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Data Science vs Data Analyst

Algunas personas piensan que los científicos de datos y los analistas de datos son lo mismo. Pero no lo son. Conozcamos más sobre las diferencias entre estos dos roles, y lo que necesitas saber si quieres entrar en ellos.
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Data scientists and data analysts are terms that are often used interchangeably. These two works largely work together, but they are definitely not the same thing. Let's take a look at what it means to be a data scientist vs. what it means to be a data analyst




What does a data scientist do?
Data scientists are people who use data to solve problems. Their job is to find insights in the data and present their findings in a way that makes sense to the company and its customers.
They usually work with large amounts of unstructured data. The data scientist will clean this unstructured information by transforming it into something that can be analyzed using model predictive analytics tools such as R, Python, so that predictions about future events can be made using algorithms built on machine learning.


What does a data analyst do?

Data analysts are tasked with analyzing data and turning it into actionable insights. They work with a wide range of data, both structured and unstructured. If you want to be an analyst, you probably have the opportunity to use different types of databases, such as Oracle or SQL, which allow your team members to quickly query the database without having to write code to get their answers.
Which companies have used these terms?

Data Analyst
Uber
One of the first advantages in data analytics was demonstrated in location services through GPS.
Smartphones with satellite positioning have allowed the development of advanced maps and algorithms to optimize traffic routes in cities.

Data science
RAPPI
Provide data & insights to ops teams and the Restaurants vertical to improve decision-making and datadriven.

In short, data science, data analytics, and data engineering are all parts of the same puzzle. The terms are often used interchangeably because they have similar goals: they all use data to solve problems. Although there is some difference between these functions, each one has its own responsibilities and tools that make it unique.
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