![]() ![]() ![]() Is an object-oriented programming (OOP) language, so it's more general purpose than Bash.Lacks good debugging tools and utilities.Lacks many functions, objects, data structures, and multi-threading, which limits its use for complex scripting/programming.Piping ("|") CLI utilities like sed, awk, grep, etc.Is not fully compatible with other shells (e.g., csh, zsh, fish).Does not come preinstalled in Windows your script might not be compatible with multiple operating systems, but Bash is the default shell on most Linux/Unix systems.Has better startup time than Python but poor execution time performance.Can utilize command-line commands and utilities as-is.Is great for writing shell scripts that use command line interface (CLI) utilities, utilizing output from one command to another (piping), and executing simple tasks (up to 100 lines of code).Let's compare these two languages to get a better understanding of where each one shines. The honest answer is: It depends on the task, the scope, the context, and the complexity of the task. Both have pros and cons, and sometimes it can be hard to choose which one you should use. The former had 7000 listings, the latter had 500.Bash and Python are most automation engineers' favorite programming languages. I did a search on Linkedin job listing for "python data science" and "jmp data science". ![]() I have a goal this year to grow my knowledge and experience in using R and Python, if for nothing else, career security. And it is what is commonly taught in many training and university data science curriculum. The reality is that many (most) jobs in "data science" utilize a large toolbox of both commercial and open source tools. Python is definitely much more of a programming language, but if you have ever used any other programming language, it's easy to pick up and learn. I currently have a routine type of analysis that I do periodically that utilizes JMP as the interface to R, and it combines analysis from both tools. When I was using SAS extensively for predictive analytics, JMP was always at my side to help me understand and visualize the data I was using and the results of my analysis. JMP complements many other analytical tools. It's not a matter of JMP "or" Python (or JMP or R, JMP or Matlab. So to answer your question, you need to learn Python because data scientists are expected to be able to code like nobody's business, understand algorithms at an intimate level and be able to adapt them as needed, and to have tools that can take advantage of GPU and distributed computing. That's why Viya supports integration with Python. SAS Viya has some nice, modern AI tools but real data scientists need to be able to adapt algorithms to suit the problem at hand. To be fair, it is not really JMP's niche at all. It's more of a classical neural network platform, thought it is capable of having multiple hidden layers. JMP has a nice basic neural platform, but it would not be practical for most real-w orld AI problems. The past decade has seen so many ground breaking innovations, step change after step change enhancement that is very difficult to keep up with all of it. Neural networks of today are not the neural networks of the past. There is a huge emphasis on AI, particularly through different kinds of neural networks. When I really started delving into data science, I quickly realized I was greatly mistaken. What is your definition of data science? I'm a statistician by background and I used to balk at the term "data scientist" thinking it was just a sexier word for statistician. I love JMP, but if you try training a massive network with millions, let alone billions of parameters with JSL on a CPU with and no distributed computing of any kind and you will quickly realize why almost any data science job requires Python expertise. ![]()
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