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

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.

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) ...

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

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.

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.

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 ...

Find out what makes Python a versatile powerhouse for modern software development—from data science to machine learning, systems automation, web and API development, and more.