Blog

Your a Data... What?

Troy Sadkowsky - Tuesday, June 26, 2012

Let’s face it, data science is hard to explain.  

It happens to me all the time. I'm there at a networking or social event, everyone is chatting away with general chit-chat, I bring up the topic of data science and it's like pulling the power plug on the music player.  

Now this might be largely due to my lack of experience in conversing in general chit-chat, however the other factors at play here are that data science is new, data science sounds intellectual and data science doesn’t fit into any of the existing main industries.

We all have an initial knee jerk to anything new, this comes from our primal instincts and the ancestorial wiring of our brain.  Back when we were all cavemen, anything new was cause for alarm, because they hadn’t performed their is it safe test.  It is only after they’d performed their “is it safe” test (usually done with a series of poking and proding) that they’d know whether to be happy or not about the thing that is new.  So because data science is so new we are cautious about it and in the first instance things to be cautious about are best avoided.

If we do get over the first initial knee-jerk and they haven't gone to sort their socks, the next challenge is that the preconcpetions general society has about the word “scientist”.  Media and general communication depict the scientist as a highly intelectual person.  A person standing there in a lab coat straining their brain to quantitatively measure the relationships between physical phenonemon.  Now whenever we meet someone new what happens is that first we run the “is it safe” test, and then the next thing that we do is compare the person we are talking with, with ourselves.  And if we find out that this person is “better than me” it triggers a whole lot of other primal instincts using ancestorial wires in the brain which generally lead to the feeling of being annoyed.

Leading on from the “better than me” analysis, if we do get past this, there is still another challenge ahead.  The following analysis is on the context of what is being said and is the “relevant to me” analysis.  We live in such a busy world that we don’t have time to listen and talk about things that are seemingly not “relevant to me”.  The difficulty arises when you see that data science deosn’t have its own domain to belong to.  It crosses the boundries of Information and Technology, Business and Science.  So unless the data scientist role can be put into context for the person you are talking to, their “relevant to me” analysis will usually result in them wanting to talk about something else.

So, where does this leave us data scientists when wanting to tell everyone we meet and yell from the talest building that “I am a Data Scientist!”.  

Don’t even try!

Now of course it wont be this way for ever, in time, we will all come to know what a data scientist is and these initial challenges will be greatly reduced.  And even today, there will be situations and environments that provide an exception to this rule.  So at the moment it is best to call yourself something other than a “data scientist” and just adopt the data science methodology on the sly.  If your a computer scientist, entrepreneur, database administrator, software engineer, data curator, be just that and use the data science way of working to bring fulfilment and passion into being that.  

One more thing.

If you are looking to branch out and do more networking to expand into new market opportunities, you may find the following guide on how to get your personal web presence into gear.

Here is how I built this wordpress site from scratch (including the webserver) to kick off my web presence as a Data Warehouse Architect.
 
  1. If you don't have it grab Virtual Box from here >>
  2. If you don't have one lying around grab a Ubuntu Distro from here >>
  3. Run up your new Ubuntu server VM (Instructions can be found here >>)
  4. Configure as a LAMP stack (Instructions can be found here >>, but basically it is apt-get install lamp-server^) 
  5. Configure WordPress (Instructions can be found here >>, but basically it is apt-get install wordpress and read the readmes)
  6. Buy a domain name and point it to your IP (I used Net Registery)
  7. Mine content and create your news page (I used Paper.li
  8. Publish and embed your CV (I used Google Docs)
  9. Populate your blog with quality, unique content
  10. Get known as the expert in this space (Still under development, but I'll keep you posted)


Check out the end product here >>

Btw, the total work effort required was 4 hours and total cost was AU$39.90, and if you want to skip the first 5 steps let me know an I can give you the VM vdi file.

Talk again soon,

Troy.

Trackback Link
http://www.datascientists.com/BlogRetrieve.aspx?BlogID=4599&PostID=300741&A=Trackback
Trackbacks
Post has no trackbacks.

