In this tutorial, you will be introduced to the command line. We have selected a set of commands we think will be useful in general to a wide range of audience. […] after completing this tutorial, readers should be able to use the shell for version control, managing cloud services (like deploying your own shiny server etc.), execute commands in R & RMarkdown and execute R scripts in the shell.
If you want a deeper understanding of using command line for data science, the original authors suggest you read Data Science at the Command Line. Moreover, Software Carpentry has a lesson on shell. More references are listed at the end of the original tutorial. Use the clickable table of contents to quickly browse to the topic of your interest:
A while back I discovered this free game called Screeps: an RTS colony-simulation game specifically directed AI programmers. I was immediately intrigued by the concept, but it took me a while to find the time and courage to play. When I finally got to playing though, I lost myself in the game for several days on end.
Screeps means “scripting creeps.”
Basically, screeps is very little game. You start with in a randomly generated canyon of some 400 by 400 pixels, with nothing more than some basic resources and your base. Nothing fun will happen. Even better, nothing at all will happen. Unless you program it yourself.
As a player, it is your job to “script” your own creeps’ AI. And your buildings AI for that matter. You will need to write a program that makes your base spawn workers. And next those workers will need to be programmed to actually work. You need to direct them to go to the resources, explain them how to mine the resources, when to stop mining, and how to return the mined resources to your base. You will probably also want some soldiers and some other defenses, so those need to be spawned with their own special instructions as well.
Everything needs to be scripted well, as the game (and thus your screeps) runs on special servers, 24/7, so also when you are not playing yourself. Truly your personal, virtual, mini-AI colony.
Heck, it was fun while it lasted : )
PS. I read here that, using WebAssembly, one could also compile code written in different languages and run it in Screeps: C/C++ or Rust code, as well as other supported languages.
Many (aspiring) programming professionals competed in this challenge, sharing their learning journeys in domains from web development, machine learning, or data visualization.
With this blog, I wanted to share two of those learning journeys that stood out for me.
First, there’s Avik Jain’s 100 days of Machine Learning code repository on Github. Avik’s repository contains all learning activities he followed during the 53 days of programming he completed. Some of Avik’s entries really stood out, and I particularly liked his educational infographics:
Just look at the wonderful design and visual aids on this decision tree for dummies infographic, pseudocode and all:
Although Avik didn’t seem to have completed the full 100 days, many others did.
I have blogged about Hannah Yan Han‘s 100 days of code project before, but she definately deserves another mention here. Her 100 days revolved around data science, data visualization, and storytelling using both R and Python. You can find her #100DaysOfCode Medium page here, and her associated Github repository here.
For example, one day Hannah explored where instant noodles come from, how they are served, and whether people like them or not.
What I found so great about Hannah’s project is that she picked a novel dataset every couple of days. Moreover, she used a extremely large variety of different visualization formats. All visuals were equally beautiful, but Hannah made sure to pick the right one for the purpose she was trying to serve. If you are interested in data visualization, you seriously should check out Hannah’s 100DaysOfCode Medium page.