What is TensorFlow primarily used for in the field of data science?

TensorFlow is primarily used for building and training neural networks because it is a powerful open-source library designed specifically for deep learning and machine learning tasks. It provides a flexible framework that allows data scientists and machine learning practitioners to create complex models tailored to various types of data, including images, text, and audio.

The library supports extensive neural network architectures and facilitates tasks such as automatic differentiation, which simplifies the optimization process during model training. TensorFlow includes tools for model deployment and offers extensive community support and resources, making it a go-to choice for projects that utilize artificial intelligence, natural language processing, image recognition, and other advanced machine learning techniques.

In contrast, data visualization focuses on presenting data in graphical formats, basic statistical analysis deals with exploring datasets using statistical methods, and database management involves organizing and retrieving data in databases. These tasks, while important in data science, do not specifically leverage the capabilities and strengths of TensorFlow as a deep learning framework.