Vim is the Swiss-army knife of text editing. It’s not enough that it has a feature and command for almost every use case and user: it will also let you customize it to add whatever specific things you think it’s missing.
In this tutorial we’re going to see how to use two of those features: multiple windows, and multiple vim registers
Whether it’s testing the output of an API before deploying it to production, or simply fetching a response from a website (for instance, to check it’s not down), Curl is practically omnipresent.
SIMD Assembly instructions let you manipulate batches of data in parallel, in a single core. I’ve said it once and I’ll say it again: programming is the closest thing we have to magic.
To some, Vim is a beautiful relic from the past. To others, it’s that annoying thing you have to escape whenever you need to write a message for a merge commit.
Today I’ll introduce you to this picturesque text editor and its wonders, and show you why we’re still using it 26 years after its first release.
Machine Learning is the perfect dessert after a good couple days of Feature Engineering and Exploratory Analysis.
Let’s start from scratch on a real Kaggle Dataset.
Curiosity and Intuition are two of a Data Scientist’s most powerful tools. The third one may be Pandas.
As developers, there are lots of repetitive things we do every day that take away our precious time. Finding ways to automate and optimize those processes is usually very lucrative.
Sometimes, when facing a Data problem, we must first dive into the Dataset and learn about it. Its properties, its distributions — we need to immerse in the domain.
Today we’ll leverage Python’s Pandas framework for Data Analysis, and Seaborn for Data Visualization.
As developers, the terminal can be our second home.
However, we can’t use the shell until we learn how to, and need to practice using it to learn. Really — it’s a catch-22!
I hope this introduction will solve that puzzle for you. I want to help you start making use of the terminal right away.