Python’s data science libraries make it an instant hit among data scientists. From Numpy, Scipy, StatsModels, and sci-kit-learn, Python continues to add data science libraries to its collection.

Python data science essential: SciPy 1.7 Python users who want a fast and powerful math library can use NumPy, but NumPy by itself isn’t very task-focused.

Pydantic: This library enforces type constraints and validates data, making sure consistency and accuracy. It is essential for structuring data in your AI workflows.

The main reason to use Python is that you get a lot more options than what's included in most spreadsheets. Spreadsheets are primarily designed for business and financial calculations. You can perform ...

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 for Data Science Essential Training is one of the most popular data science courses at LinkedIn Learning. This is course 1 of 2. In this course, instructor Lillian Pierson takes you step by ...

This article rounds up some of the most valuable free data science courses offered by top institutions like Harvard, IBM, and ...

It’s also possible to interface with Python code by way of the PyCall library, and even share data between Python and Julia. Julia supports metaprogramming.