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, ...

You don't have to spend a fortune and study for years to start working with big data, analytics, and artificial intelligence. Demand for "armchair data scientists" – those without formal ...

Anaconda provides a handy GUI, a slew of work environments, and tools to simplify the process of using Python for data science. No question about it, Python is a crucial part of modern data science.

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 to ...

Java can handle large workloads, and even if it hits limitations, peripheral JVM languages such as Scala and Kotlin can pick up the slack. But in the world of data science, Java isn't always the go-to ...

Already using NumPy, Pandas, and Scikit-learn? Here are five more powerful Python data science tools that deserve a place in your toolkit. Python’s rich ecosystem of data science tools is a big draw ...

What do you get when you combine the No. 1 code editor with the No. 1 programming language for data science? You get more than 60 million installs of the Python ...

What are some use cases for which it would be beneficial to use Haskell, rather than R or Python, in data science? originally appeared on Quora: the place to gain and share knowledge, empowering ...

Do you find yourself doing the same repetitive SEO tasks each day or facing challenges where there are not tools that can help you? If so, it might be time for you to learn Python. An initial ...