This expertise area is important, because it can help understand the context for which you’re designing. To design, you often need to analyse quantitative data, usually large amounts in order to be reliable. There are numerous ways to do this, but during my study I have learned how to use Python with the Pandas plugin running in Jupyter Notebooks and R in Rstudio. Both are useful tools to perform a statistical analysis with large amounts of data. It could be useful to analyse multiple kinds of data. When there is too much data (thousands of entries) to analyse and manipulate by hand in a tool like Excel, this is a necessary step. Like in the course Making sense of sensors, we had large amounts of heart rate, temperature, humidity and sound levels to analyse, which already meant we had to use Python. But cleaning the data to get it into a simple format to use, proved to be half of the work already. Something like that just is not possible using a tool like Excel.
However, something that Excel is pretty strong with, is handling smaller amounts of data live when it is being added to the dataset. This relates to an extracurricular activity of mine I am currently doing. I recently became the treasurer of a music association in Maastricht, of which I have been a member for over 10 years. I aimed to make my work as treasurer as easy and convenient as possible, so I created an Excel file to which I only have to enter any transaction as they happen. I enter the date, amount, category, and to or from who it is, and the rest of the bookkeeping happens automatically. It automatically keeps a table with the sums of categories and months up to date, which needs to be sent for every board meeting. It automatically tracks how much contribution was payed by everyone, so I have an overview which is always up to date. It keeps track of the balance on the bank account so I can double check if everything is correct, the same for the cash flow. By just entering the transaction on my phone, I can keep my work up to date and never have to spend much time on preparing any financial overviews for the board meetings, or when sending reminders about paying contribution.
Programming is not only used for data analysis, in design it is used even more to create products and prototypes. An example is when creating digital prototypes, like when I coded webpages for project 2 or made a (simple) game in Unity. Or when using Arduino in a design prototype, like I did during the course exploratory making or for my demonstrator of my final bachelor project. While this can also be considered to be for the expertise area of Technology & Realization, I consider the programming part of it to be relevant to the expertise area of Math, Data & Computing as well, because it involves a lot of logic.
My work during the course intelligent interactive products is similarly in between these two expertise areas. On the one hand, the creation of the prototypes and incorporating the electronics is more related to Technology & Realization, but the usage of Machine Learning models is more related to Math, Data & Computing. It also involves large amounts of data to train a model used to make a product interactive, with the only difference being that the data is analysed by a classifier rather than by the designer. However, as a designer you still need to understand the classifier and what it does, because there are numerous classifiers that can be used to train a model. Depending on the sensor used and data processing performed, some classifiers will be much more reliable than others. It is the designer’s job to understand why and use the best classifier for the job.
Creating a CAD model using software like SolidWorks can also be considered relevant to this expertise area. Although what I did in my final bachelor project is more relevant to Technology & Realization, I would be able to use the things I learned about SolidWorks to perform a structural or mechanical analysis when that would be required, by running simulations in the software.