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Kursusnavn (dansk):	Data design
Kursusnavn (engelsk):	Data Design
Semester:	Forår 2019
Udbydes under:	cand. it, digital design og interaktive teknologier (k-ddit)
Omfang i ECTS:	7,50
Kursussprog:	Engelsk
Kursushjemmeside:	https://learnit.itu.dk
Min. antal deltagere:	12
Forventet antal deltagere:	0
Maks. antal deltagere:	140
Formelle forudsætninger:	The course builds upon knowledge from the courses of the 1st semester of the DDIT (k-ddit) program. The students should have completed Advanced Digital Design Processes; Interactive Technologies; and Users in Context.
Læringsmål:	At the end of the course the students should be able to: – Conceptualize data and data visualization. – Discuss different debates about the implications of data visualization. – Identify the knowledge claims underlying different forms of data visualizations. – Apply data-visualization methods and tools in a data design process. – Reflect on the ethical and societal implications of data visualization in different contexts. – Develop and reflect on a data design process.
Fagligt indhold:	Data visualizations are used to get fast insight into a topic, to create powerful narratives about data, make connections visible, and to explore, discover and persuade. Analyzing, designing, and curating information into useful communication, insight, and understanding have become essential in our digital society. Data design has become a key component in how we understand our world. For digital design, data visualizations and data-driven design have become essential, but this has consequences. In this course, the students will learn how to conceptualize, visualize and present data but also to understand the consequences of data visualizations. The course encompasses data design as a circular process which moves between a) tools and methods to visualize data, b) the conceptualization of data and data visualization, c) application of data visualization and interpretation, and d) addressing its consequences. By understanding data design as a socio-technical process, the students will critically dissect data visualizations to explore their inherent social, ethical and cultural consequences. What data do we take into data visualizations? What do we leave out? How do we visualize data? What tools, techniques and methods can we use for visualizing data? And what consequences do these choices have? What ethics and values do we bring into data visualizations? How do these value systems inherently become part of data visualizations? And what are the larger consequences of data-visualizations as socio-technical products in a network society? These questions are essential when trying to understand data design and its societal impact. The course is built around four components exploring approaches to data visualization from different elements encompassing the data design process – tools and methods, theory and concepts, application, and consequences. Within the four components, the students will develop their own data designs. They will be introduced to data visualization tools (e.g. Tableau), but also conceptualize and think of the consequences of data design. In this course, the students will learn how to visualize data but also be enabled to critically reflect upon their choices as data designers – from a practical and conceptual perspective.
Læringsaktiviteter:	14 weeks of teaching consisting of lectures and exercises. – During the lectures we will introduce tools and methods for data-visualisation; as well as concepts, theories and debates. – Individual and group exercises during the exercise sessions where the students work with the data visualisation tools and methods; and discuss and reflect upon the data design process with the concepts, theories and debates introduced in the lecture. We will work with various small cases and current examples, publicly available data sources and manually collected data.
Obligatoriske aktivititer:	Two written mandatory assignments and active participation in class.
Eksamensform og -beskrivelse:	C: Skriftlige arbejder uden mundtlig eksamen., (7-scale, external exam) The written hand-in exam will comprise two parts: 1) A data visualisation applying the tools and methods introduced in class; 2) A reflection paper where the students use concepts and theories introduced in class to reflect upon their choices during the data design process, discuss its implications, and relate it to larger debates.