You Ask, I Answer: Best Language for Marketing Data Science, R or Python?

In today's episode, Maria asks, which is the best language to learn for 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 R is the better language.

Because R is purpose built for statistics. It was originally a statistical language. And SPSS had its own programming language called S. And it was very good, did a lot of uh great things. It is was also a very expensive product that a lot of folks in academia could not afford.

So as happens in the open source world, someone said, okay, let's try and replicate the functionality without having to um 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 R. Uh, and that the design intent of R was to replicate the statistical language of SPSS. And so if you're doing data science and you're doing a lot of stats heavy work, I think uh R is the better language to learn. Now, if you're going to be doing machine learning, particularly deep learning, deep neural networks uh of all kinds, uh, and you want to be using the most advanced stuff, the a lot of that code and a lot of those languages 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 Python and you can and work with it, you'll have a uh easy time getting started with those those particular libraries because it's just familiarity with it. So it depends. Now here's the catch. I would personally recommend R 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 know, just uh uh like a spreadsheet, you have to do what's called a loop, where you have to loop through each row and perform operations on each row in order to be able to you know process the data and get an answer. R can address the entire table all at once, so you don't have to to write code to loop, you just reference the whole table and apply a function to that table. You want to add one to every every uh number in a column. It's a very so 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 you can't run the latest uh machine learning libraries, is perceived only. There's a package uh in R called reticulate that allows you to run Python libraries and Python code inside of R and natively written with R. So you don't have to learn Python. You just need to know what are the reference points for the different functions and and features you're trying to use, and you can use Python within R.

So there's that limitation is largely gone. Now there may be some uh unique oddities here and there, as with any kind of port or any kind of uh conversion of languages, but for the most part, it's pretty straightforward. The other thing that is useful is that R supports, you know, it's your standard types of notebooks, Jupyter notebooks and things like that. And many of the uh leading data science platforms and tools and stuff uh 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 um IBM Watson Studio, you can run a Jupyter notebook that has Python code in it, that has R code in it, that has SQL code in it, and interchange which languages you're using, especially if you are fluent in one language more than more so than another, you can step out of the language you're comfortable in, write the few lines of code that you absolutely need in the other language, and then step back into the language you're comfortable with and be able to run those heterogeneous code blocks all within one environment. So it's very, very powerful. Um, all these notebooks that a lot of data scientists use, um, very very powerful tools that don't limit you to one language. That said, R functions a lot more from a syntax perspective, like uh older languages like C, for example. So if you are comfortable with those more traditional programming languages, you'll do better with R.

From a mindset perspective, if you like the more uh freeform, almost language-based style of programming, um very object-oriented, then Python, you're gonna you'll you'll enjoy Python better. I being somebody who has a little more gray hair than than uh some of my compatriots, uh, lean towards R. Because I grew up, you know, learning C, learning Java, learning uh PHP, learning these older languages that have you know much more rigid syntax, uh, and I do better in those environments. Um, I don't do nearly as well in 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 were once you've got the basic syntax down of both languages, try writing uh a straightforward piece of code that you know, say just does a very simple like linear regression, right? Very, very simple, but try and do it from memory and try and do it um 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 gonna be different for every person, it's going to be based on your preferences and 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 uh that is something that that's what should make your decision is what you're most comfortable with, because all these 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 in another, at most it's like three to six months before the machine learning community is like, oh, I want that too, and they and someone makes a port of it. Pick what is cut most comfortable for you when it comes to languages for marketing data science.

Really good question, an important question. Uh, if you have follow-up comments, please leave them in the comments box below. Subscribe to the YouTube channel and the newsletter. I'll talk to you soon. Take care.

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