Beautiful is better than ugly.

Explicit is better than implicit.

Simple is better than complex.

Complex is better than complicated.

Flat is better than nested.

Sparse is better than dense.

Readability counts.

Special cases aren't special enough to break the rules.

Although practicality beats purity.

Errors should never pass silently.

Unless explicitly silenced.

In the face of ambiguity, refuse the temptation to guess.

There should be one-- and preferably only one --obvious way to do it.

Although that way may not be obvious at first unless you're Dutch.

Now is better than never.

Although never is often better than right now.

If the implementation is hard to explain, it's a bad idea.

If the implementation is easy to explain, it may be a good idea.

Namespaces are one honking great idea -- let's do more of those!

What are my other choices for a vectorized statistics language. Professor was pushing SAS Jump (or was that SPSS) with a menu-driven point and click approach. (Reproducibility can still be accomplished, you publish the script generated by all your clicks.) But I want to type everything, great online tutorials for R. I think I made the right pick.

To me, this is by far the best programming language. Why? Because it’s the only language that really got me going after trying to get into programming with Java for a while. Python is powerful, easy to learn, and gets you to unsderstand other languages more once you understand it. Did I state I love the python language? Well, I do..

Backend server for analysis of image samples from iPhone microscope lens. Chose this because of familiarity. The number one thing that I've learned at hackathons is that work exclusively with what you're 100% comfortable with. I use Python extensively at my day job at Wit.ai, so it was the obvious choice for the bulk of my coding.

been a pythoner for around 7 years, maybe longer. quite adept at it, and love using the higher constructs like decorators. was my goto scripting language until i fell in love with clojure. python's also the goto for most vfx studios and great for the machine learning. numpy and pyqt for the win.

Large swaths of resources built for python to achieve natural language processing. (We are in the process of deprecating the services written in python and porting them over to Javascript and node)

I have been study with videos and tutorials

• https://www.youtube.com/watch?v=L_GvP5XTJj4&list=PL8eBmR3QtPL3pDzQpwPYfWQ4NEPGu6j7z

Connect to database, data analytics, draw diagram. Machine Learning application, and also used Spark-R for big data processing.

Visualisation of air quality in various rooms by RShiny (hosted free on shinyapps.io)

Backend logic (REST, Auth0, Redis, Postgres, functional graph library (fgl), ...)

Our internal deployment tool, called Mordor, is written in Haskell.

Still experimental work for multikernel OS.

R is primarily used by SESYNC's researchers

Offline deep analytics and modeling

Don't worry, be type-safe.