What is Data Science?

2020-11-16

Welcome to the sexiest profession of the 21st century ! Don't worry, we don't say it ourselves and we don't think we are sexy either... this is simply how "The New York Times" defined the Data Scientist profession, one of the pillars of Data Science.

If you still don't know what Data Science is, and you happen to surf the internet you will find definitions such as "It is the discipline of combining the knowledge of mathematics and probability, together with the power of machine learning by computers, and business knowledge". You can also read that "A data scientist is a statistician who programs or a programmer who performs statistics". There are many definitions and there is no absolute one, but in order not to bore you with being, essence, nothingness and eternity, here is a summary:

Data Science is the set of methodologies of: taking data, applying statistical technology to convert it into value and using that new information to solve business and everyday life problems.

Not quite clear? Let's take it one step at a time. It is considered a set of methodologies. Every Data Science project follows a series of steps in order to obtain valuable conclusions from which to make a decision.

The first step of these methods involves taking data, i.e. collecting evidence. But from where? Is it necessary to have a well-structured database? Is it a necessary condition to have large amounts of data piled up in order to apply Data Science? Absolutely not.

In today's world there is a shortage of many things, except data. Data is everywhere and disguised in a thousand different forms. Every Like, click, email, credit card swipe or tweet is a new piece of data. It can then be used to better describe the present or better predict the future.

If we have data on what happened in the past, it is very likely that there is some pattern or behavior in that data that may be repeated in the future.

Often these data are not visible to humans, and that is where "statistical technology" comes into play. It helps us to detect behaviors and thus predict future events.

Statistical technology refers to the use of algorithms. These algorithms are fed with data, learning hidden patterns and providing as output a series of predictions. In addition, we can use statistics to mathematically evaluate the probability of our predictions, thus clarifying our level of uncertainty.

Although, in Data Science and Artificial Intelligence predicting values is not the only expected result, (since they are also used to automate tasks and processes) we will focus on predictions to encompass the concept. The different applications will be mentioned in detail in future blog entries.

To conclude with the idea, it is the outputs of our models that should drive decision-makers to execute actions that bring value to their businesses. It is through their actions, their decisions, that they will affect the world around them. It is through Data Science that we will guide them, reducing the degree of uncertainty in the different scenarios that arise.

Published by

Wais

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