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.com
Myth #2 Data scientists are an elitebunch of precious eggheads.
Data scientists get their fingernails dirty dumping piles of data into analytical sandboxes, cleansing, and sifting through it for usefulpatterns that may or may not exist. Then, they do it all over again. Reality #2 IBMbigdatahub.com
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
Myth #3Data scientists are a nouveau fad that will soon fade.
The term “data scientist” has beenaround 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
The term “data scientist” has beenaround for years, and the various advanced analytics specialties that fall growth under n job iit are even older. Ste ady the academic been usedRecently,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
Myth #4Data scientists are all just PhD statisticians who failed to make tenure.
Many data scientists acquired their quantitative and statistical modeling skills in college, but pursued degrees in business administration, economics andengineering. They actually know about business problems. Reality #4 IBMbigdatahub.com
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
Myth #5 Data scientists are just BIspecialists with fancier titles.
Many longtime BI power users are, in fact, data scientists of a sort. They are business domain specialists whose jobs involvemultivariate analysis, forecasting,what-if modeling, and simulation. Reality #5 IBMbigdatahub.com
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 predmultivariate analysis, forecasting,andwhat-if modeling, and simulation. Reality #5 IBMbigdatahub.com
Myth #6 Data scientists aren’t reallyscientists in any meaningful sense of the word.
Statistical controls are the bedrock of true science—the coreresponsibility of the data scientist. If data scientists are confirming their findings through statistical controlsand real-world experiments, they’re scientists, plain and simple. Reality #6 IBMbigdatahub.com
Statistical controls are the bedrock of true science—the coreresponsibility of the data scientist. If True s cience data scientistsnare confirming their othing is withou findings throughvstatistical tcontrols obser ationa l dataand real-world experiments, .they’re scientists, plain and simple. Reality #6 IBMbigdatahub.com
Myth #7 Data scientists need fancy, expensive statistical powertools to get their work done.
The job of the data scientists is to look for hidden patterns. They canaccomplish 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
The job of the data scientists is to look for hidden patterns. They canaccomplish 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
Myth #8Data scientists simply pourdata into Hadoop and pullout mind-blowing insights.
The data scientist will be thefirst to tell you that Hadoop isjust another platform for deep exploration into data. Reality #8 IBMbigdatahub.com
There i n’t a The data scientistswill be the Ouija magic boardfirst to tell youich wh that Hadoop h throug is the bigjust anotherspirits sp forddeep platform ata eak to me e m exploration rintoodata. s u rtals. Reality #8 IBMbigdatahub.com
Myth #9 Data scientists are analyticsjunkies who couldn’t care less about business applications.
If you spend time with any real- world data scientist, they’ll bend your ear discussing how theytackled a specific business problem, such as reducing customer churn, targeting offers across channels, and mitigating financial risks. Reality #9 IBMbigdatahub.com
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
Myth #10Data scientists don’t have anyresponsibilities that force them out of their ivory towers.
That used to be the case. However, as next best action and real-worldexperiments become ubiquitous, the data scientist is evolving into the role that stokes, tweaks and fuels the operational engine. Reality #10 IBMbigdatahub.com
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, theexperiments- 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
For more from James Kobielus and other big data thought leaders, visit The Big Data Hub at IBMbigdatahub.com