myths and mathemagical superpowers of data scientists
DESCRIPTION
Some people think data scientists are mythical beings, like unicorns, or they are some sort of nouveau fad that will quickly fade. Not true, says IBM big data evangelist James Kobielus. In this engaging presentation, with artwork created by Angela Tuminello, Kobielus debunks 10 myths about data scientists and their role in analytics and big data. You might also want to read the full blog by Kobielus that spawned this presentation: "Data Scientists: Myths and Mathemagical Superpowers" - http://ibm.co/PqF7Jn For more information, visit http://www.ibmbigdatahub.comTRANSCRIPT
Data Scientists:Myths & Mathemagical Powers
James Kobielus
James Kobielus shoots down10 myths about Data Scientists
“Data Scientists: Myths and Mathemagical Powers,” James Kobielus, Thinking Inside the Box, June 29, 2012
Data scientists are mythical beings, like the unicorns.
Myth #1
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Data scientists are an elite bunch of precious eggheads.
Myth #2
Reality #2
Data scientists get their fingernails dirty dumping piles of data into analytical sandboxes, cleansing, and sifting through it for useful
patterns that may or may not exist. Then, they do it all over again.
IBMbigdatahub.com
Data scientists get their fingernails dirty dumping piles of data into analytical sandboxes, cleansing, and sifting through it for useful
patterns that may or may not exist. Then, they do it all over again.
Reality #2
It’s often mind-numbingly detailed grunt work, not the sport of armchair data philosophers.
IBMbigdatahub.com
Data scientists are a nouveau fad that will soon fade.
Myth #3
The term “data scientist” has been around for years, and the various
advanced analytics specialties that fall under it are even older.
Recently, the term has been used in the convergence of disciplines
that have become super-hot.
Reality #3 IBMbigdatahub.com
Reality #3
The term “data scientist” has been around for years, and the various
advanced analytics specialties that fall under it are even older.
Recently, the term has been used in the convergence of disciplines
that have become super-hot.
Steady growth in job
listings and academic
curricula is undeniable.
This is no fad.
IBMbigdatahub.com
Data scientists are all just PhD statisticians who
failed to make tenure.
Myth #4
Reality #4
Many data scientists acquired their quantitative and statistical modeling skills in college, but pursued degrees in business
administration, economics and engineering. They actually know
about business problems.
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Reality #4
Many data scientists acquired their quantitative and statistical modeling skills in college, but pursued degrees in business
administration, economics and engineering. They actually know
about business problems.
Many data scientists you’ll encounter in the working world are business domain specialists!
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Data scientists are just BI specialists with fancier titles.
Myth #5
Reality #5
Many longtime BI power users are, in fact, data scientists of a sort. They are business domain specialists whose jobs involve
multivariate analysis, forecasting, what-if modeling, and simulation.
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Reality #5
Many longtime BI power users are, in fact, data scientists of a sort. They are business domain specialists whose jobs involve
multivariate analysis, forecasting, what-if modeling, and simulation.
Career development
may stall out if they
don’t stay up to speed
on topics like Hadoop
and predictive modeling.
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Data scientists aren’t really scientists in any meaningful
sense of the word.
Myth #6
Statistical controls are the bedrock of true science—the core
responsibility of the data scientist. If data scientists are confirming their findings through statistical controls and real-world experiments, they’re
scientists, plain and simple.
Reality #6 IBMbigdatahub.com
Statistical controls are the bedrock of true science—the core
responsibility of the data scientist. If data scientists are confirming their findings through statistical controls and real-world experiments, they’re
scientists, plain and simple.
Reality #6
True science is nothing without observational data.
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Data scientists need fancy, expensive statistical power tools to get their work done.
Myth #7
Reality #7
The job of the data scientists is to look for hidden patterns. They can
accomplish this through user-friendly visualization tools, search-driven
BI tools and other approaches that don’t require a deep mastery of
statistical analysis.
IBMbigdatahub.com
The job of the data scientists is to look for hidden patterns. They can
accomplish this through user-friendly visualization tools, search-driven
BI tools and other approaches that don’t require a deep mastery of
statistical analysis.
Reality #7
The market for cost-
effective exploratory
BI tools has many
vendors, including
IBM Cognos.
IBMbigdatahub.com
Data scientists simply pour data into Hadoop and pullout mind-blowing insights.
Myth #8
The data scientist will be the first to tell you that Hadoop is just another platform for deep
exploration into data.
Reality #8 IBMbigdatahub.com
Reality #8
The data scientist will be the first to tell you that Hadoop is just another platform for deep
exploration into data.
There isn’t a magic Ouija board through which the big data spirits speak to us mere mortals.
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Data scientists are analytics junkies who couldn’t care less about business applications.
Myth #9
Reality #9
If you spend time with any real-world data scientist, they’ll bend
your ear discussing how they tackled a specific business problem, such as reducing customer churn, targeting offers across channels, and mitigating financial risks.
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Reality #9
If you spend time with any real-world data scientist, they’ll bend
your ear discussing how they tackled a specific business problem, such as reducing customer churn, targeting offers across channels, and mitigating financial risks.
Most data scientists
aren’t nerds. They
know people regard
all this big data lingo
as confusing jargon.
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Data scientists don’t have anyresponsibilities that force them
out of their ivory towers.
Myth #10
Reality #10
That used to be the case. However, as next best action and real-world
experiments become ubiquitous, the data scientist is evolving into the role that stokes, tweaks and fuels
the operational engine.
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Reality #10
That used to be the case. However, as next best action and real-world
experiments become ubiquitous, the data scientist is evolving into the role that stokes, tweaks and fuels
the operational engine.
Data scientists test the analytic-centric models
at the heart of agile business processes.
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For more from James Kobielus and other big data thought leaders,
visit The Big Data Hub atIBMbigdatahub.com