IBM Data Science Practice Test

The choice of scientific computing packages is correct because libraries like NumPy are fundamentally designed for numerical and scientific computing tasks. NumPy provides support for arrays, matrices, and a wide range of mathematical functions to operate on these data structures, making it essential for performing efficient numerical analysis. This capability is especially relevant in data science, where manipulating large datasets and performing complex mathematical operations are common tasks.

While data manipulation tools might refer to functionalities offered by libraries such as pandas, deep learning frameworks pertain to libraries like TensorFlow or PyTorch, and visualization techniques relate to libraries like Matplotlib and Seaborn, NumPy specifically specializes in numerical computation, indicating its categorization as a scientific computing package. Therefore, when you import such libraries into Jupyter Notebooks for data science work, you are engaging with tools that enhance your ability to perform high-level mathematical and statistical computations efficiently.