Coming from an academic background, appending the word "science" to such a generic term as "data" could be considered almost an insult. If you think about it, it seems a meaningless concept since everything in the world can be measured today. Even for the things that apparently can't be measured like happiness or love there is a gazillion of metrics proposed to compare different countries, individuals within societies or for psychological purposes, for example. Hence everything is data, and the science of data cannot be another thing but the science of everything (!), which sounds a little bit generic, to say the less.
However, it is easy to recognise that both ideas, data and science, work quite well as a buzz word, so I guess that is why nowadays we see data science almost everywhere and in every possible field, ranging from health to sport and from finance to ecology. Therefore, it looks like in its generality is actually its greatest virtue as a concept; but also it is its greatest inconvenience, because very few people in this planet would be able to tell you with a simple sentence what a data scientist is or what she/he does. Even many data scientists and companies trying to hire data scientists usually struggle to clearly define what they expect to do or to be done in a data scientist role. Many times the term data engineer appears as an attempt to differentiate the guys who are supposed to design/implement ETL processes and dig into endless log files (those regex heroes), from those who write equations impossible to understand to the common citizen and from those who collect whatever ugly output and makes it up into a gorgeous infographic.
You could find a myriad of articles here and there in newspapers, blogs and in recruiters' websites. Usually vaguely written with few guidelines to follow. And this is precisely what I consider to be the main challenge for this blog: to provide a wide description of the roles, persons and skills, with some dives into the deeps of the technicalities of the data science ocean. A broad user guide for the intrepid student who wants to climb the career ladder from the data face of the mountain, or for the confused manager who just wants something to be done and needs the right guy who knows how to use the right tool for the job.
Obviously, that is something that I only aim to describe from my own experience, since it would be rather presumptuous even to attempt to achieve such a task by oneself. In this regard every comment, suggestion, feedback, guide, critique or correction, constructive or otherwise, would be greatly appreciated, howdy visitors!
It subsequent entries I will try to define this amazing field of study by describing the experiences I acquired over the years I have been travelling inside this world, in academia and in industry, from the more general overview to the problems different data scientists solve in their everyday workload and the common skills that would define what a data scientist is and why she/he is different from a statistician, a developer or a graphic designer. That is, to draw the big picture of what a data scientist is, or whatever they call the person who does that job.
Welcome to yet another data science blog!
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