September 28, 2015 at 11:21 pm
I know and work with people who would be considered "true" data scientists as well as some aspiring data scientists. The consensus seems to be, based on what I've read and among people that I've talked to with post graduate degrees in things like Predictive Analytics is that a Data Scientist:
1) Has a post graduate degree in Math, Statistics, Economics, Finance or some other math-heavy field of study
2) Has solid expertise in BI or Big Data... E.g. the MS BI Stack or AWS. They may be a highly certified or just have several years of solid experience
3) Has expertise in a specific business subject area... Banking, Finance, Advertising, etc...
Such a person could understands a specific business, software and has the math skills to develop a solution that helps them turn mad amounts of data into actionable intelligence. That's my understanding of what a data scientist is and what they do. I'm a BI who works in big data but don't see myself going back to school to become a data scientist.
What I've seen is, because the person I just described is as rare as a unicorn many companies will opt for a data science team that consists of an analytics person(s), a BI person(s) and visual experience person(s) (e.g. a Tableau guru) and they get their expertise about the business from one or more people who are SME's in the business about that business.
Again, this is based on my conversations with people and what I've read. I'm interested in seeing how Big Data and data science evolves.
-- Itzik Ben-Gan 2001
September 29, 2015 at 12:00 am
Comments posted to this topic are about the item Are you a Data Scientist?
September 29, 2015 at 4:31 am
No, Steve, I wasn't a data scientist, or any of those BS titles. I was a true programmer and SQL developer, the guy who actually made work the high-falutin' ideas all the 'business analysts' and 'managers' came up with. Developing good efficient accurate code that worked on skillfully conceived and constructed data bases was a true art form, not the leftover tasks after the big guys did their thing. In a 42-year career, I spent 11 years as a manager and then went back to the real world of making things happen. Never missed management at all, and was happy to be a skilled SQL developer who could git 'er done.
Rick
Disaster Recovery = Backup ( Backup ( Your Backup ) )
September 29, 2015 at 6:01 am
Interesting editorial. I'm always amazed and amused at labels and 'titles' people have. My official title (on my business card) is 'database analyst'. (I did not give myself that title). I don't even know what my title is supposed to mean, and the people who came up with that title know much less about databases in general than I do!
I just happen to be one of the few in our office who knows a bit of SQL and can generate simple reports, so I guess that makes me a 'database analyst'. (?) It's a far cry from a data scientist!
September 29, 2015 at 6:15 am
I'm not so sure. I think a 'data scientist' WOULD in fact be one who gets people the data they want and need. It sounds like you do that, so in the true sense, you probably ARE the best example. Just keep making yourself better and increasing your skills.
Rick
Disaster Recovery = Backup ( Backup ( Your Backup ) )
September 29, 2015 at 6:35 am
Interesting comments Steve. I too, have seen the "big data" bandwagon that so many have jumped onto recently as though it's something completely new. There are relatively new tools for sure (Hadoop, etc.) but the problems have been with us for some time. Terms like "big data" and "data scientist" are shiny and folks are pounding the square pegs into the round holes just to be able to use those shiny new terms whether they fit or not.
Such generic terms as "big data" certainly doesn't imply only one solution or one tool to work with it and the knee-jerk reaction to always pair "big data" with NO-SQL is about as accurate as installing screws with a hammer. Sure, Hadoop is an awesome tool that when used properly produces outstanding results. The same is true for SQL Server. Sometimes even, these two tools can be used in conjunction with each other for spectacular results. It takes careful planning and an excellent understanding of the strengths of each tool to apply them properly to each unique situation.
We shouldn't be like Maslov's dogs and just automatically say "NO-SQL" whenever we hear "big data" and also assume that implies "data scientist". If we do, then we're just falling prey to marketing hype and the sway of an ill-informed public opinion.
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September 29, 2015 at 6:41 am
What's wrong with the world today? If you say "I'm a Data Scientist", you will probably be kidnapped from the street.
September 29, 2015 at 6:45 am
I like Alan's definition of Data Scientist, although I would offer that the post graduate degree might be overkill.
