Fri. Apr 16th, 2021

What Is Data Science For?


In the information age, where almost any device is capable of generating and recording data, and how, through data, some companies have achieved unimaginable successes, or cut costs in an unthinkable way, or increased efficiency, company managers feel urgent anxiety to take advantage of data and do data science in hyberadad.

 The concept is simple; clearly, the task is not; however, paradoxically, just the opposite prevails in the market, the idea that taking advantage of data is a matter of having a tool that will make it easy. When they come across reality, business people then seek help from data professionals who may well be systems engineers or, in the best of cases, mathematicians, who squeeze, knead, and distort the data without a particular purpose, reaching catastrophic results. 

 Data Science aims to find a way to take advantage of data. This objective has two aspects, and the first is to analyze the existing data, the second is to obtain the data that is needed to obtain this advantage.

Neither of the two previous tasks makes sense if you do not first define the advantage you want to obtain, that is, for people who dedicate themselves to mathematics beauty of an equation, of a concept, of a discipline, this site is full of those examples in which, regardless of how useful a graph may be, it is published on its own merit. And if this is the way this site is, those of the social networks Facebook, LinkedIn, etc., are much more saturated.

But that does not mean that we do not understand the concept of utility or that we cannot pursue it and obtain it. However, who has to establish it is the business user.

For example, to make a forecast based on data, many tools can be used, both technological and mathematical, that is, a simple average can become a good forecast depending on the business case in question, or on the contrary, an average can give us the values ​​that no member of the population has (for example, the average height of a population is, generally, the height that nobody has, some above and others below, but the exact mean, nobody), in the same way, a Bar graph can be sufficient to understand a trend or find a correlation or more complicated regression models can be used.

Recently, machine learning capable of making forecasts using several regression models at the same time has gained relevance, and that can be “trained” to offer increasingly precise and accurate results.

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