Conda for data scientists
Conda is useful for any packaging process but it stands out from other package and environment management systems through its utility for data science.
Conda’s benefits include:
Providing prebuilt packages which avoid the need to deal with compilers or figuring out how to set up a specific tool.
Managing one-step installation of tools that are more challenging to install (such as TensorFlow or IRAF).
Allowing you to provide your environment to other people across different platforms, which supports the reproducibility of research workflows.
Allowing the use of other package management tools, such as pip, inside conda environments where a library or tools are not already packaged for conda.
Providing commonly used data science libraries and tools, such as R, NumPy, SciPy, and TensorFlow. These are built using optimized, hardware-specific libraries (such as Intel’s MKL or NVIDIA’s CUDA) which speed up performance without code changes.