My lazy ass don't care

My beard can achieve what it wants to

What I want I can wear

My girlfriend I feel like she may sue

My lazy ass because she don't like it

But whether my beard was 2 inches or 10 feet long,

I don't give a shit.

At work, I be serving up dat food real fast

My legs be giving out, I might not last

'Till the end of my shift, the customers don't let up

And then my manager calls me to the hall

"Son, why don't you ever shave at all?"

I'm like man, I know you got an appreance to maintain,

and them customers don't like seeing a brown boy with mane

but I gotta keep it real inside;

without my beard I feel naked.

Although something I did not confide:

To me, I didn't give a fuck about what he or anyone said

You can't change me,

You can't display me,

As someone who don't cut his beard

For no one,

I preserve my manlihood and confidence

Over others' self-proclaimed common sense.

#mensrights

These days, everyone seems to be crazy about the Python programming language. You can use it for pretty much anything, whether you want to create a website, perform data analysis, create a video game, make interesting GUIs (graphical user interfaces, or as normal people say, windows with images and text in them), or machine learning. Currently, the two things I'm having exploring Python for are data analysis and machine learning, where you basically create a model, train it on some data, and see how well it can classify new things, kind of like teaching a kid to recognize plants from animals. I think that R is much better for both of those tasks.

When it comes to statistics, R is king. It is a STATISTICAL programming language, and indeed has many functions just for statistics, like mean(), median(), and sd() for standard deviation. It typically stores data in these things called vectors, which you can connect together and make matrices or dataframes. Matrices store the same data type (usually numbers) and dataframes store different data types across columns or rows. Dataframes make reading in data from external sources like kaggle.com easy, and let you get to manipulating data right away. In addition, R's minimalist and easy-to-read syntax can't be beat, and the fact that many different libraries (files with code you can access by telling the computer to import the functions in that file) are easy to download. R lets me get whatever I want up and running super fast so that I can focus on the creative aspect of what I'm doing.

Python, on the other hand, is a generalist language, a jack-of-all-trades. The only reason it is popular (or any language is popular) is because many people are developing libraries for it. These libraries have made Python available to do many different things, and thus it allows people like web developers and biologists to explore other related computer science areas like data science. Because of having to cater to so many different groups, to get a particular function from an external library you have to explicitly import it, and have in-depth knowledge of how the module (library) is structured. In addition, you need to use weird syntax to reference structures, functions, and classes. It's much less readable than R. Also, unlike R, dataframes are not native to Python. You have to import these modules called numpy and pandas to get anything close, and even these don't provide quite the same flexibility as they are in R.

I suspect that the reason why Python is starting to be preferred over R is because of speed: Python is a little faster. However, in terms of readability and maintainability, I think that the tradeoff isn't worth it, and more people should develop libraries for R, and provide more ways to speed it up to bring it to the level of Python.

If you are wondering how to break into computer science, my answer is this: if you want to do something with statistics, math, biology, or engineering, R is great. Otherwise, you can start with Python for a nice introduction to programming and its wonders. :)

#notmylanguage #programming #machinelearning #datascience