matlab界area

意見 (Opinion)

My personal interest in Data Science spans back to 2011. I was learning more about Economies and wanted to experiment with some of the ‘classic’ theories and whilst many of them held ground, at a micro level, many were also purely fictitious. Many of the themes that you learn: on how savings and Investment are directly related, or even on how Supply and Demand are directly related to price just didn’t hold true.

我個人對數據科學的興趣可以追溯到2011年。我正在學習更多關于經濟的知識，并想嘗試一些“經典”理論，盡管其中許多在微觀上站穩了腳步，但許多理論也完全是虛構的。 您所學習的許多主題：關于儲蓄和投資如何直接相關，甚至關于供求關系與價格直接相關的主題都不成立。

To be fully conclusive on my research though, I had to be sure that any conclusion I drew was purely unadulterated and spoke from the data. It’s all good and well that some notable academic from some notable University informs us about a theory, but is it actually true? Is it true everywhere?

但是，要完全確定我的研究結論，我必須確保我得出的任何結論都是純正的，并且是從數據中得出的結論。 某著名大學的一些知名學者向我們介紹了一種理論，這一切都很好，但實際上是真的嗎？ 到處都是嗎？

Collecting data wasn’t too hard but from there I had to teach myself programming. Python was free, easy to use, and the ‘new thing’ that people said to learn to ‘future proof’ my knowledge. However, after learning it and persuading a company to let me join their Graduate Scheme, I began to use Matlab at work.

收集數據并不難，但是我必須從那里自學編程。 Python是免費的，易于使用的，人們說要學會“證明未來”的“新事物”是我的知識。 但是，在學習并說服一家公司讓我加入他們的研究生計劃后，我開始在工作中使用Matlab 。

From here, I’ve always had this conflict where Matlab feels like a better language to work in but Python has always been my, you could say, ‘mother tongue’.

從這里開始，我總是遇到這樣的沖突， Matlab感覺像是一種更好的語言，但是Python一直是我的母語(可以說)。

The following article will highlight why after almost 10 years of experience in both languages, I feel that Python absolutely dominates Matlab in Data Science and also, why new Data Scientists should focus on solely on Python.

下一篇文章將重點介紹為什么在經過這兩種語言近10年的經驗之后，我感到Python絕對在數據科學領域主導了Matlab ，而且，為什么新的數據科學家應該只專注于Python 。

Python and Matlab are similar and different at the same time. Matlab was created as a private enterprise and as a closed form platform solution with a high price tag. On the other side, Python was created with ‘openness’ in mind to be easy and simple to use for all general tasks.

Python和Matlab相似又不同。 Matlab是作為一家私營企業和具有高價的封閉式平臺解決方案而創建的。 另一方面，在創建Python時考慮到了“開放性”，以使其易于使用和簡單地用于所有常規任務。

Matlab got a head start in the popularity contest as it was released in 1984 and despite me not being around back then, the various permutations and iterations of the language lent themselves well to the discipline of Mathematics. This is because vectors and multi-dimensional matrices are super simple to use in Matlab — a feature which only came in later at the time of Numpy (which was still kind of annoying) but Pandas has made using Python infinitely easier. Given that change, does my thesis hold true?

Matlab在1984年發布的人氣競賽中處于領先地位，盡管那時我還不在身邊，但該語言的各種排列和迭代方式非常適合數學學科。 這是因為向量和多維矩陣在Matlab使用非常簡單-該功能僅在Numpy出現時才出現(這仍然很煩人)，但是Pandas Numpy簡化了使用Python 。 有了這種改變，我的論文是否成立？

熊貓是Python開始超越Matlab的原因嗎？ (Was Pandas the reason why Python began to overtake Matlab?)

So we know that Pandas was first an internal library at AQR Capital and written my Wes McKinney and looking at Trends on Stack Overflow, we can see that from 2012 onwards, the percentage of questions with a Tag of Pandas began to increase where from 2015, there was a sharpe inflection point.

因此，我們知道Pandas首先是AQR Capital的內部圖書館，并寫了我的Wes McKinney并研究了Stack Overflow的趨勢，我們可以看到，從2012年開始，帶有Pandas標簽的問題所占的百分比開始從2015年開始增加，有一個尖銳的拐點。

Now notice here that since 2013/2014, the number of questions for Python as a langauge began to increase as well. Makes sense right? Pandas is a subset of Python, so naturally, the two are related, however …

現在請注意，自2013/2014年以來，作為語言的Python的問題數量也開始增加。 有道理吧？ 熊貓是Python的子集，所以自然地，兩者是相關的，但是……

source]源 ]

…since 2015, Matlab has been on a downward spiral. The proportion of questions which have a tag of Matlab has seriously been going down and why is that?

