Data science is primarily about communication, both of your analysis to other people, including laypeople, domain experts, other data scientists, ops folks ...

Data science is primarily about communication, both of your analysis to other people, including laypeople, domain experts, other data scientists, ops folks ... and those same people communicating with you about their needs, important context, expectations of SLAs or constraints ... The main barriers to good communication of data science is the translation between the real world goals, data, algorithms, assumptions and priors, scientific and statistical methods being applied, testing, the deployed model's performance and ongoijg monitoring and management, etc .. and the actual code and artifacts produced to achieve those ends. Python wins because there is a wide ecosystem around those translation efforts. * Visualization and statistical profiling are first class citizens, probably the killer app in terms of communicating difficult mathematical concepts * Easy to extend, very "framework-friendly", so you can "speak the same language" between data engineering, DS, analysts, MLOps folks * Needless to say, network effects of a community used to Pythonic idioms