Why is the Data Scientist profile so important in 2022?

Can you imagine what you could do if you knew when a customer might lose interest in your services or products? The data scientist can help companies answer this and many other questions.

Data scientist

What is a data scientist?

A data scientist is a professional who is responsible for analyzing, processing and modeling large amounts of data from existing data. As such, they are curious individuals as they explore answers to questions posed to them or questions they may ask.

Also, it is a person who must have strong skills in mathematics, statistics, and optimization methods, with knowledge of programming languages, and who also has practical experience in the analysis of real data and the elaboration of predictive models. So says Jose Antonio Guerrero, considered the best data scientist in Spain.

They are professionals who need skills in many branches of knowledge and span the worlds of business and IT, are highly sought after and well paid. Who wouldn’t want to be one of them?

Why is the task of data scientists so important?

In the vast amount of data in the world that is generated daily on a variety of topics, it is increasingly important to have people who know how to handle and interpret this data.

According to forecasts, the volume of data generated worldwide will exceed 180 zettabytes by 2025, which represents an average annual growth of almost 40% in five years.

Infografía: El Big Bang del Big Data | Statista

Precisely, the search for unclassified data and the interpretation of this valuable information is what is known as Big Data and this is what data scientists are dedicated to.

In parallel, many companies currently use this data to do Machine Learning, which is how systems learn automatically to provide solutions through algorithms. An example of this is Netflix, which recommends movies and series based on the information about the movies you have already watched along with the type of profile it considers you according to your browsing.

And likewise, companies of all kinds, in any sector require experts who are dedicated to Big Data and Machine learning. Unfortunately, the skills that these professionals must have are not easy to find in the market, and the supply cannot satiate a demand that, since 2014, has been increasing by 33% annually.

What does a data scientist do?

In principle, the data scientist’s job is to extract knowledge from the data an organization has in order to answer the questions posed to it.

They also develop and apply more sophisticated algorithms and machine-learning techniques to data analysis. Precisely the ability to predict is one of the key points of this role and probably where they provide the most value for the business.

This information is of the highest value for any company. The effective implementation of these models in multiple customer segments will help us reduce churn and promote retention.

How to be a data scientist? 3 essential skills you need

As mentioned above, the demand for this talent is increasing for companies. And, as they are among the most demanded profiles, they are also among the best paid as there is great competition from companies to attract and retain these professionals.

Due to this phenomenon, many professionals are turning their careers to data. However, they are encountering the big barrier of having the innate or acquired skills necessary for these positions and for the greater needs of companies.

So, if you are interested in becoming a data scientist, we show you the most important skills that these profiles must have:

Data scientist skills


Behind the Big Data and Machine Learning processes that data scientists do, mathematics plays a basic role. Specifically, having a strong foundation in Probability and Statistics and Multivariable Calculus and Linear Algebra is important.

One of the main functions of a data scientist is to predict, infer or estimate. And for this, he or she uses processes, algorithms, or systems to extract knowledge, and insights and make informed decisions from data. Probability and statistics are the mathematical basis for developing these estimates.

On the other hand, most machine learning is built with several predictors or unknown variables. Therefore, knowledge of Calculus using several variables is important for these models.

So if you consider yourself good in these knowledge areas you already have a good excellent knowledge base. And, if this is not the case, you can always learn, this is only a part of what these professionals do, don’t be discouraged!

Data processing

This is really the bulk of the skills required of a data scientist. It is one of the most important skills as software can do all the mathematical work you want, however all the exploration, cleaning, model building, and presentation of results you will have to do on your own with the skills you have.

In this aspect is the whole process of the data treatment, from data exploration, through Data Wrangling.

Going through the administration of databases by means of programs that can edit, index, and manipulate the database. Up to the correct visualization of data in order to understand and understand the data in the best way.

About 80% of the work this professional does is based on data preparation and visualization.

Programming languages, packages, and programs

Of course, data scientists have a strong foundation in programming, which is where raw data is transformed into actionable knowledge. In general, data scientists choose the programming language that helps them most to arrive at their solution. The truth is that Phyton and SQL are preferred by these professionals.


Phyton is one of the programming languages most used by people in the world and this has many explanations, but it is mainly because Python programming is versatile and adapts to different styles and projects.

So, if you are a systems engineer, Full-stack or you like data mining, most likely you can work with this program. Additionally, it is easy to read, write and learn.

Programming languages


68% of data scientists use SQL as a relational database manager. And, like Phyton, SQL is one of the most widely used programming languages in the world. Basically, any interface you see anywhere is most likely based on this language.


R allows you to analyze any kind and size of data. From exact techniques for small data sets, high-performance statistical modeling tools for tasks with large data sets, and modern methods. As a language, it also has a long history. However, its advances have been stagnating, making other types of programs more satisfying.

Salary of a Data Scientist

Now, how much does a data scientist earn on average? As we have repeated several times, its importance in virtually all industries and types of companies places these professionals as one of the most demanded profiles and therefore, with better salary compensation.

For example, the average salary of a data scientist is 31,510 € per year in Spain. In the United States, the average salary of a Data Scientist is USD 121,558 per year in New York. In Switzerland, the average salary is CHF 108,226 per year in Zürich.


As you can see there are many opportunities in this field, due to the importance that this role takes today in Big Data and Machine Learning take into account the tips we give you in this article and we assure you that you will have a secure future in this field as a Data Scientist. What are you waiting for?

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