How does the data scientist profession work?



The world of data analysis in companies is going through a real revolution: the growth of Big Data in the last ten years. From this, a new set of technologies and a new profession were created, that of the data scientist. This set of factors is allowing companies to analyze and create solutions from a volume of data that was unimaginable until recently.

This vast amount of information can bring great insights to the business, but it is of very little use if it is not passed on to professionals who know what to do with it. That's where the data scientist comes in.

Understand what a data scientist does, why he is such a coveted professional, and why your company will have to embark on the Big Data revolution to stay competitive. Read our article and understand the transformations caused by BigData in the IT market.

What does a data scientist do?

The data scientist (also called a data scientist) usually has training in areas such as Mathematics, Computer Science, Physics or even Economics. It is the professional who works directly with Data Science , or data science.

By profile, this professional needs to have a deep knowledge of computing, mathematics and data analysis, but that is not enough. The data scientist needs to be able to generate insights and solutions from the analysis of a large volume of data. And this is accomplished, most of the time, with the help of machine learning algorithms.

Therefore, this professional must be curious. He is called a “scientist” because he does not only analyze and present this information, as a data analyst would do, he needs to develop hypotheses, test them and seek solutions that are beyond the obvious. He must also have a deep understanding of the scientific method, as he needs to test hypotheses about complex problems.

Why is the data scientist such a coveted professional?

When talking about data scientists, usually the first example that appears in every conversation is that of the doctor of physics, Jonathan Goldman. He joined LinkedIN in 2006, when the company already had approximately 8 million users, but was still struggling to grow.

The main problem with LinkedIN was that its users, despite the possibility of inviting friends to the tool, still interacted very little and spent little time on the social network.

From the analysis of a large volume of data, Goldman began to formulate a series of hypotheses and test them in the tool. The main functionality he tested was called “People you may know”, which consisted of a small area of ​​the interface that featured the name of three LinkedIn users that the person probably knew.

These suggestions were given by crossing information such as the school where he studied, the company he worked for during the same period and other connections in the same network. The result: the “People you may know” area became the most clicked on the network and LinkedIN finally took off.

This example shows that having a professional with high data analysis skills, creativity and the ability to test less obvious ideas can be critical for companies. The story of Goldman and LinkedIN also shows how important it is for this professional to have the autonomy to test hypotheses, without having to go through the approval of dozens of executives in the chain of command.

The importance of diving into data science

What is the real urgency for companies to implement solutions to handle large volumes of data and to have a data scientist? And if she really wants to embark on this area, is there a good number of these professionals in the market?

The answer is no, the data scientist is still rare and extremely disputed by companies. According to DataCamp , which specializes in data science training, the annual salary of a data scientist in the United States is around $118,000.00. In addition to costing a high salary, this professional is extremely difficult to maintain, being disputed by the largest technology companies in the world.

This shortage is mainly due to the fact that it is a very new career, which requires many skills in different areas. In addition, there are still few institutions that offer specific training in Data Science.

So would it be better to wait for the consolidation of this market and the training of new professionals? The answer is definitely no!

Data science , by providing the possibility to analyze a huge volume of data, offers an almost unfair competitive advantage to those who apply it in relation to other competitors. It offers the company the possibility of seeking new market paths and making projections based on concrete data, not simply the executives' experience and “feeling”.

Netflix, Google, Facebook, Amazon and LinkedIN are some examples of companies that hire as many data scientists as possible. All these companies understood the importance of the professional and are investing heavily in Big Data. Therefore, those who know how to embark on this revolution as soon as possible will be ahead of their competitors, who can simply disappear if they don't know how to run after.

This blog, for example, arose from my interest in migrating to this area. It's a way to exercise one of my passions, content creation, and to acquire knowledge in data science. Currently my study journey brings together courses in Python, Power BI and statistics, which I am studying to enter my master's degree.

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