How To Build A Successful Data Science Team - InformationWeek
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How To Build A Successful Data Science Team
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Whoopty
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Whoopty,
User Rank: Ninja
12/31/2013 | 6:33:40 AM
Good luck
Good luck to those that find themselves needing to convince higher ups that they need to take on three people to tackle one particular function!
DanielN381
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DanielN381,
User Rank: Apprentice
12/31/2013 | 9:04:45 AM
So much to learn
Interesting post, but in this day one skill is not enough for you to be a scientist. You must know how to collect data, process data, build a model, and present it in a meaningful way. 
WKash
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WKash,
User Rank: Author
12/31/2013 | 11:15:42 AM
Super Team
One would not expect to have a single person trying to master all the dimensions of security (software, infrastructure, risk management, etc.).  It takes a team.  I suspect the roles and dimension of big data are still so new, it's easy to see how organization's are latching onto the notion of a data scientist. It's a convenient way to embody the disciplines. There may be a few supermen/superwomen out there.  But most likely, the organizations that will profit from data sciences will be those with a super team.

 
RobPreston
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RobPreston,
User Rank: Author
12/31/2013 | 11:32:29 AM
Re: So much to learn
We as a society tend to throw around the words "scientist" and "science" too liberally. Colleges award bachelor of science degrees in such non-scientific fields as management and philosophy. Urban planners fancy themselves as social scientists. And don't get me started on political science--double-speak is far more of an art.
shamika
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shamika,
User Rank: Ninja
12/31/2013 | 11:50:07 AM
Re: So much to learn
This is an interesting article and one of my favorite subjects. This gives the ability to understand the data sets and to perform detailed analysis on the same.

 
shamika
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shamika,
User Rank: Ninja
12/31/2013 | 11:55:44 AM
Re: So much to learn
@DanielN381 yes you are correct and having all these will help you to master on data. 
shamika
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shamika,
User Rank: Ninja
12/31/2013 | 12:09:58 PM
Re: So much to learn
Developing algorithms and crunching numbers is also an important aspect when it comes to data scientists.
I give
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I give,
User Rank: Moderator
12/31/2013 | 7:43:48 PM
Re: So much to learn
Thanks, Rob.  I could go on and on about this.  A "Data" Scientist?  What would we expect an "Air" Scientist to know and do? How about a "Dirt" Scientist", "Atom" Scientist, "Wood" Scientist, "Word" Scientist, "Fur" Scientist (not to be confused with a "Hair" Scientist?  Which came first Data Science, or the First Data Scientist?    Heck, I doubt that there is an agreed to definition of Data, not among Data Scientists anyway.

IMHO, the very idea that a number cruncher is expected to develop consumer insights is so naive that it does littel more than show the tendency for everyone in every discipline whatsoever to assume that someone else does the work, and the "user" need know nothing, or do nothing other than be a "manager" who hires a bunch of other managers all the way down to the single person who does everything, which is then fed up the food chain to The Manager.  

So the best way to succeed is by knowing nothing, but by getting to manage the most managers you are capable of.  Just be sure that the lowest level manager has a Data Scientist working for them.  That low level manager can always replace the Data Scientist if the individual is not up to carrying the Company.
cbabcock
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cbabcock,
User Rank: Strategist
1/2/2014 | 4:21:56 PM
Specialties matter but world needs generalists
The three roles captured in the term "data scientist" perhaps should be handled by three people of different skill sets. But in an ideal world, these three people would rotate jobs within the trio every three months until each could take a stab at performing the role of the others. The person who consistently performs best in all three roles should be named the team leader. Sounds crazy, but the world will always need generalists on top of the specialists.
pcalento011
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pcalento011,
User Rank: Apprentice
1/4/2014 | 11:48:17 PM
Skills important, but context even more so.
One of the items to address re: data science isn't so much the skills or team, but the understanding of what the data all means. This is not merely an academic exercise. This is business. Patterns need to be mapped and measured into a relevance scale. Without this added step, Big Data will be just that ... more and larger data. Context. Context. Context.
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