All online platforms, such as Netflix, Spotify, YouTube, Instagram and Facebook They generate large amounts of data. To work with these elements and transform them into relevant information, companies need professionals who understand what Data Science is.

With greater data availability and reduced storage and processing costs, professionals in the field are using technology to make decisions and uncover valuable insights.

Advanced data analysis tools are constantly being developed, enabling data scientists to make predictions that solve problems and improve our daily lives. Today, the suggestions offered by applications, For example, they are becoming increasingly precise and accurate.

With that in mind, we've prepared this article to help you understand what Data Science is, the importance of the field, and how to work in this sector that attracts so much attention.

What is Data Science?

Data Science is based on the collection and analysis of data from different sources to aid in predictive decision-making, in large quantities, and generating insights. It's important to note that this prediction does not guarantee the future and is not immune to errors, but it is an essential tool for improving the decision-making process.

Data is extracted at different speeds and in large quantities (Big Data), so that processing generates value from the information. The set of methods that generate the expected result consists of defining the problems or questions, preparation, research, conclusion, and communication.

For data science to be useful to businesses, it must be inherent to the business, and the professional needs to have a comprehensive understanding of all aspects surrounding the subject. This field studies information through the process of capturing, transforming, generating, and analyzing data. To achieve this, data science involves several disciplines, such as:

• mathematics;
• statistic;
• computing;
• knowledge of the business.

The Importance of Data Science

Through careful analysis of the collected and processed data, it is possible to understand consumption patterns, for example, in more detail. Historical records may show a growth trend in purchases that leads the customer to spend twice as much, but they do not indicate that this same consumer may buy five times more from the competition.

To better understand this scenario, it is necessary to conduct an in-depth analysis of the data, through technologies, processes, algorithms, and methods. With this, it is possible to generate actions based on insights that will create value for the business.

The role of Data Science also has academic importance. Universities are realizing that companies are looking for programmers who know how to work in teams and have a strong grasp of Data Science. Therefore, many educational institutions have adjusted their courses to deliver more value to the market, in addition to developing research in the field.

What does a Data Scientist do?

Once the data is generated, the scientist can apply analytical techniques to obtain relevant information from a seemingly disorganized dataset (semi-structured, structured, or unstructured).

Therefore, this professional is responsible for cleaning, processing, transforming, and organizing information in order to then apply their knowledge to benefit the business. It's important to have a good contextual understanding of the data and know how to use some machine learning algorithms.

Therefore, Data Scientists find solutions and contribute to strategic decision-making. This is why professionals with such knowledge are increasingly in demand in the market, and receive good compensation for their research and analysis. This trend is related to companies' growing concern with Data Science.

Are Data Scientist and Data Analyst the same thing?

Data scientists and data analysts perform different roles within companies. Therefore, understanding these differences is important so that companies can hire the most appropriate professional for each need.

The definition of Data Analyst is very similar to that of Data Scientist. Generally, both are responsible for transforming raw data into useful information for companies, in which... managers They can use this information to make the best decisions.

The distinction lies in some very subtle points, but these make a significant difference in the day-to-day work. A data analyst is also responsible for gathering, processing, and analyzing data statistics. However, their skills may not be as highly developed as those of a data scientist.

In some cases, the analyst is unable to develop new code, being restricted to working with pre-existing tools, as well as systems and datasets established by another professional.

However, the goal of both the analyst and the data scientist is the same: to understand how the information contained in the data can be used to solve problems and meet demands.

How to work with Data Science?

To work in Data Science, it's important to have a strong background in Information Technology (IT). To achieve this, you can pursue a more comprehensive Computer Engineering degree and then specialize according to market demands through... free courses and postgraduate studies.

Furthermore, it's important to have solid technological know-how and good interpersonal skills. After all, a data scientist must interact with both the IT department and the business department of the company.

Regarding technical skills, it is essential to have knowledge of:

• different programming languages;
• operating systems;
• data modeling;
• Statistics and mathematics applied to data, such as inclusion of tests and distributions;
• business and company processes.

Ideally, a data scientist should have expertise in three areas: Information Technology, Advanced Analytical Sciences and Technologies, and Business.

Now that you know what Data Science is, it's important to understand that professionals in this new field must create models to enhance the productivity of all sectors.

There are no restrictions in any field for the work of a data scientist, which is a great opportunity to make human effort increasingly efficient.

Did you enjoy the content and are you interested in the area? Then be sure to check out our... A complete guide to the Computer Engineering course..