Name: データサイエンスのためのプログラミング言語トップ3 (Top 3 Programming Languages For Data Science)
Uploaded: 2021-01-14T10:21:37.000Z
Duration: 9 min 5 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

So today we're going to talk about the top three programming languages for data science.

転換語

And before we do that, I want to talk about my life because I'm a youtuber and I'm a narcissist, and I need constant validation from you guys.

So I'm thinking a short vacation because and I felt a little bit burnt out from work, so I wanted to chill and do nothing for, like, a few days and the perfect person to do that with my brother.

And this is what we did when we were here.

Yeah, but since I'm an aspiring filmmaker, that's how I saw it anyways, So I'm gonna go back home now, uh, gonna have to go find a scooter so I could scoot back home a See you later.

They see me roll, they hate patrol in and trying to catch me Ride and dirty catch me riding Thurday trying to catch me riding, riding dirty Right, So I'm swinging and they hold in the big catch me ride Okay.

This is my bed This is a Nintendo switch.

I've been playing a lot of Celeste super fun.

So So I'm going to answer this question really quick, and I'm going to answer it for Data Science analytics, because that's what I know.

So the top three programming languages are python are and sequel.

And I actually don't even like answering these questions because it's just so different in terms of the languages and their use case.

Like, for example, sequel isn't even a programming language.

But I still put it there because it's quite important to have, or at least to know how to use it.

Because no matter what data use, you have to use something taqueria.

And I wouldn't even consider having to learn sequel because it's basically just a tool to grab data out of the database.

So in some ways it's kind of saying, like learning how to use Windows computer or learning how to write you or else Thio access the Internet like I'm exaggerating a little bit, but that's kind of how I feel about enemy.

So here's why I chose Python as number one python and are both have the best package is related to stats are the programming language is basically made for numerical computations.

So that's why our has a vast range of statistical packages that you can use, and they're all open source, even more so than Python.

But the thing about Python is it wasn't built for numerical computation of her stats or anything like that.

Python was built for a clear programming.

So then Python got more attention from developers.

And then, because of that, developers would soon start to develop more frameworks or libraries to support the pipe language like, for example, Web frameworks like Flask Django.

They were all built for python, and then because it's so popular, it also drew attention from the stats.

People and day party really like the language because it's clean.

So then that's why the stats people started developing psychic pandas, Jupiter notebooks, everything that you need to make statistical analysis.

It's a little bit of a virtuous cycle because the more you have support for numerical computation, the more people will use it, and because the more people use it, the more frameworks will be developed and more support it would have So, for example, tensorflow for machine learning.

And the reason they provide a python a p I.

I think it's because Python is so popular amongst the machine learning developers or set positions or maybe even data scientists quants because Python is already so popular and tensorflow decided, have an A p I for python and see.

So basically, python is just super popular, and it has tons of resource is online and more more people, I feel, are moving away from our and starting to use python.

Further statistical analysis, they use Jupiter Notebook.

It's super nice because it's a way to organize your notebooks and a way to present analysis are still a very, very good option.

And I mean, when you think about it, our is literally built four statistical computing, so it's pretty damn good.

However, it is less flexible like imagine if suddenly you want to start making websites, you're gonna you're not gonna make a web back in using our There's no frameworks for that.

No matter what you do, you don't need to be a data scientist.

And you want a query, Some data for testing purposes or maybe to validate your feature or something like that.

You're so gonna need to pull that data somewhere and usually is from a database and in most database, except for no sequel.

Most relation databases they use equal as their language.

That's top three programming languages for data science.

Now, I just want to thank Rahm Sand for supporting this channel much, much appreciate it.

And also Joshua Pop asked, What is one social thing and one technical thing you wished you learned in college that would have helped prepare you for the tech industry?

Well, the thing I will say is not super tired to tech industry.

It's more about me as a person and my personal growth.

But the one social thing is, I wish I was a better leader and communicator at work.

If you take the initiative and you communicate really clearly what you're going to do.

You'll do a lot better than most new grads.

I nobody listens to me and I'm not very assertive.

And also, you know, I have sometimes trouble communicating what's on my mind on my ideas or even my road map to my peers.

And then because of that, I get affected negatively.

So for the technical thing, I wish I learned out West Development because if ever I want to quickly prototype app, app idea, then Aiken do it a lot easier if I know I owe its development.

And most of the apse nowadays, they have a mobile component to it because I didn't know how do I always development?

I couldn't execute on some ideas that I wanted to, because I just suck at front and programming, and I feel like that's been always my limiting factor.

I just really suck a front and development.

I can quickly build a p eyes or, you know, *** ends of databases and all that.

A man front and I suck so much anyways, before I go.

There's a quick word from our sponsor today.

By the way, this video was sponsored by brilliant dot org's, which is kind of cool because I actually used brilliant dot org's when I was applying to my untitled large company.

Since the company gave me links to help me prepare for my interview and one of the links that they gave me is a bunch of combinatorics questions, a k a probability questions on brilliant dot org's.

It really helped me on the map part of the interview because I was killing the probability questions.

I actually find it really fun to crank out these questions.

It makes me feel good about myself and keeps me sharp.

And they're good brain teasers for interviews, especially for data science of quantum.

Very good supplement to my interview, perhaps so highly recommended.

Also, if you're in school, you could supplement you're learning by using this website because there's a wide variety of topics which is pretty dope because I love you, I on the website, and it makes studying so much more fun with their quizzes.

If you're interested, there's a link in the description down below.

Best of all, you could get 20% off their premium subscription.

So YouTube, you can kill it at the interview for the large and title company.

Uh, now, I'm just gonna add some random clips.

So that extend this video to 10 minutes, I could get more ad revenue.

 So I guess I guess you can watch me play video games or something, like, just make sure my stream no e. 

字幕リスト動画再生

データサイエンスのためのプログラミング言語トップ3 (Top 3 Programming Languages For Data Science)

basically

exaggerate

brilliant

industry