About what to exactly do, well the sky is the limit. The thing I love the most about programming is how it allows you to

This factor will soon play a decisive role in the fall of Python and rise of Julia and Swift. Julia is a language exclusively designed to address the shortcomings of Python. It primarily offers three features: coroutines (asynchronous tasks), multi-threading, and distributed computing — all of which only show the immense possibilities for concurrent and parallel programming. This structure makes Julia capable of performing scientific computations and solving big data problems at a far greater speed than Python.

Layers in Software Architecture that Every Sofware Architect should Know “All architectures have the same objective - the separation of concerns. They all achieve it by dividing the software…danielrusnok.medium.com

With this portentous beginning to many a well-intentioned post, you can almost be certain that what follows will be some distillation of painfully direct life advice intended to remind people of what’s important. Consider this article a really extended version of that, directed primarily at digital designers in senior and leadership roles as they are our industry’s best chance to right the ship. In order to bring you to the forward-thinking, uplifting bit, I’m going to have to first unsettle you with some difficult truths.

In short, Python uses GIL (Global Interpreter Lock), which prevents multiple threads from executing at the same time in order to boost the performance of single threads. This process is a big hindrance because it means that developers cannot use multiple CPU cores for intensive computing.

We are back talking about projects, and for good reasons, besides being vital to learning the subjects you are studying they are your best way to introduce yourself to potential employees, especially if you have no formal experience or education to speak of.

Finding an empty tube where the roll of toilet paper was can, perhaps, make you frown a bit, but that doesn’t really piss you off significantly. Because you remember that, sometimes, you tend to be a little lazy too.

For the last few years of my career, I’ve felt the weighty realization that the design industry’s accepted and most practiced ways of creating digital products are more deeply flawed than I ever imagined and much more than most will admit — as is the way we educate aspiring designers. This has been nagging at me to such an extent that I realized I could not stay complacently on the sidelines watching it happen any longer. If you follow me on Twitter, then you know I haven’t exactly been silent on this topic, but up until now, I haven’t really pushed for action, and that platform doesn’t really lend itself to fully expressing ideas. This then is my attempt to get a lot of my thoughts out in one place to hopefully catalyze new discussions. And I know many of you share these feelings even if you don’t dare talk about them.

Despite the disadvantages it has with respect to speed, multi-threading, and type-safety, Python still has a huge ecosystem that boasts an enormous set of libraries and packages. Understandably, Swift, and Julia are still infants in the field of machine learning and possess only a limited number of libraries. Yet, their interoperability with Python more than compensates for the lack of library support in Julia and Swift.

There’s no anxiety about whether the relationship is going to last or not. It’s never an on and off thing. And you never feel that it’s complicated or uncertain. Instead, there’s a mutual sense of decisiveness, commitment and security.

I agree with the commonplace notion that we’re currently doing fine when leveraging Python’s interoperability with C/C libraries like Tensorflow and PyTorch. But a Python wrapper doesn’t solve all debugging issues. Ultimately, when inspecting the underlying low-level code, we’re falling back on C and C . Essentially, we can’t leverage the strengths of Python at the low level, which puts it out of the picture.

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best analyzed and understood through visualization. We can write all the codes to do resampling and moving averages etc. and create new data frames all we want but in the end, we can’t understand anything until they are visualized.

Layers in Software Architecture that Every Sofware Architect should Know “All architectures have the same objective - the separation of concerns. They all achieve it by dividing the software…danielrusnok.medium.com

None of the quirks of the other becomes too annoying and both of you can be your authentic selves. You can play the guitar in your underwear, drink juice directly from the carton or do a nude workout in the living room. And it’s ok.

Julia not only lets programmers use Python code (and vice-versa), but also supports interoperability with C, R, Java, and almost every major programming language. This versatility would certainly give the language a good boost and increase its chances of a quick adoption among data scientists.

As compiled languages that offer type annotations, Swift and Julia are a lot faster and robust for development than Python. That alone might be enough to recommend them over the older language, but there are other factors to consider as well.