
Retail:
Walmart
launches
compeAAon
to
solve
business
problems
and
to
recruit
talent
Online:
NeHlix
launched
$1
million
prize
to
improve
recommendaAon
system

8. Usage
of
Data
Science
Healthcare:
Heritage
Network
launched
a
compeAAon
to
predict
the
probability
of
hospitalizaAon
of
paAents.
ScienAfic:
NaAonal
Data
Science
Bowl
to
predict
ocean
health:
one
plankton
at
a
Ame

9. Why
Should
YOU
Care?
According
to
McKinsey1
(2011),
Big
Data:
The
next
fron5er
for
innova5on,
compe55on,
and
produc5vity.
“By
2018,
the
United
States
alone
could
face
a
shortage
of
140,000
to
190,000
people
with
deep
analyAcal
skills
as
well
as
1.5
million
managers
and
analysts
with
the
know-­‐how
to
use
the
analysis
of
big
data
to
make
effecAve
decisions”

10. Why
Should
YOU
Care?
According
to
Forbes2
(Oct
2015),
The
Hunt
For
Unicorn
Data
Scien5sts
LiCs
Salaries
For
All
Data
Analy5cs
Professionals
• Experienced
data
scienAsts
are
paid
more
than
$200k
per
year
• Median
salary
for
data
scienAst
increased
from
$115,250
to
$125,000
in
one
year
• Managers
managing
large
teams
can
expect
a
median
salary
of
$235,000

11. Because
it’s
a
growing
and
exciAng
field
with
high
compensaAon!

12. Explosion
of
Data
Science
Why
now?
• Storage
cost
has
decreased
dramaAcally
• CompuAng
power
has
increased
exponenAally
• People
are
carrying
smartphones,
mini
supercomputers
in
their
pockets
• Perfect
intersecAon
of
data
availability
and
compuAng
power
for
analyAcs

13. Massive
amount
of
data
Streaming
into
your
company
…

14. What
is
data?
It
can
be
raw
web
traffic
logs
…

15. What
is
data?
Semi-­‐structured
data
from
APIs
…

16. What
is
data?
Or,
structured
data
from
databases…
…
what
to
do
with
all
this
data?

17. The
Data
ScienAst
Can
wrangle
data
from
many
sources
or
formats

18. The
Data
ScienAst
do
deep
data
exploraAons
…
and
perform
thorough
analyses

19. DS
Skills
Inferred
by
Job
Openings
• Ph.D.
in
math,
staAsAcs,
engineering
or
physical
science
(Is
it
really
required?)
• Has
5+
years
in
programming
experience
in
Java,
Scala,
Python,
R,
SQL,
MapReduce,
etc.
• Has
5+
years
experience
in
most
of
the
Apache
Open
Source
Technologies
(e.g.
Hadoop,
Spark,
Hive,
Pig,
Kaka,
etc)*
• Tell
a
story
like
a
novelist
(coherently
and
beauAfully)
*
By
the
Ame
you
read
this
footnote,
the
Apache
stack
has
already
grown.

20. The
OpAmal
Data
ScienAst
Is
a
person
with
deep
staAsAcal
and
machine
learning
knowledge,
extensive
somware
engineering
skills
and
well-­‐versed
in
business
strategy!

21. The
OpAmal
Data
ScienAst
–
Take
2
Personality
Traits3
• Compulsive
• Propulsive
laziness
• Drive
to
create
and
learn
• Irritable
determinaAon
• InsensiAvity
to
pain
(hmm…)
• Integrity
• Humility

22. The
OpAmal
DS
Manager
• Former
data
scienAst
(good
to
have
but
not
necessary;
that’s
just
asking
for
another
unicorn!)
• Actually
interested
in
managing
people
• Thirst
to
learn
• Apt
in
managing
different
projects
• PaAent
and
diplomaAc
to
manage
a
diverse
group
of
data
scienAsts
and
business
owners
• Understand
when
to
go
with
an
80/20
approach

