Читать книгу Data Science For Dummies - Lillian Pierson - Страница 31

Data science, machine learning engineering, and data engineering cover different functions within the big data paradigm — an approach wherein huge velocities, varieties, and volumes of structured, unstructured, and semistructured data are being captured, processed, stored, and analyzed using a set of techniques and technologies that are completely novel compared to those that were used in decades past.

All these functions are useful for deriving knowledge and actionable insights from raw data. All are essential elements for any comprehensive decision-support system, and all are extremely helpful when formulating robust strategies for future business growth. Although the terms data science and data engineering are often used interchangeably, they’re distinct domains of expertise. Over the past five years, the role of machine learning engineer has risen up to bridge a gap that exists between data science and data engineering. In the following sections, I introduce concepts that are fundamental to data science and data engineering, as well as the hybrid machine learning engineering role, and then I show you the differences in how these roles function in an organization’s data team.