What is an interactive computing environment that combines code, visualizations, and text for data exploration?

The choice of a data science notebook as the correct answer is fitting because these notebooks are specifically designed to integrate code execution, visual representations of data, and narrative text within a single interactive interface. This allows users, particularly data scientists and analysts, to conduct data exploration efficiently.

Data science notebooks, such as Jupyter Notebooks or Google Colab, facilitate a hands-on approach to analysis where users can write code in various programming languages (with Python being the most prevalent), visualize outcomes through graphs and charts, and document their findings and interpretations simultaneously. This combination fosters an environment conducive to experimentation, learning, and collaborative work, making them quintessential for tasks involving data exploration.

Other options do not encapsulate the same combination of elements. For instance, a data pipeline refers to a series of data processing steps rather than an interactive environment. Data quality management focuses on ensuring data accuracy and integrity, while data privacy pertains to the protection and ethical handling of data. None of these adequately describes the multifunctional and interactive nature of a data science notebook.