Exploring the Diverse Possibilities in the Field of Data: Where You Can Work?

Hey everyone, how are you all doing? I hope you're well. Today we're going to discuss the diverse possibilities for careers within the field of data.

Let's imagine that the data field is like a big city full of different avenues and neighborhoods, each with its own purpose. There are various roles within this city, and each one plays an important part in ensuring that data is collected, organized, analyzed, and used to solve real-world problems. Today, I want to share with you what some of these professionals do, the differences between them, and the new professions that are emerging in this universe as the field continues to expand.

The Main Possibilities within the Field of Data

Within this field, there are three positions that are the most well-known when we think about data professionals: Data Engineer, Data Scientist, and Data Analyst. Each of them has a very well-defined role, but don't be fooled into thinking that each one works individually; quite the opposite, they all need to work together to transform data into something truly useful.

What Does Each Professional Do?

Below, I will briefly explain the function of each of the most well-known professions in a logical manner. That is, I will present what each professional does, in the order required by the data process.

Data Engineer:

Think of the Data Engineer as the builder of roads and bridges in our hypothetical data city. They are responsible for organizing, cleaning, and creating the structures that allow data to travel safely from one place to another. These structures can be databases, data pipelines (which are like pipes for transporting information), and even entire platforms.

Example: Imagine you own a large apple farm and need to send your apples to the market. The Data Engineer builds the roads that take the apples to the market. Without them, we wouldn't have roads, and everything would be scattered and confusing, making it difficult for the apples to reach their destination.

Data Scientist:

The Data Scientist would be like a detective in our city. They take the data already organized by the engineer, analyze it, and uncover patterns, trends, and answers to difficult questions. They often use Artificial Intelligence and advanced statistics to predict the future or solve more complex problems.

Example: Remember the apples? The Data Scientist will use market data to discover which type of apple sells the most, at what time of the year, and why. This way, they can help the farmer better organize their sales and save money.

Data Analyst:

The Data Analyst is the one who interprets the numbers and presents this information clearly and objectively through charts and reports. They are like a translator in our data city, transforming numbers into insights that anyone can understand.

Example: After the Data Scientist discovers that green apples sell more in the summer, the Data Analyst creates a chart showing this information and presents it to the farmer, so the farmer can make the decisions they believe are best for their business.

New Positions That Are Emerging:

With the growth and expansion of the data field, new professions with more specific roles are emerging. I will briefly cover two of them:

Analytics Engineer:

This professional is like a hybrid between a Data Engineer and a Data Analyst. They work with a data team, usually with the engineers and analysts, to create representations and tools for datasets that allow the end-user to understand and evaluate the information provided in the data.

Example: They create a "little machine" that shows, every minute, how many apples were sold in each market.

Machine Learning Engineer:

This is the specialist in building and deploying Artificial Intelligence models at a large scale. They take the models created by the Data Scientists and turn them into systems that work automatically.

Example: They create a system that analyzes apple sales and automatically suggests how many to plant for the next season without anyone having to do it manually, which speeds up the process significantly.

Conclusion:

Well, we're reaching the end of another article. It's clear that the data field is vast, full of possibilities, and has room for all kinds of talent. From those who like to build the foundation to those who prefer to analyze and present the results, there's always a way to contribute.

However, it's important to remember that for this great, fictional "data city" to function, every part needs to be well-connected and organized. When this happens, the results are incredible: smarter decisions, problems solved, and a lot of value generated for companies and people.

So, have you thought about which avenue you want to explore in our data city? That's all for today, everyone. All the best, and see you in the next topic.