Math, Data &
Computing
Industrial designers a well-founded foundation in math, data analytics and logic. They should be able to identify and explain important aspects of their design with data visualisations and models.
Activities
To develop a sound base in ‘Math, Data and Computing’, I completed the basic courses ‘Calculus’, ‘Applied Physics’, and ‘Data Analytics for Engineers’ in the first year. This helped create a basic encyclopedia and skillset for future courses and projects. It allowed me to understand the work of students of other faculties, which was very helpful when I
collaborated with them during the course ‘Engineering Design’. In the second year, I successfully completed ‘Making Sense of Sensors’ where the team and I researched the influence of the number of steps taken during office hours on work comfort.
Examples
During the project for ‘Data Analytics for Engineers’, I collaborated with students from multiple different faculties. We designed a bike application that would help people of age with impaired hearing cycle more safely. I assisted with the calculations for the design and with the visualisations of the data.
During the course ‘Data Analytics for Engineers’, I learned to code with Python. I could create and document the software in a way it could be understood by others. This came in handy during the course ‘Making Sense of Sensors’. We mostly coded with Python. The team and I combined qualitative data (emotions) from a PANAS questionnaire with quantitative data (number of steps per time unit). The data were processed and analysed with Pandas (Python Data Analysis Library). We worked with Interactive Log-Likelihood MOdeling (ILLMO), LLP intersection method, Empirical Likelihood statistics and both robust and
non-robust EL methods. The data was plotted on histograms which were analysed in the paper. This is an example of my experience with statistical theory in a research process.
In Project 2 ‘The Interactive Museum’, we explored how to gather personal data from the visitors in a fun and entertaining way. This data would then be processed and analysed to get an insight into what type of visitors visit the museum. The project linked an analogue and tangible user experience with data. This data could then be plotted in graphs and other visualisations for the museum. This is an example of how I could apply my knowledge of math, data and computing in a project. We did not collect and process data real data during the project, but because of my background in math, data and computing, I know how to design for it.
Conclusion
I am by nature good at logic. I enjoy solving complex problems. From my studies, I realised that there is an interesting connection between math & logic and design thinking. Learning the basics of this expertise area helped me to practice problem-solving in an abstract, yet logical manner. It is all about structure. It is a practice of learning to understand the rules, and with that, exploring where the boundaries are. This allows discovering new ideas and insights.
In the future, I will not be a programmer or mathematic designer. But I will be able to collaborate with people who are like that because of my prior experiences in this expertise area. Because of my success in these courses, I proved that I am capable to solve mathematical problems, program in different coding languages and make mechanical and electrical calculations. But it has been an active choice not to because other aspects of design appeal more to me.