Basic Libraries for Data Science These are the basic libraries that transform Python from a general purpose programming language into a powerful and robust tool for data analysis and visualization.

The annual Python Developers Survey shows a programming environment in transition. Data science accounts for more than half ...

Although Julia is purpose-built for data science, whereas Python has more or less evolved into the role, Python offers some compelling advantages to the data scientist.

Programming languages Python and R are often pitted against each other over which is best for data science and analysis. Both are popular, although Python appears to be much more widely used, at ...

Nvidia wants to extend the success of the GPU beyond graphics and deep learning to the full data science experience. Open source Python library Dask is the key to this.

But Continuum Analytics in Austin, Texas, have released Anaconda, a free package that bundles together around 200 of the most popular Python libraries for science, maths, engineering and data ...

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

Because when you combine Python with the Numba just-in-time (JIT) compiler, the Cython compiler, and runtime packages built on Intel performance libraries such as Intel Math Kernel Library (Intel MKL) ...

What are some use cases for which it would be beneficial to use Haskell, rather than R or Python, in data science? This question was originally answered on Quora by Tikhon Jelvis.