helloitsian

I've seen these types of comparisons, and I understand there a loads of factors playing a part in them.

However, it does make me wonder, why has python become to standard for data science? Is it Library support or purely community based?

What would be a good place to find one of these biases?

There doesn't seem to be any safeguards for editing the content that gets saved to the file. Much like injecting extra commands into the clipboard when a user copies a terminal command, what's stopping someone from injecting a obsfucated piece of code to line 10000 of a python file? Maybe fugu could enforce a diff result on the of the original vs new file as an additional security step but that's just another alert out of the 10 added ones you have to deal with in your development process.

Is there any safeguards in the FileSystem Writable api to protect from this?

Personally, I see it come up in more vague areas during my Web Development. It's more along the lines of, "based off my understanding of X, I can assume this bit of JavaScript code works like this under the hood". Understanding how memory works traditionally keeps me "memory-conscience", but this may be redundant when working with JavaScript. When writing complicated operations I can use things I've learned previously to improve efficiency. To put it nicely, I'm not balancing binary trees and sorting my arrays with JavaScript. However, it was helpful when learning how the VDom works.

In other industries...

I started my Software Development journey with C, then moved into Game Development in C++ as my first job. Game Development DEFINITELY requires a lot of CompSci info (that I lacked). Hash Maps, Pathfinding, Matrix math, physics calculations, Linked Lists; all the works. We were also using a proprietary Game Engine so maybe with other Engines CompSci isn't as needed, I haven't worked in different engines.

I have to ask though... Was it at scale?