What Does A “Data Scientist” Do And Why Is It Such A Sexy Profession?
Much has been written about what is going to be the sexiest profession of the 21st century. What comes to express that idea is the importance that a data scientist will have in an era of ubiquitous data, storage, processing, and transport costs practically zero and ongoing digitization. The modern practice of data analysis, which is popularly and often erroneously known as “Big Data,” is based on what is “Data Science” or “Data Science.”
A professional who, combining knowledge of mathematics, statistics, and programming, is in charge of analyzing large volumes of data. Unlike traditional statistics that used samples, the data scientist applies his statistical knowledge to solve business problems by applying new technologies, which allow calculations that could not be performed until now.
And it has been gaining in popularity in recent years due mainly to the development of the most technological part. Big Data technologies are beginning to enable companies to adopt them and begin to value data analysis in their day today. But that’s when they realize they need more than just technology.
Statistics for the construction of analytical models, mathematics for the formulation of problems and their coded expression for machines, domain knowledge (knowing the company’s functional area that wants to adopt it, the sector of economic activity, etc.), become equally fundamental.
But if this is so sexy, what does the data scientist do? And above all, what does this have to do with Big Data and Business Intelligence.
As can be seen, it is an aggregation of three disciplines that must be well understood in this new paradigm that Big Data has brought:
- Hacking skills or digital skills with computational thinking: I know that I lose a lot of the meaning of what the “Hacking Skills” expresses when translating it into Spanish. But I think it is also well understood what ‘digital skills’ mean. We are in a time in which constant «algorithmization» of what surrounds us, the computational thinking that there are already countries that have introduced since preschool, makes that digital skills do not pass only by «knowledge of Office automation» or «information systems.» This is about more than looking at what computers do, how they process data, and how they use it to conclude. I call this «Computational Thinking» as a (bad) translation of «Computation thinking, » which, together with digital skills (understanding what digital tools do and putting it into practice), seem fundamental to me.
- Statistics and Mathematics: First of all, statistics, which is a critical problem-solving tool. It gives us work tools of enormous value for those of us who work with company problems. And mathematics, oh, what about formal science par excellence, which, following logical reasoning, allows us to study properties and relationships between the variables that will be part of our problem. While mathematics has come to be known as the exact science, we more like to play with confidence intervals and uncertainty in statistics. But, due to their peculiarities, they nurture each other. It means that to build analytical models that allow us to solve the problems that companies and organizations pose to us, we need both.
- Knowledge of the domain: to design and develop massive data analysis to different use cases and applications, it is necessary to know the context. The problems must be posed according to these characteristics. As I always say, this Big Data thing is more a matter of planting problems than anything else, so knowing how to ask the right questions with people who know the application domain well is essential. This is why I usually refer to as many Big Data projects as there are companies. Each project is a world, so when someone tells you about their project, then relativize it to your needs.
These three questions (informatics and computation, statistical methods, and application/domain areas) were also cited in data science training institute in Hyderabad. Therefore, it is not a new conception.
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