The months of May and June were very insightful. I committed myself to do the bios of Research Orientated Data Scientist and Data Scientist turning Entrepreneurs. While, some of the profiles were really interesting, others were a bit off the pitch for me due to the engineering background.
However I managed to get those bios done and ended up with drawing the contours of my theme as a Data Scientist :
“Using data while inputting energy and thinking, and delivering insights and enlightenment in various fields to be understood and used by everybody!”
Do you remember this article : EP9 – Data Scientists rocking the house?
While writing this article in April; I was pondering how come I want to be a Data Scientist without having a theme? So I initiated some research and decided that I may get a glimpse of my theme by looking inside the work of contemporary Data Scientist.
Hence; I divided them into 3 groups. The two mains are: Research Orientated Data Scientists and Data Scientist turning Entrepreneurs. The other group is what i may label as others (I will be doing their bio later on as separate analysis).
Research Orientated Data Scientists
This section was a new thing for me; as I am not a physics or computer programming addict (for the time being). So getting to understand the jobs of: Geoffrey E. Hinton, Yann Lecun, Yoshua Bengio, Jurgen Schmidhuber, Alex “Sandy” Pentland, Peter Norvig, Corinna Cortes and Michael I. Jordan was the heck of a challenge for me.
Nevertheless; I managed to find an inspiring figure in this group.
The latter was A.Pentland.
His work concerning Social Physics got me pondering a lot. The fact that crowds behavior could be measured and predicted via Mathematics and Physics got me really excited. Being initially keen to learn about sociology, Pentland’s work allowed me to lay down one of the corner stones of my theme.
Data Scientist turning Entrepreneurs
On the other hand, this section was really enticing for me! Initially being a “Business & Competitive Intelligence Post-Graduate”, business related questions always get me “on fleek”. Hence; getting through the paths of: Andrew Ng, Daphne Koller, Hilary Mason, Sebastian Thrun, Jeff Hammerbacher, Jeremy Achin and Carla Gentry was a really refreshing and inspiring.
However some profiles although being entrepreneurs tend to be more interested in software development rather than data analytics. Hence; only 3 Data Scientists of this section got me triggered: J.Hammerbacher, H.Manson, J.Achin and C.Gentry.
Those 4 do taught me a log in their approach of data and in they methodology. However to resume what I learned from them; I will simply quote their most insightful caption:
“The best minds of my generation are thinking about how to make people click ads. That sucks.” – J.Hammerbacher
“Simply: I do beautiful things with Data” – H.Manson
“I saw a lot of people on LinkedIn changing their titles to data scientists — like, rapidly. But it’s clear that some of these people are not data scientists and can’t actually do the job.” – J.Achin
“Social media without a purpose is like posting a billboard with your logo but no brand message or call to action.” C.Gentry
In simple words; those 4 laid down the remaining contours of my theme. Data is for sure the place to be and indeed a great field to grow in. However, we should work to achieve the best from the available data and not be on basic, not to say worthless analysis.
It is a matter of purpose and delivery. Let us say that it is like playing with Lego the aim is to have fun while delivering value added structures. I do consider Data the same way. I just wanna get fun while mastering new tools, learning new methodologies, observing wider data-sets and delivery valuable insights to people while making it easy to understand and to communicate in order to achieve sustainable positive changes.
A.Pentland got me thinking about large groups analysis via data measured through mathematics and physics related formulas.
J.Hammerbacher helped me to not limit myself.
H.Mason showed me that data can be fun and beautiful.
J.Achin allowed me to define the learning curve and relate to what a Data Scientist is really made of.
C.Gentry dropped the kind reminded that doing analysis without a real purpose is like a useless communication.
Bottom line; I think that I want to be a Data Scientist behaving like a Data Alchemist. Using data while inputting energy and thinking, and delivering insights and enlightenment in various fields to be understood and used by everybody!