This book is edited by Li Hui and Chen Yanyan, with associate editors Yang Yu, Gao Yong, Zhang Qiaosheng, Bi Ye, and Liu Dengzhi. It is rich in content, covering 32 theories and 32 practical cases, ...

Introduction to Python for Data Analysis Recall that R is a statistical programming language—a language designed to do things like t -tests, regression, and so on. The core of R was developed during ...

This online data science specialization is designed for learners with little to no programming experience who want to use Python as a tool to play with data. You will learn basic input and output ...

Anaconda provides a handy GUI, a slew of work environments, and tools to simplify the process of using Python for data science.

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.

New extension pack bundles wildly popular tools for Python development, assisted by the AI-powered GitHub Copilot and a data wrangler.

Students are constantly learning new analysis skills, and it can be easy to fall behind, get confused, or need a touch-up after a break from material. To help out, CADS has student interns devoted to ...

Java has a lot going for it, but it's not the top language for data science. Java professionals may want to familiarize themselves with Python or R for data science workflows.

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.