We code to learn

Across STEM, questions worth asking have outgrown the reach of pen-and-paper methods. For instance, large datasets cannot be manipulated by hand and large-scale simulations cannot be run by hand. Computation has therefore become an essential part of many disciplines rather than an optional tool, and formal thinking, long equated with mathematical thinking, now requires computational thinking as well.

Our mission is to develop computational thinking, research skills, and technical communication by guiding every member through an original computational research project and the publication of a computational essay in the Wolfram Language.

1. We code to learn, not learn to code. Coding is our means of thinking and problem-solving rather than the object of study. We use computation to investigate questions that belong to our own respective fields of study.
2. We are not a coding club; we are aspiring computational researchers. Our goal is to effectively investigate and solve real-world problems using computation rather than to develop the most sophisticated software programs.
3. We produce knowledge rather than consume it. Every member produces an original piece of work and publishes it publicly.
4. The distance between having an idea and computing it should be as small as possible. Wolfram Language support and faculty guidance help members turn ideas into original research.

The Wolfram Language is a computational language for expressing ideas in a form that humans can understand and computers can process. It combines built-in algorithms and curated data with a symbolic structure that treats data, code, and graphics as symbolic expressions. Its readable and consistent syntax helps members focus on research questions and communicate their methods clearly.

A computational essay is an intellectual narrative that weaves human language, code, and computer output together to communicate an idea. The author explains the ideas in prose, writes code in the Wolfram Language to specify it precisely, and interprets the results. Text, code, and output work together to develop one coherent argument, making the reasoning visible and allowing the computation to support the author’s claims.