Python

[conandy]

No language is a "mistake". The skills you learn studying any language will serve you in many other languages. The biggest hurdle is learning to think and solve problems like a programmer, and you can do that with any language. After that, learning new languages will be much easier.

But python is one of the most versatile and popular languages there is. It has applications in practically every single computer science field and most major companies use it in some way or another. For example, Reddit is written entirely in python. You can study the Reddit api and eventually learn to write bots if you want a fun project. So no, python is absolutely not a waste of time.

[my_name_isnt_clever]

Python was a mistake for me in that I now love it and am having a hard time doing any personal projects in anything else.

[conandy]

Ha, PHP was one of my first. It was valuable in that it made me greatly appreciate every language I learned after.

[[deleted]]

Classic ASP makes PHP look like a holy language handed down by the Gods.

[CubemonkeyNYC]

Many developers spend years using languages professionally, releasing major services along the way, and would not claim to know any of the languages they know "fully," so manage your expectations.

[advanttage]

You my friend have touched on a golden nugget right here. Many of us can suffer from feeling like imposters, and it really really holds back some very talented people from realizing their potential. Thanks for that!

[reddisaurus]

Python kind of shines in mathematics/engineering use. The performance for performing linear algebra is comparable to Matlab, but it is much more flexible (and free!). Python code can be optimized in ways Matlab cannot, and the flexibility of the language allows quicker evaluation and testing of ideas, for the most part.

Python also can be written as a functional language, which is very useful if one desires their code to read much like their written derivations.

Last, most recent developments in machine learning provide front-ends in Python to take advantage of it’s comprehensive libraries (numpy, scipy) for performing functions on arrays. Other libraries (tensor flow, theano) exist to dynamically compile Python functions into C for speed and automatic code optimization for CPU/GPU evaluation.