In this article, we’ll introduce you to some of the libraries that have helped make Python the most popular language for data science in Stack Overflow’s 2016 developer poll.

Python can be made faster by way of external libraries, third-party JIT compilers (PyPy), and optimizations with tools like Cython, but Julia is designed to be faster right out of the gate.

By implementing popular Python packages such as NumPy, SciPy, scikit-learn*, to call the Intel Math Kernel Library and the Intel Data Analytics Acceleration Library (Intel DAAL), Python applications ...

Data science and machine learning professionals have driven adoption of the Python programming language, but data science and machine learning are still lacking key tools in business and has room ...

Python has been the language of data science since before machine learning was trendy, and now you can use it for building AI ...

A key part of CUDA-X AI is RAPIDS. RAPIDS is a suite of open-source software libraries for executing end-to-end data science and analytics pipelines entirely on GPUs. And a key part of RAPIDS is Dask.

Python is great because it includes an interactive mode for learning the language and quickly testing out code ideas. IPython ...

But with Python libraries, data solutions can be built much faster and with more reliability. SciKit-Learn, for example, has built-in algorithms for classification, regression, clustering, and ...

A study conducted by the Institute of Electrical and Electronics Engineers (IEEE) put Python at the top of the list of favorite languages among such contenders as Java, JavaScript, C++, and Go.