Math, data and computing is the mastering of data. being able to collect it, filter it, analyze it, visualize it and extract useful conclusions from it. Both quantitative data and qualitative data are essential for the design process, to be able to correctly identify and validate your designs and your users needs. It is not only about gathering data from real world sources but also through digital twins and simulations. Creating a digital alternative in which more parameters can be taken into account, being able to concretely see the links between them. A beautiful intersection exists in the realm of machine learning where the behaviors of a human can be sensed and then integrated into a learning algorithm that will change and base the experience based the habits and preferences of the user.
My first year at Industrial Design MD&C felt completely and utterly intimidating. Having gone through Calculus, Physics and foundations of data analytics, and needing to apply that knowledge again in Making sense of sensors. I had not yet seen the potential that it had within a design process. Until I started with my third project in my second year. There, I saw the potential that it had in an aesthetic sense through the integration of creating visuals with fractals and a regenerative data stream. Or the use of qualitative data to explore and see opportunities within communities where design could make a positive impact. Now, seeing qualitative data like that gained from a cultural probe study or an interview not as something to only be integrated within U&S, but to be methodologically approached through MD&C. This has given me a lot more steadfastness in my ability to use MD&C.
Future Development
I do still feel a weakness here and thus would like to follow an extra elective in this expertise area next year to solidify my position within this expertise area.
