Course syllabus autumn 2024
Course syllabus autumn 2024
Title
Data Analysis and Visualization
Swedish title
Dataanalys och visualisering
Course code
ME659E
Credits
15 credits
Grading scale
UA / Excellent (A), Very Good (B), Good (C), Satisfactory (D), Pass (E) or Fail (U)
Language of instruction
English
Decision-making body
Faculty of Technology and Society
Syllabus approval date
2021-05-28
Syllabus valid from
2021-08-30
Entry requirements
- Bachelor degree in media technology or in a related field with a minimum 180 European Credit Transfer System credits. Examples of related fields include: computer science, informatics, information systems, human-computer interaction, interaction design, media/IT management, game development, media and communication science and digital cultural studies.
- English proficiency equivalent to English 6 from a Swedish upper secondary school
Level
Advanced level
Main field
Media Technology
Progression level
A1N
Progression level in relation to degree requirements
The course is part of the main area of study media technology and can be included in the degree requirements for a Degree of Master of Science in Media Technology (120 credits).
Course objectives
In the course, students advance their knowledge and understanding of qualitative and quantitative methods and methodologies for data analysis. The course focuses on the use of data and visualization techniques to analyze complex phenomena and to build models to support research or decision making related to media technology.
Course contents
- qualitative and quantitative methods for data-driven analysis, such as data mining, network analysis, content analysis and digital ethnography,
- processing and analysis of various forms of data,
- data visualization and modelling,
- statistical concepts and principles,
- tools for data-driven analysis and visualization.
Learning outcomes
Knowledge and understanding
By the end of the course students should be able to:
- describe applications, challenges and opportunities with different forms of data collection and analysis in digital environments,
- describe how the data source and choice of method affect the possibilities for, and the quality of analysis.
Competence and abilities
By the end of the course students should be able to:
- relate theoretical concepts to applied problems,
- plan and perform qualitative and quantitative analyses in digital environments based on scientific and strategic objectives,
- identify and apply different principles and tools for collecting, analyzing and visualizing data,
- present and critically discuss data analyses and the consequences of methodological choices and conditions.
Evaluation abilities and approach
By the end of the course students should be able to:
- argue for how different methods and methodologies can allow for a more complex understanding of phenomena in digital environments,
- justify the choice of methodology, taking into account both concrete problems and broader scientific perspectives,
- critically discuss applications of data-driven analysis with respect to ethical, integrity and social issues.
Learning activities
The course consists of lectures, seminars, workshops, laboratory and project work.
Assessment
Grading is based on oral and written examination, including active participation in seminars and project work.
The course is assessed based on the following:
- Seminars (2.5 credits, UG)
- Lab (2.5 credits, UA)
- Written assignments (5 credits, UA)
- Project (5 credits, UA)
For a pass (A-E): At least grade E on lab (2.5 credits), written assignments (5 credits) and project (5 credits) and grade G on seminar participation is required. Course grade is calculated based on the weighted average of assessed parts.
Course literature and other study material
- Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. New Riders.
- Drucker, J. (2020). Visualization and Interpretation: Humanistic Approaches to Display. MIT Press.
- Kirk, A. (2019). Data Visualization. Los Angeles: SAGE Publications. (2nd edition)
- Rogers, R. (2019). Doing Digital Methods. Los Angeles: SAGE Publications.
- Salganik, M. J. (2017). Bit by Bit: Social Research in the Digital Age. Princeton: Princeton University Press. (alt 2019, soft cover)
Recommended literature:
- Jewitt, C. (Ed.). (2017). The Routledge handbook of multimodal analysis. New York: Routledge.
- Rogers, R., Sánchez-Querubín, N., & Kil, A. (2015). Issue mapping for an ageing Europe. Amsterdam University Press
- Tukey, J. W. (1977). Exploratory data analysis (Vol. 2, pp. 131-160).
- Venturini, T., Munk, A., & Meunier, A. (2018). What's in a data-sprint?. Routledge Handbook of Interdisciplinary Research Methods.
Course evaluation
The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).
Interim rules
When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.
Additional information
The syllabus is a translation of a Swedish source text.