September 29, 2015 at 10:31 am
I prefer the term Data Analyst or the more informal Data Dude for myself.
Data Scientists are 1 percent real unicorns and 99 percent common hucksters.
September 29, 2015 at 10:44 am
To be a true Data Scientist, you probably need a solid academic background in data analysis as well as deep knowledge of whatever domain you're working in. I'd also expect that person to work almost exclusively in the arena of data analysis. It's not just a programmer who has read a couple of O'Reilly books on the topic.
"Do not seek to follow in the footsteps of the wise. Instead, seek what they sought." - Matsuo Basho
September 29, 2015 at 11:27 am
Eric M Russell (9/29/2015)
To be a true Data Scientist, you probably need a solid academic background in data analysis as well as deep knowledge of whatever domain you're working in. I'd also expect that person to work almost exclusively in the arena of data analysis. It's not just a programmer who has read a couple of O'Reilly books on the topic.
I think you need solid knowledge of the academics, but not necessarily a degree.
September 29, 2015 at 1:18 pm
Steve Jones - SSC Editor (9/29/2015)
Eric M Russell (9/29/2015)
To be a true Data Scientist, you probably need a solid academic background in data analysis as well as deep knowledge of whatever domain you're working in. I'd also expect that person to work almost exclusively in the arena of data analysis. It's not just a programmer who has read a couple of O'Reilly books on the topic.I think you need solid knowledge of the academics, but not necessarily a degree.
Yes, their foundation can come from book learning, mentoring, and years of experience. However, they would have to be very well read on books written by folks who do have specialized degrees on the topic, and they would have to perform their work with scientific discipline.
"Do not seek to follow in the footsteps of the wise. Instead, seek what they sought." - Matsuo Basho
September 29, 2015 at 2:56 pm
I view analyst and scientist differently.
Analyst don't necessarily need to know anything about statistics, modeling and so forth to their job in most of the companies I've worked for. But, that should not be a road block for them to expand in more advance concepts for them to do their job better. Most of the so-called data scientist I work with started out as low level analyst. The deeper they went down that rabbit hole with statistics, machine learning and so forth, the more justification they had to switch from analyst to scientist.
In my area, I very much fill the role of the data engineer who works with the data scientist. I fill the technology void the scientist may or may not have. The scientist fills the statistic void that I may or may not have. While you can find people who can do both, they are extremely rare and extremely expensive. But as the same with the analyst, you should not let those roles define you. I work on improving my statistics and probability concepts where the data scientist works on his technology skills.
When I sent this article off to my co-worker who works as the supervisor data scientist, he agreed with the 3 areas covered in the article. Those are the areas he emphasis the most to his leaders. He also mentioned that advancing in all 3 is the uphill battle they all face to be good at along with trying to expand in other areas that may help them do their jobs better.
September 29, 2015 at 3:46 pm
Well, I.T. hijacked and cheapened the 'Engineer' moniker a long time ago so why not 'Scientist'? Most of the I.T. "Engineers" I've worked in the past are no more engineers than I'm an astronaut. I've actually worked alongside real engineers in the nuclear energy field and they have this affection for methodology that is far too rarely seen anymore in I.T. and I doubt the newly minted "scientists" will raise the bar much. I have to admit I'm a little jealous. "Scientist" is so much more sexier than "Administrator". It's like "Sage" is to "Librarian". CERN probably has some real Data Scientists.
September 30, 2015 at 1:27 am
I like Alan's definition.
There is a difference between a data scientist and the skills required in a data science department.
No matter how predictive the models or actionable the insight if they contradict received wisdom in the business it will all go to waste.
There is a role for someone who can make a compelling case that business people can and will accept for stuff that is counterintuitive at first glance but in hindsight turns out to be screechingly obvious. The trick is to steer people to the point where you are looking back in hindsight.
That role also needs to market the value of failure. Learning what doesn't work and why is as important as finding something that does. Science is about experimentation and experimentation carrys the probability of failure
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