Reasons for using Jupyter via Anaconda

In data science or data analytics, we usually work in a team. This means that each developer, researcher, or team member, might have his/her favorite programming language, such as Python, R, Octave, or Julia. If we could have a platform to run all of those languages, it would be great. Fortunately, Jupyter is such a platform, since this platform can accommodate over 40 languages, including Python, R, Julia, Octave, and Scala. 

In Chapter 2, Anaconda Installation, we will show you how to run those four languages via Jupyter. Of course, there are other benefits of using Anaconda: we might worry less about the dependency of installed packages, manage packages more efficiently, and share our programs, projects, and working environments. In addition, Jupyter Notebooks can be shared with others using email, Dropbox, GitHub, and the Jupyter Notebook Viewer.