1) The document discusses 10 myths about data scientists and provides realities to counter each myth.
2) Some myths include claims that data scientists are mythical beings, elitist academics, or a fading trend. However, the realities note data science requires hands-on work with data and has experienced steady growth.
3) Other myths suggest data scientists are just statisticians or BI specialists, but the realities indicate data scientists come from varied backgrounds and tackle business problems through experimentation and analysis.
2. James Kobielus shoots down
10 myths about Data Scientists
“Data Scientists: Myths and Mathemagical Powers,”
James Kobielus, Thinking Inside the Box, June 29, 2012
6. Myth #2
Data scientists are an elite
bunch of precious eggheads.
7. 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 IBMbigdatahub.com
8. Data scientists get their fingernails
It’s ofte
nu piles n mind- into
dirty dumpingm
bingly
of data
analytical sandboxes, detailed
grunt cleansing,
the sp work,
ort of a n useful
and sifting through it for ot
rm
data por may chairexist.
patterns that may hiloso not
phers.
Then, they do it all over again.
Reality #2 IBMbigdatahub.com
10. 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
11. The term “data scientist” has been
around for years, and the various
advanced analytics specialties
that fall growth
under n job
iit are even older.
Ste ady the academic been used
Recently,and term has.
st i ngs iable
unden
lithe convergence of disciplines
in ricula is
c ur fad.
that Thi s is no
have become super-hot.
Reality #3 IBMbigdatahub.com
13. 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.
Reality #4 IBMbigdatahub.com
14. M ny
Many dataascientists acquired
data s
c entis
you’ll and istatistical
their quantitativenco
e ts
the wo unter
modeling skills rking
in college, but in
are bu world
sine in business
pursued degreesss dom
sp e c ia ain
administration, economics and
l i st s !
engineering. They actually know
about business problems.
Reality #4 IBMbigdatahub.com
15. Myth #5
Data scientists are just BI
specialists with fancier titles.
16. 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.
Reality #5 IBMbigdatahub.com
17. nt
meBI power users
Many develop ey
er longtime
Care i f th
tdata scientists of a
are,yintall ou speed
a s fact, to
m p
y uare business domain
sort.t They e Hadoop
do n’ sta ik
on to ictiv
specialists e mod e ing.
pics l whose ljobs involve
pred
multivariate analysis, forecasting,
and
what-if modeling, and simulation.
Reality #5 IBMbigdatahub.com
18. Myth #6
Data scientists aren’t really
scientists in any meaningful
sense of the word.
19. 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
20. Statistical controls are the
bedrock of true science—the core
responsibility of the data scientist. If
True s
cience
data scientistsnare confirming their
othing is
withou
findings throughvstatistical tcontrols
obser
ationa
l data
and real-world experiments, .they’re
scientists, plain and simple.
Reality #6 IBMbigdatahub.com
21. Myth #7
Data scientists need fancy,
expensive statistical power
tools to get their work done.
22. 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 IBMbigdatahub.com
23. The job of the data scientists is to
look for hidden patterns. They can
accomplish rthisfo ory r cost- user-friendly
a ket through
The m explorat
visualization tools, y
ctive n search-driven
effe as ma g
BI tools tools h cludin
BI and other approaches that
don’t end ors, ina deep mastery of
v require gnos.
I BM C o
statistical analysis.
Reality #7 IBMbigdatahub.com
25. 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
26. There
i n’t a
The data scientistswill be the
Ouija magic
board
first to tell youich
wh that Hadoop h
throug is
the big
just anotherspirits sp forddeep
platform ata
eak to
me e m
exploration rintoodata. s u
rtals.
Reality #8 IBMbigdatahub.com
27. Myth #9
Data scientists are analytics
junkies who couldn’t care less
about business applications.
28. 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.
Reality #9 IBMbigdatahub.com
29. If you spend time withnany real-
e t i st s
ta sci
world data ost da rds. They bend
Mscientist, they’ll
are n’t ne
your ear discussing how egarthey d
e ople r ingo
kn ow pbusinessl problem,
tackled a specific big data on.
al l th is g jarg churn,
u si n
such as reducing fcustomer
as con
targeting offers across channels,
and mitigating financial risks.
Reality #9 IBMbigdatahub.com
30. Myth #10
Data scientists don’t have any
responsibilities that force them
out of their ivory towers.
31. 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.
Reality #10 IBMbigdatahub.com
32. That used to be the case. However,
Da best action and real-world
as nextta scien
analy tists te
s the
tic become t ubiquitous, the
experiments- cent
at the ric mo
dels
data scientistrt oevolving into the
hea is
busine f agile
ss pro tweaks and fuels
role that stokes,cess
es.
the operational engine.
Reality #10 IBMbigdatahub.com
33. For more from James Kobielus and
other big data thought leaders,
visit The Big Data Hub at
IBMbigdatahub.com