Let me address another question that I keep getting asked- what should I do to enter the world of Data Scientist- not a wonder given all the buzz around this space- and pitched as among the top 5 jobs of the 21st century
Before I get started, let me mention upfront that I don't operate in Silicon Valley and my understanding of Data Science is not the frothy ideas from the start-ups but more embedded in how traditional organizations that have a more critical view of returns and don't easily buy in the hype. I see a much more stable role for Data Science in reinventing business processes and bringing a new culture to make decisions.
Also, I don't have jobs to offer and am not in the business of providing leads for consulting gigs- the best I can do is to share guidance around how you can better prepare yourself in the space. I would prefer to answer via comments below so it becomes a shared discussion- but if you wish, can handle offline mails at raghu2222in at gmail dot com [might take 1-2 weeks to get back and no promises]
First, pls check out my thoughts on key attributes for making a good data science career
http://bizmachinelearning.blogspot.in/2015/04/what-makes-for-ideal-data-scientist.html
To me, there are different paths to achieving the chosen career, but some things are a core need irrespective of role-context
a) strong consultative skills- communication, quick understanding of context
b) strong math skills- you might not understand all the details, but atleast appreciate the highlights to contribute in discussions
c) technical context- every role might have a varying technical architecture (statistical packages, Big Data technologies etc)
I would suggest taking in college or online programs to pick up on the math skills and technical knowledge- an option might be to work on the high quality MOOC courses from Standord, Caltech and many others. As in many areas, I would focus more on learning by experimenting than getting knee-deep in theoretical concepts- the good news is that most of the platforms, analytical packages and rich data sets are available for free.
The critical consultative capability and business context comes with experience and you might even be able to apply some of the new insights and tools you have picked up without formally changing your role. Typically we bleed in freshers through blended teams whereby the smart talent play along in the initial stages carrying out the data acquisition and model implementation tasks... and over 6-9 months start playing the more interesting conversations around definition of the business problem to be solved, articulating recommendations and metrics to track progress
Let me know what you think
Before I get started, let me mention upfront that I don't operate in Silicon Valley and my understanding of Data Science is not the frothy ideas from the start-ups but more embedded in how traditional organizations that have a more critical view of returns and don't easily buy in the hype. I see a much more stable role for Data Science in reinventing business processes and bringing a new culture to make decisions.
Also, I don't have jobs to offer and am not in the business of providing leads for consulting gigs- the best I can do is to share guidance around how you can better prepare yourself in the space. I would prefer to answer via comments below so it becomes a shared discussion- but if you wish, can handle offline mails at raghu2222in at gmail dot com [might take 1-2 weeks to get back and no promises]
First, pls check out my thoughts on key attributes for making a good data science career
http://bizmachinelearning.blogspot.in/2015/04/what-makes-for-ideal-data-scientist.html
To me, there are different paths to achieving the chosen career, but some things are a core need irrespective of role-context
a) strong consultative skills- communication, quick understanding of context
b) strong math skills- you might not understand all the details, but atleast appreciate the highlights to contribute in discussions
c) technical context- every role might have a varying technical architecture (statistical packages, Big Data technologies etc)
I would suggest taking in college or online programs to pick up on the math skills and technical knowledge- an option might be to work on the high quality MOOC courses from Standord, Caltech and many others. As in many areas, I would focus more on learning by experimenting than getting knee-deep in theoretical concepts- the good news is that most of the platforms, analytical packages and rich data sets are available for free.
The critical consultative capability and business context comes with experience and you might even be able to apply some of the new insights and tools you have picked up without formally changing your role. Typically we bleed in freshers through blended teams whereby the smart talent play along in the initial stages carrying out the data acquisition and model implementation tasks... and over 6-9 months start playing the more interesting conversations around definition of the business problem to be solved, articulating recommendations and metrics to track progress
Let me know what you think
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