Maria asks, “Which is the best language to learn for marketing data science, R or Python?”
It depends. For data science, in which you’ll be doing a lot of statistics-heavy work, R is the better language. For machine learning, especially deep learning, Python is the better language. So it depends; that said, I would personally recommend R across the board. With the Reticulate package (that permits use of Python libraries and code in R), there’s no limit to what you can do with it, and for pure mathematics, R is purpose-built. Ultimately, it’s up to how your brain works. Watch the video for explanation.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Maria asks, which is the best language to learn from marketing data science, R or Python? So the answer to this question depends, it depends on a bunch of different things.
Number one, what you’re going to be doing if we’re talking pure data science where you’re going to be doing a lot of very statistics heavy work.
I personally believe our is the better language.
Because our is purpose built for statistics.
It was originally a statistical language, very quick bit of history.
There was a company and a product called SPSS, which is now owned by IBM full disclosure, my company’s an IBM Business Partner.
And SPSS had its own programming language called s.
And it was very good did a lot of great things is was also a very expensive product that a lot of folks in academia could not Ford.
So, as happens in the open source world, someone said, Okay, let’s try and replicate the functionality without having to, to pay at the time the SPSS company exorbitant amounts of money for their software.
And so a bunch of scientists and a bunch of coders came up with our, and that the design intent of our was to replicate the statistical language of SPSS.
And so if you’re doing data science, you’re doing a lot of stats heavy work, I think AR is the better language to learn.
Now, if you’re going to be doing machine learning, particularly deep learning deep neural networks of all kinds, and you want to be using the most advanced stuff, but a lot of that code and a lot of those Lang languages and libraries are going to be in Python.
Python is the native language for a lot of those things that they’re written in.
And if you can read my Thought and you can and work with it, you’ll have an easy time getting started with those those particular libraries because, you know, it’s it’s just familiarity with it.
So it depends.
Now here’s the catch, I would personally recommend are for data scientists across the board.
Again, it’s designed for statistics.
It’s designed for mathematics and the way it handles certain types of data.
And the way it applies functions to them are much more efficient than other programming languages.
A real simple example that in Python, and many, many other programming languages, if you have a table of data, you don’t just have like a spreadsheet.
You have to do a call loop where you have to loop through each row and perform operations on each row in order to be able to process the data and get an answer our can address the impact Higher table all at once.
So you don’t have to write code to Luke, you just reference the whole table and apply a function to that table.
Want to add one to every every number in a column, it’s a very, it’s one line, you know, the table, the column, you know, and then plus one.
And so for data science, it is a very efficient language.
And the perceived disadvantage that R has, which is that can’t run the latest machine learning libraries is perceived only.
There’s a package in our called articulate that allows you to run Python libraries and Python code inside of our and natively written with our so you don’t have to learn Python.
You just need to know what are the reference points for the different functions and features you’re trying to use.
And you can use Python within our so there’s that limitation is largely gone.
There may be some unique oddities here and there, as with any kind of Port, or any kind of conversion of languages, but for the most part, it’s pretty straightforward.
The other thing that is useful is that our supports, you know, it’s your standard types of notebooks, Jupiter notebooks and things like that.
And many of the leading data science platforms and tools and stuff, support this as well.
So if you’re comfortable in both languages, you can write code back and forth and pass variables back and forth inside the same environment.
For example, in IBM Watson Studio, you can run a Jupiter notebook that has Python code that has our code in it that has SQL code in it.
And interchange which language is using especially if you are fluent in one language more than most other than another.
You can step out of the language you’re comfortable in quite a few Latin lines of code the absolutely need in the other language, and let’s step back into the language.
You’re comfortable And be able to run those heterogenous code blocks, all within one environments is very, very powerful.
All these notebooks that a lot of data scientists use very, very powerful tools that don’t limit you to one language.
That said, Our functions a lot more from a syntax perspective, like older languages like C for example.
So if you are comfortable with those more traditional programming languages, you will do better with our mindset perspective.
If you’d like the more freeform almost language based style of programming.
Very object oriented than Python, you’re gonna you’ll you’ll enjoy Python better.
I being somebody who has a little more gray hair than then so my compatriots lean towards our because I grew up you know, learning Learning Java learning, PHP learning these older languages that have, you know, much more rigid syntax.
And I do better in those environments.
I don’t do nearly as well.
And in Python.
If you’re starting from scratch, try out both and see which language you prefer.
And it will, it will depend.
What I would say is if you once you’ve got the basic syntax down of both languages, try writing a straightforward piece of code that, you know, say just as a very simple like linear regression, right? Very, very simple.
But try and do it from memory, and try and do it.
googling as little as possible and copying and pasting as little as possible and see which one feels more natural to you which one feels like okay, I can do this.
So, and that will give you an indication of which of the languages is the better choice for you personally to pursue.
It’s going to be different for every person.
It’s going to be based on your preferences.
how your brain works and what you are comfortable with? And what makes sense to you.
There is no right answer with any of these data science tools.
There’s no one answer that works for everybody.
There are answers that best fit who you are as a person, the way you work, perhaps even the type of company you work at.
And that is something that that’s what should make your decision is what you’re most comfortable with.
Because all the languages all these tools and technologies within the data science and the machine learning communities are being ported back and forth to each other.
If a tool becomes available in one language that isn’t available and another at most, it’s like three to six months before the machine learning community is like, Oh, I don’t want that too and they want to make support of it.
So pick what is cut most comfortable for you when it comes to languages for marketing, data science, really good question and important question.
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