Data Science is a huge domain and it includes a lot of processes and tools. In a nutshell, data science refers to the art and science of converting raw data into actionable insights that take businesses forward with the help of data-driven decision-making.
In the entire data science lifecycle, one can come across various tasks and processes, right from data collection to building efficient data science models.
Currently, every organization, big or small, understands the importance of data science and hence they are actively integrating a culture of data science into their business operations. This has led to a rapid increase in the data science market which is expected to reach around $322.9 billion by 2026 (Markets and Markets).
But, behind this huge growth, data science professionals are often entangled in the concepts of data science, big data, and data analytics. These terms are often used interchangeably in the world of data science.
So, are they the same?
USDSI® brings a detailed document pointing out the intricate differences between these three popular data science terms and explains their importance in today’s data-driven world.
In this guide, you will learn about the definition, applications, tools, and role of each of these terms.
Download now to understand their unique roles and differences
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