What Managers Need to Know about Data Science. Annie Flippo

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What Managers

Need to Know

about Data Science

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Outline

• What is data science

• Industry trends

• What is data

• The Optimal Data Scientist

• The Optimal Manager

• Topics in Data Science

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Who am I?

Annie Flippo Data Scientist Software Engineer Product Manager Database Developer

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Usage

of Data Science

Finance: fraud detecAon, score buying habits, calculate risks

Insurance: inspect driving habits, assess risks, determine premiums

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Usage

of Data Science

Biometrics: wearable devices to monitor and improve health

Digital MarkeAng: recommender systems, audience segmentaAon, retargeAng, churn predicAon

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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

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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

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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”

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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

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Because it’s a growing and exciAng ﬁeld with high

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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}

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Massive amount of data

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What is data?

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What is data?

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What is data?

Or, structured data from databases…

… what to do with all this data?

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The Data ScienAst

Can wrangle

data from

many

sources or

formats

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The Data ScienAst

do deep data

exploraAons …

and perform

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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)

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The OpAmal Data ScienAst

Is a person with deep staAsAcal and machine learning knowledge, extensive somware

engineering skills and well-‐versed in business strategy!

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The OpAmal Data ScienAst – Take 2

Personality Traits3

•  _Compulsive

•  _{Propulsive
laziness}

•  _{Drive
to
create
and
learn}

•  _{Irritable
determinaAon}

•  _{InsensiAvity
to
pain
(hmm…)}

•  _Integrity

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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

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Data ScienAsts: The Challenge of Managing

Stubbornly Autonomous Experts

4

“I no5ced … that data

scien5sts, but also sta5s5cians and top coders, oCen have

diﬃcul5es accep5ng orders from managers who don’t have technical skills

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Journey to become a DS Manager

Nate Silver on Finding a Mentor, Teaching

Yourself StaAsAcs, and Not Sesling in Your

Career

5

•  _{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

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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

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Data Sources

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Machine Learning

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Machine Learning

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Data Science Concepts

PredicAve AnalyAcs

ClassiﬁcaAon

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Topics in Cloud CompuAng

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Your Job: Provide Guidance

Tell us a data story

… about your business

Do you understand the outcome?

What is your

recommendaAon to the business?

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Gezng Started: Locally

Meetups

•  _{LA
R
users
group}

•  _{LA
Machine
Learning}

•  _{LA
Data
Warehouse,
BI
&
AnalyAcs}

•  _{LA
Big
Data
Users
Group}

Conferences:

•  _{datascience.la}

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Gezng Started: Podcasts

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Good Places to Start

Data Science for Business

by Foster Provost & Tom Fawces

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Good Places to Start

Doing Data Science

by Rachel Schus & Cathy O’Neil (mathbabe.org)

Free at

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Good Places to Start

The Art of Data Science

by Roger Peng & Elizabeth Matsui

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Get Kids Started

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Thank You!

Annie Flippo @ACﬂippo

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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