Sebastian-Coleman describe Data Representation as "a set of rules for recording data items." Data cannot be recorded haphazardly - in order to be used meaningfully, it has to be ordered properly. Therefore data representation, as one aspect of the structure of data, is important for maintaining quality and usefulness of data. This is further reinforced by the classification by Redman which Sebastian-Coleman point to: data representation encompasses qualities such as interpretability, portability, the precision and flexibility of format, the ability to represent null values, efficient use of storage and representational consistency. All of these values and qualities - rules and constraints on how we store data - help to ensure that we are creating *useful* data, not simply increasing the *volume* of data we have access to. Data are collected for specific purposes to represent a meaning beyond the actual bits of data themselves, which is the 'semiotic function' - "using data means interpreting data's meaning" because data is inherently representational and is not 'the thing itself' that we are drawing conclusions about. Data, and our ability to interpret data, provides us with information about something that exists outside of the data itself,

Inform 7 is an engine for creating interactive fiction that relies on an interpreter format. The early games Zork, as well as my parents' strange personal favorite Leather Goddesses of Phobos (fun fact: the latter game was referenced in the movie adaptation of Andy Weir's novel "The Martian") use an interpreter style - that is, text adventures where the player plays the game by entering commands and hoping that the command they enter is understood by the game. There is a standard vocabulary of commands that's used to create these games, and they have a few big advantages over choose-your-own-adventure formats such as what you can create in Twine. Characters that move from place to place, coherent movement systems built into the game itself, Inform 7 comes pre-loaded with a lot of these features already assumed and then lets you build on top of them.

“Feature Engineering” is an integral part of the data analysis process consisting of representing the underlying data objects in ways that are machine readable and can be understood by an algorithm. While this is a subject I’ve worked with in my job and studied in classes, I haven’t until recently taken a formal, direct approach to learning the theory behind this specific facet of my work. I acquired the book “Feature engineering for machine learning and data analytics” in a humble bundle quite a long time ago and have been starting to go through it. The book is focused on the theory and doesn’t have any specific exercises to go through, but I have been supplementing it by doing exercises I find on the internet using R code in RMarkdown.

Ignore this post, I’m just testing the repository. For some reason the post isn’t showing up properly, and I find this very irritating. Let’s go!