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The field of data is becoming increasingly in demand in the market, both in Brazil and worldwide. Companies across all sectors require a data team because proper interpretation of data can bring many benefits to the business, such as cost reduction, increased efficiency, competitive advantage, identification of opportunities, and many others.
Therefore, if you intend to work in this field, it is very important to learn more about this career, starting with the distinction between the roles of data analyst and data scientist. Do you know what each one does?
Don't worry. We have prepared this special content for you to better understand these two highly in-demand professions in the market. We will explain what each one does, what the proper training is, and provide details about these professionals' careers. Keep reading to clear up any doubts!

What does a data analyst do?

The data analyst is the professional responsible for gathering, organizing, and interpreting statistical data. They must master specific data analysis tools to draw conclusions and provide relevant insights for the business, which will assist in decision-making.
In general, we can say that the data analyst is the first step in a career in this field. This is because they do not yet need to have the in-depth knowledge that a data scientist should possess.
However, to be considered a competent professional, they must perform their role in structuring data according to identified patterns. This requires a certain level of knowledge and, with that, the ability to generate valuable insights.
It's also worth mentioning that this is a generalist role. In other words, the data analyst is a versatile professional who can work in various teams and functions.

And what does a data scientist do?

The data scientist is the professional responsible for analyzing and interpreting raw data using Big Data and Machine Learning algorithms. In this sense, the work of a data scientist is similar to that of a data analyst. The difference is that the former uses more advanced algorithms and has more solid knowledge in the field of statistics.
The work of a data scientist is not merely technical. It goes beyond that by assuming a strategic role in companies, capable of generating a high impact on the organization. After all, with their knowledge, they can predict future patterns based on previous patterns.

This allows for relevant mappings and experiments that help the company grow sustainably based on solid data. Particularly in the technology sector, data scientists deal with infrastructure, machine learning, testing, and other areas.

What are the main differences between a data analyst and a data scientist?

As you may have noticed, both data analysts and data scientists have similar functions. However, they differ in levels of complexity and how they work with data.
Next, let's take a look at the main differences in the roles of data analysts and data scientists.

Data Analyst

The data analyst:

• explores and looks for patterns, trends, and possible errors in data, gathering insights;

• uses analytical techniques and prepares regular reports for the company;

• creates graphs and tables to visualize what the data reveals;

• analyzes data from sources such as CRM, Data Warehouse, Data Lake, and relational databases, although they usually

• work with one source at a time;

• seeks solutions to issues previously identified by the company;

• is not expected to have experience with machine learning or the ability to build statistical models.

Data Scientist

The data scientist:

• is an expert in data interpretation;

• is skilled in programming and machine learning;

• is capable of creating new data modeling processes;

• works with predictive models and algorithms;

• has business acumen and the ability to translate analyst insights into a business story (Data Storytelling);

• explores and analyzes data from multiple sources simultaneously;

• is knowledgeable in SQL;

• formulates the questions for which certain solutions may benefit the business.

What are the technical skills of a data analyst?

A data analyst must have some essential technical skills and knowledge for their daily work. Check them out!

Programming Languages

The data analyst needs to be proficient in programming languages such as Python, R, and SQL. These are important for properly manipulating data, creating visual representations, and performing statistical analyses.

Visualization Tools

The analyst should master tools like Tableau and Power BI. These are widely used to create visual representations of complex data.
After all, it’s not just about creating simple charts. These tools allow the analyst to tell compelling visual stories with the collected data.

Statistical Analysis

Statistical analysis is an essential part of a data analyst's work. It enables the use of methodologies to make inferences about data. It is important to be proficient in both descriptive and inferential statistical analyses, as well as in hypothesis testing.

Data Wrangling

Data wrangling is the process of cleaning and transforming data into a usable format. Cleaning involves identifying errors and inconsistencies in the data, and transforming it allows for proper analysis.

In addition to technical skills, some interpersonal skills are also important for a data analyst, such as:

• good communication;

• problem-solving;

• critical thinking;

• analytical reasoning;

• innovation;

• attention to detail, among others.

What are the technical skills of a data scientist?

All of the technical and interpersonal skills of a data analyst also apply to a data scientist. The main difference is that a data scientist needs to have more advanced knowledge in some areas, such as:

• machine learning;

• technology;

• modeling;

• statistics;

• programming;

• mathematics;

• computer science.

Additionally, an essential skill for a data scientist is having a solid understanding of the business model they are working within.
This is because, unlike the more technical role of a data analyst, the data scientist needs to have strong business acumen. This enables them to communicate with IT leaders and other stakeholders, contributing to the company's data-driven decision-making process.

In this sense, a data scientist can be compared to a true agent of innovation within companies.

How is the education of data analysts and data scientists?

You may have noticed that both data analysts and data scientists have similar knowledge and skills. The same goes for the education of these two professionals.
Both roles require solid knowledge in mathematics, statistics, programming, and analysis. In this sense, undergraduate and postgraduate courses in these areas can be beneficial.
However, more specific courses focused on the relationship between data science and the business world are very valuable for staying ahead of the competition.

Get to know PM3 Sprints on data!

A career in data is very promising. After all, companies of all sizes and sectors rely on data to make strategic decisions that enable continuous business growth.

Therefore, the roles of data analyst and data scientist are in high demand in the market. The key differentiator for these professionals, as you may have noticed, lies in the depth of their knowledge.

So, whether you're already working with data or want to start in the field, PM3 courses are for you! From beginner to advanced levels, PM3 Sprints on data offers essential knowledge for those looking to take the next step in data analysis and interpretation.

To give you an idea, PM3 Sprints offers courses on:

• SQL for data manipulation and analysis;

• Power BI and advanced analytics;

• Excel beyond formulas;

• Data visualization and dashboard design;

• Problem solving in practice;

• Data analysis methods;

• Experimentation with A/B testing, among others.

Can you see how this is a comprehensive opportunity to update your skills and elevate your career? So, get to know PM3 Sprints on data!