23. Data
ScienAsts:
The
Challenge
of
Managing
Stubbornly
Autonomous
Experts4
“I
no5ced
…
that
data
scien5sts,
but
also
sta5s5cians
and
top
coders,
oCen
have
difficul5es
accep5ng
orders
from
managers
who
don’t
have
technical
skills
themselves.”
-­‐
Istvan
Hajnal

24. Journey
to
become
a
DS
Manager
Nate
Silver
on
Finding
a
Mentor,
Teaching
Yourself
StaAsAcs,
and
Not
Sesling
in
Your
Career5
• Find
a
Mentor
(Yes,
even
if
you’re
already
a
senior
manager)
• Teach
Yourself
(online
resources,
MOOCs)
• Understand
the
life-­‐cycle
of
a
data-­‐driven
project
• Just
do
it!

25. Why
Just
Do
It?
Why
do
I
need
to
learn
about
data
science
and
manage
data
projects?
“I
have
[insert
#
of
years]
years
of
experience
in
[insert
my
industry].
I’m
comfortable
and
successful
being
a
[insert
your
Atle
here].”

26. Company
Structures

27. Data
Sources
Data
projects
are
lurking
everywhere
…

28. Machine
Learning

29. Machine
Learning

30. Machine
Learning

31. Machine
Learning
Google
X
laboratory5

32. Machine
Learning
Google
Research6

33. Data
Science
Concepts
PredicAve
AnalyAcs
ClassificaAon
RecommendaAon
Systems

34. Big
Data
Technology

35. Topics
in
Cloud
CompuAng
New
services
added:

36. Your
Job:
Provide
Guidance
Tell
us
a
data
story
…
about
your
business
Do
you
understand
the
outcome?
What
is
your
recommendaAon
to
the
business?

37. Gezng
Started:
Locally
Meetups
• LA
R
users
group
• LA
Machine
Learning
• LA
Data
Warehouse,
BI
&
AnalyAcs
• LA
Big
Data
Users
Group
Conferences:
• datascience.la
• bigdatadayla.org

38. Gezng
Started:
Podcasts
dataskepAc.com
thetalkingmachines.com

39. Gezng
Started:
MOOCs

40. Good
Places
to
Start
Data
Science
for
Business
by
Foster
Provost
&
Tom
Fawces

41. Good
Places
to
Start
Doing
Data
Science
by
Rachel
Schus
&
Cathy
O’Neil
(mathbabe.org)
Free
at
www.columbiadatascience.com

42. Good
Places
to
Start
The
Art
of
Data
Science
by
Roger
Peng
&
Elizabeth
Matsui
hsps://leanpub.com/artofdatascience

43. Get
Kids
Started
scratch.mit.edu
www.ixl.com

44. Thank
You!
Annie
Flippo
@ACflippo
Slides
are
available
at
goo.gl/1X2NMH

45. References
1. hsp://www.mckinsey.com/insights/business_technology/
big_data_the_next_fronAer_for_innovaAon
2. hsp://www.forbes.com/sites/gilpress/2015/10/09/the-­‐hunt-­‐for-­‐unicorn-­‐
data-­‐scienAsts-­‐lims-­‐salaries-­‐for-­‐all-­‐data-­‐analyAcs-­‐professionals/
3. hsp://cdn.oreillystaAc.com/en/assets/1/event/119/Data%20Science
%20Bootcamp%20PresentaAon.pdf
4. hsp://www.ibmbigdatahub.com/blog/data-­‐scienAsts-­‐challenge-­‐managing-­‐
stubbornly-­‐autonomous-­‐experts
5. hsps://hbr.org/2013/09/nate-­‐silver-­‐on-­‐finding-­‐a-­‐mentor-­‐teaching-­‐yourself-­‐
staAsAcs-­‐and-­‐not-­‐sesling-­‐in-­‐your-­‐career/
6. hsp://www.nyAmes.com/2012/06/26/technology/in-­‐a-­‐big-­‐network-­‐of-­‐
computers-­‐evidence-­‐of-­‐machine-­‐learning.html
7. hsp://research.google.com/archive/unsupervised_icml2012.html