Nowadays, we are overwhelmed by large amounts of information—see Shi, Zhang, and Khan (2017), or Fang and Zhang (2016)—the catchphrase being big data. However, defining it is still controversial, since many explanations are available. Davenport and Patil (2012) suggest that if your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a mashup of several analytical efforts, you've got a big data opportunity.
Many users of data science or data analytics are learning several programming languages such as R and Python, but how can they use both of them at the same time? If John ...