…自2015年以來，Matlab呈螺旋式下降。 帶有Matlab標簽的問題所占的比例已嚴重下降，這是為什么呢？

source]源 ]

Matlab昂貴且缺乏靈活性：學生負擔不起 (Matlab is Expensive and Inflexible: students cannot afford it)

Being a closed platform where every new library requires you to pay for the inbuilt functions means that things can become very expensive very quickly.

作為一個封閉的平臺，每個新庫都需要您為內置功能付費，這意味著事情會很快變得非常昂貴。

It’s pretty crazy but if you do practise Machine Learning in Matlab (as I have before) — you either have to shell out and buy a number of libraries (which have dependencies on each other), or you have to build everything for yourself from scratch.

這非常瘋狂，但是如果您確實在Matlab中練習機器學習(就像我以前一樣)–您要么必須掏空并購買大量的庫(彼此依賴)，要么必須從頭開始為自己構建一切。

This isn’t a necessarily a problem for those in industry with a big budget but if you’re playing with the latest deep neural network, or looking to create GPT-4, you’ll need to include several libraries and pay through the nose whereas in Python, you can use Tensorflow and Numpy for free.

對于預算巨大的行業人士來說，這并不一定是問題，但是如果您正在使用最新的深度神經網絡，或者正在尋求創建GPT-4，則需要包括多個庫并從頭開始而在Python ，您可以免費使用Tensorflow和Numpy 。

Also assuming you want to put into production any code that you’ve built, be prepared to build some form of a Matlab Wrapper or be prepared to code the final version in a different language. And yes, I’ve actually had to do that.

同樣，假設您想將已構建的任何代碼投入生產，請準備構建某種形式的Matlab包裝器，或準備使用其他語言編寫最終版本。 是的，我實際上必須這樣做。

Matlab and Java do work together but why bother learning two languages and building some weird symbiotic system when you can simply just learn Python and have an entire-tech stack in one language? It’s so much easier, so much more flexible and so much more cost effective.

Matlab和Java確實可以一起工作，但是當您只需要學習Python并用一種語言擁有整個技術棧時，為什么還要花時間學習兩種語言并構建一些奇怪的共生系統呢？ 它非常容易，靈活得多并且更具成本效益。

Photo by Tim Mossholder on Unsplash Tim Mossholder在Unsplash上拍攝的照片

Matlab幾乎沒有在線支持 (Matlab has little online support)

Also if the cost isn’t enough to deter you, the amount of Online support in Matlab land is far lower than you expect. Matlab does deserve some respect in that it’s Help functionality is better than most languages and usually solves most of the problems that you face but if you require some additional help on the internet, Python has roughly 100x more questions on Stack Overflow than Matlab does. That’s a lot less people to help you out, and yes, I’ve been stuck with a question that no one could solve before.

同樣，如果費用不足以阻止您，則Matlab領域的在線支持量遠遠低于您的預期。 Matlab確實值得尊重，因為它的“ Help功能比大多數語言要好，并且通常可以解決您面臨的大多數問題，但是如果您需要互聯網上的一些其他幫助，Python在堆棧溢出方面的問題比Matlab大約多100倍。 幫助您的人減少了很多，是的，我一直被困在一個沒人能解決的問題上。

There’s no free lunch but for all the reasons above, the use case for Matlab in Statistics and Machine Learning has been far reduced since Python got good with Pandas. I could go on all day about how much more useful Python is than Matlab but I’ll save you all the ear-ache.

沒有免費的午餐，但是由于上述所有原因，自從Python與Pandas相處以來，統計和機器學習中Matlab的用例已大大減少。 我可能整天都在談論Python比Matlab有用的多，但我會省掉所有的麻煩。

But at the same time, Python is free. It’s verbose and it has succeeded in many places where Matlab fell short. I thoroughly encourage all future Data Scientists or Statisticians to please let Python be your mother tongue.

但同時，Python是免費的。 它很冗長，并且在Matlab欠缺的許多地方都取得了成功。 我完全鼓勵所有未來的數據科學家或統計學家讓Python成為您的母語。

It’ll be the best decision you make.

這將是您做出的最佳決定。

Thanks for reading! If you have any messages, please let me know!

謝謝閱讀！ 如果您有任何留言，請告訴我！

Keep up to date with my latest articles here!

在這里了解我的最新文章！

翻譯自: https://medium.com/swlh/the-demise-of-matlab-in-data-science-bfb74e42dc8e

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