During the programme
The MDDD curriculum consists of five learning tracks: Human AI Interaction, Ethical Design, Data Science, Applied Research, and Critical Thinking and Data Studies. Each block focuses on a step in the design process and incorporates various knowledge and skills from these learning tracks. During the four ten-week blocks, you will work on projects, follow workshops and lectures within these tracks.
The curriculum is based on the demands and challenges of the professional field. This means you can work on genuine, data-driven solutions based on your passions, personal interests, and (domain) expertise. In this setting, you quickly develop a good sense of what you have to offer as a data-driven designer and how to position yourself within a multidisciplinary team of professionals.
Design intelligent interfaces (e.g., adaptive systems, recommender systems, chatbots) using data, machine learning, and AI.
Use value-sensitive Design and collaborate closely with stakeholders considering the ethical aspects and human values within a data-driven project.
Use data and machine learning to learn about how users interact with a product, app, service, or system, and use those insights to enhance the Design of the user experience.
Organisations face data challenges and are always seeking new innovative insights. You, as a student, can offer a fresh perspective gained through the master's programme's learning tracks. During the programme, you will work in a team on a real-life project, provided by an external organisation or research group. You will apply data analysis and problem-solving skills to help organizations overcome challenges. By collaborating with professionals from various industries, you will gain practical experience and contribute to growth and innovation. You will use this project as your case study for the programme. This gives you the opportunity to develop new skills and master the learning competencies in a professional context. You will be paired with a client project coach, a skilled member of our lecturer team to help guide you through this process.
Reflect and critique how datafication affects and interacts with individuals, organizations, and society.
Applied Research project
Organisations face data challenges and are always seeking new innovative insights. You, as a student, can offer a fresh perspective gained through the master's programme's learning tracks. During the programme, you work in a team on a real life project, provided by an external organisation or research group. You will use this project as your case study for the programme. This gives you the opportunity to develop new skills and master the learning competencies in a professional context.
Content of the Master Data-Driven Design
The program consists of four blocks, each ten weeks:
During the Exploration Phase, you will participate in a four-week bootcamp to level up your knowledge and skills in all five tracks. After the bootcamp, you will select a client brief and conduct exploratory research on the problem it presents with the help of resources given to you in the five learning tracks. At the end of the block, you will be able to present your unstructured research findings in a problem space presentation which challenges the original brief and shows different perspectives on the problem.
In this phase, you will define and frame your research problem by transforming your research findings into a workable problem. You will bring order to the chaos of empirical data by transforming it into actionable knowledge that reveals meaning in the observed behaviors, preferences, values, etc. gathered during the investigation phase. This understanding will allow you to recognize the opportunities and constraints that will shape the scope for developing solutions. At the end of this phase, you will deliver a definitive project brief that provides more context than the original brief, presents a final problem statement, and pinpoints possibilities and limitations that set the space for generating solutions in the next phases.
In deze fase bedenk je oplossingen voor het eerder geïdentificeerde probleem. Je zoekt inspiratie uit verschillende bronnen, schetst ideeën, wireframes en ontwikkelt low-fidelity prototypes van haalbare oplossingen. Je deelt creatieve resultaten, werkt samen met deskundigen, vormt hypothesen en ontwerpt experimenten voor ideeëntesten. Aan het eind moet je één of meer ideeën hebben om te prototypen en te testen. Tevens lever je een reflectie op het proces en een projectuitvoeringsplan voor de laatste fase.
In deze fase valideer je werkende oplossingen en bereid je het ontwerp voor op implementatie bij de opdrachtgever. Je elimineert minder geschikte oplossingen, analyseert de beste, en bepaalt de implementatiemethode. Dit gebeurt via een agile methode: bouwen/prototyping, testen/analyseren en itereren/herhalen. Je sluit af met een laatste pitch en presentatie van resultaten aan assessoren, opdrachtgever en collega's.
Want to know more about this programme?
This full-time programme expects you to be available to engage in educational activities at the University for forty hours a week.
Students and lecturers form an active learning community in which we aim to develop strong social cohesion. You will be expected on campus for an average of three days a week, plus two days of self-study. There will be lectures, the Coding Club for extra support with coding assignments, the Writing Club for extra support with academic writing and several guest lectures. At times, you will participate in design sprints and pressure cookers, during which you work in a team on design processes.
This programme follows programmatic learning, which focuses on meaningful reflection and feedback. The primary function of assessment within programmatic assessment is to guide and stimulate your development process as a student, especially through feedback. The 5 learning tracks all relate to each other, and you will have one formative assignment per block that tests you on the learning outcomes for all 5 tracks.
The Data-driven Design lecturers
Bob is senior lecturer and coordinator at the BA Communication and Multimedia Design programme, as well as at the master Data-driven Design.
Senior Lecturer & programme Coordinator
Erik has a bachelor's degree in Information and Communication Technology (software engineering) as well as a master's degree in Media Technology (art and technology). He joined the HU in 2005 and has worked on a variety of research projects as well as novel educational programmes that combine data science, media, and design. His research interests include interactive machine learning, computational text analysis, and democratizing data science. Erik also teaches at Utrecht University's master's programme in Applied Data Science, where he teaches students how to design and develop recommender systems for private and public media.
Rhied Al-Othmani has diverse graphic design experience, having worked on projects ranging from print to web and branding for a wide variety of clients.
In 2011, Rhied received her BA in Digital Communication from HU and in 2020 her MA degree in Data-driven Design. Her enthusiasm for user experience, combined with an insatiable thirst for knowledge, drew her gradually into research and education. Along with her current position as a lecturer at BA CMD, the Master, she is also the Study Career Counsellor for the students and a member of the Study Programme Committee.
Her other interests are the development of conversational agents, open government data, and the knowledge and tools necessary to comprehend, interpret, and apply open data.
Shakila Shayan, PhD
Shakila is a lecturer at the master Data-driven Design as well as a researcher at the research group Human Experience & Media Design.
Shakila is originally from Iran, where she completed her BA in Computer Engineering. She then moved to the US to pursue a master and PhD in Cognitive Science and Computer Science at Indiana University. She is deeply passionate about understanding the needs of individuals and communities as a whole and finding innovative solutions to help them. As a result, her research has gradually shifted towards an applied human-centred approach that can improve the “here and now” of people’s lives.
Jonas is a lecturer at the master Data-driven design and the bachelor Communication and Multimedia Design, where he teaches mostly the machine learning, data science and research methods courses. He has an academic background in artificial intelligence and cognitive neuroscience.
Currently, Jonas holds a minor position at the research group Artificial Intelligence, where he initiates student projects that focus on applying machine learning to creativity and art.
Roelof de Vries
Lecturer & Researcher
Dr. Roelof de Vries is a lecturer at the master Data-driven Design as well as a researcher at the research group Human Experience & Media Design where he investigates behavior change technology and the evaluation and validation of these technologies. Currently, he focuses on the interdisciplinary design process of behavior change technology. How can we and do we want to evaluate and validate behavior change technology? How can we design behavior change technology so that it helps tackle the growing complexity of health and wellbeing challenges?