• any bugfixes or project updates will be posted here

Recently, you spent some quality time with your raw class status data and writing a script to scrape, process, and output meaningful results.

Here we will be taking that to the next step, in appealing to our more visual tendencies: you will be writing a script and coordinating the various tools necessary to graph your project results against the class high, average, median, and low scores for each project (effectively, a graph plotting 5 different trend lines).

Visualization has a number of uses, not only in computing, but in general: our minds are visual engines; we have phrases like “a picture is worth a thousand words”, and there is a considerable amount of truth to that. We can only process so much discrete data at any given moment, yet when there exist instances where we need to process considerably more data than we can take in, we turn to things like visual representations of the data.

By eliminating the exact, and potentially numerous discrete numeric values that would be impossible to keep track of (and formulate proper analyses of), by visually representing the data, both the general sense of the values are preserved, without overwhelming us, and allowing to take in a much broader picture that may be more challenging to do if all we had were an endless stream of numbers to evaluate.

This project has us taking that step, taking our data we now have experience in gathering, and plotting it against various class benchmarks, so we can better gauge our overall progress in the class.

For this project, we will be making use of the venerable gnuplot tool. Like many powerful tools we have encountered this semester, we seek only to scratch the surface, and start to familiarize ourselves with the powerful capabilities this resource offers us.

Following will be some usage examples to help you get a