Your a Data... What?

Troy Sadkowsky - Tuesday, June 26, 2012

Let’s face it, data science is hard to explain.  

It happens to me all the time. I'm there at a networking or social event, everyone is chatting away with general chit-chat, I bring up the topic of data science and it's like pulling the power plug on the music player.  

Now this might be largely due to my lack of experience in conversing in general chit-chat, however the other factors at play here are that data science is new, data science sounds intellectual and data science doesn’t fit into any of the existing main industries.

We all have an initial knee jerk to anything new, this comes from our primal instincts and the ancestorial wiring of our brain.  Back when we were all cavemen, anything new was cause for alarm, because they hadn’t performed their is it safe test.  It is only after they’d performed their “is it safe” test (usually done with a series of poking and proding) that they’d know whether to be happy or not about the thing that is new.  So because data science is so new we are cautious about it and in the first instance things to be cautious about are best avoided.

If we do get over the first initial knee-jerk and they haven't gone to sort their socks, the next challenge is that the preconcpetions general society has about the word “scientist”.  Media and general communication depict the scientist as a highly intelectual person.  A person standing there in a lab coat straining their brain to quantitatively measure the relationships between physical phenonemon.  Now whenever we meet someone new what happens is that first we run the “is it safe” test, and then the next thing that we do is compare the person we are talking with, with ourselves.  And if we find out that this person is “better than me” it triggers a whole lot of other primal instincts using ancestorial wires in the brain which generally lead to the feeling of being annoyed.

Leading on from the “better than me” analysis, if we do get past this, there is still another challenge ahead.  The following analysis is on the context of what is being said and is the “relevant to me” analysis.  We live in such a busy world that we don’t have time to listen and talk about things that are seemingly not “relevant to me”.  The difficulty arises when you see that data science deosn’t have its own domain to belong to.  It crosses the boundries of Information and Technology, Business and Science.  So unless the data scientist role can be put into context for the person you are talking to, their “relevant to me” analysis will usually result in them wanting to talk about something else.

So, where does this leave us data scientists when wanting to tell everyone we meet and yell from the talest building that “I am a Data Scientist!”.  

Don’t even try!

Now of course it wont be this way for ever, in time, we will all come to know what a data scientist is and these initial challenges will be greatly reduced.  And even today, there will be situations and environments that provide an exception to this rule.  So at the moment it is best to call yourself something other than a “data scientist” and just adopt the data science methodology on the sly.  If your a computer scientist, entrepreneur, database administrator, software engineer, data curator, be just that and use the data science way of working to bring fulfilment and passion into being that.  

One more thing.

If you are looking to branch out and do more networking to expand into new market opportunities, you may find the following guide on how to get your personal web presence into gear.

Here is how I built this wordpress site from scratch (including the webserver) to kick off my web presence as a Data Warehouse Architect.
 
  1. If you don't have it grab Virtual Box from here >>
  2. If you don't have one lying around grab a Ubuntu Distro from here >>
  3. Run up your new Ubuntu server VM (Instructions can be found here >>)
  4. Configure as a LAMP stack (Instructions can be found here >>, but basically it is apt-get install lamp-server^) 
  5. Configure WordPress (Instructions can be found here >>, but basically it is apt-get install wordpress and read the readmes)
  6. Buy a domain name and point it to your IP (I used Net Registery)
  7. Mine content and create your news page (I used Paper.li
  8. Publish and embed your CV (I used Google Docs)
  9. Populate your blog with quality, unique content
  10. Get known as the expert in this space (Still under development, but I'll keep you posted)


Check out the end product here >>

Btw, the total work effort required was 4 hours and total cost was AU$39.90, and if you want to skip the first 5 steps let me know an I can give you the VM vdi file.

Talk again soon,

Troy.

Trackback Link
http://www.datascientists.com/BlogRetrieve.aspx?BlogID=4599&PostID=300741&A=Trackback
Trackbacks
Post has no trackbacks.