Originally, predictive analytics was performed by hand, by statisticians on mainframe computers using a progression of various language such as FORTRAN. Some of these languages are still very much in use today. FORTRAN, for example, is still one of the fastest-performing languages around, and operates with very little memory. So, although it may no longer be as widespread in predictive model development as other languages, it certain can be used to implement models in a production environment.
Nowadays, there are many choices about which software to use, and many loyalists remain true to their chosen software. The reality is that for solving a specific type of predictive analytics problem, there exists a certain amount of overlap, and certainly the goal is the same. Once you get the hang of the methodologies used for predictive analytics in one software package, it should be fairly easy to translate your skills to another package.
Open source emphasizes agile development and community sharing. Of course, open source software is free, but free must also be balanced in the context of Total Cost Of Ownership (TCO). TCO costs include everything that is factored into a softwares cost over a period of time: that not only includes the cost of the software itself, but includes training, infrastructure setup, maintenance, people costs, as well as other expenses associated with the quick upgrade and development cycles which exist in some products.
Closed source (or proprietary) software such as SAS and SPSS was at the forefront of predictive analytics, and has continued to this day to extend its reach beyond the traditional realm of statistics and machine learning. Closed source software emphasizes stability, better support, and security, with better memory management, which are important factors for some companies.
There is much debate nowadays regarding which one is better. My prediction is that they both will coexist peacefully, with one not replacing the other. Data sharing and common APIs will become more common. Each has its place within the data architecture and ecosystem that are deemed correct for a company. Each company will emphasize certain factors, and both open and closed software systems are constantly improving themselves. So, in terms of learning one or the other, it is not an either/or decision. Predictive analytics, per second does not care what software you use. Please be open to the advantages offered by both open and closed software. If you do, that will certainly open up possibilities for working for different kinds of companies and technologies