Today, data plays a key role in decision-making, and two professions stand out for their ability to exploit it effectively: the Data Scientist and the Data Product Manager. Although their missions sometimes overlap, their approaches and responsibilities are distinct.
To better understand the difference between these roles, let's imagine a car. The Data Scientist is the mechanic who adjusts the engine, optimising the predictive models. The Data Product Manager, on the other hand, is the driver who sets the direction, ensuring that the product is aligned with business needs. Together, they ensure that the vehicle moves efficiently towards its objective.
In 2024, Data Science and Artificial Intelligence (AI) continue to evolve rapidly, and the tools used by professionals in these fields reflect technological advances as well as the growing need for data processing and analysis. Whether it's for manipulating massive data, machine learning or creating predictive models, certain tools stand out for their efficiency and adaptability. Here are the main tools dominating the data science and AI landscape this year.
With the rise of data in all sectors, two key professions have emerged: Data Engineering and Data Science. Although their missions are complementary, their roles and skills are often confused. So what are the real differences between these two professions? In this article, we explore their responsibilities, their tools, and how they work together to transform data into strategic business value.
Have you ever wondered why some companies manage to stay ahead in such a competitive environment? One of the answers often lies in their ability to exploit their data, an asset made possible by the work of the Data Scientist. But what exactly is their role, and why have they become so indispensable?
But what exactly is Data Science, and why is it so valuable? Let's discover together the basics of Data Science, its essential concepts and the many applications that make it an essential strategic asset.
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