Faculty
António Rui Trigo Ribeiro
Objectives
Technical competences:
LO1: Acquire skills in problematising and testing creative and informed solutions.
LO2: Apply user-centred data analysis.
LO3: Create clear and accessible presentations and data visualisation support.
LO4: Formulate clear means of promoting the use of environmentally-friendly means of mobility, inclusion in the labour market of people with neurodiversities, based on a challenge from a PA.
Interpersonal skills:
LO5: Develop international multidisciplinary LbD experience, teamwork and respect for colleagues' opinions.
LO6: Co-construct solutions based on critical thinking, creative problem-solving, collaboration, critical observation, negotiation and collaborative decision-making.
LO7: Apply strategies to propose thoughtful solutions, autonomous work based on research into solutions and sustained construction of arguments.
LO8: Develop oral and written communication skills and technical discussion skills.
Program
PC1. Learning by Developing for Students
PC2. Futurism – cities foresight
PC3. Information, data visualisation, and accessibility for user decision-making
PC4. Design thinking for envisioning cities futures with a focus on inclusive social design
PC5. Sensing mobility data
PC6. Transport innovation – sustainable urban development
PC7. Human-centered artificial intelligence
Evaluation process
Assessment will prioritise student participation and the quality of their work throughout the course. The assessment criteria include:
1) in-depth analysis of the challenges proposed by the APs,
2) critical analysis and synthesis of concepts,
3) iterative development of intermediate and final solution proposals,
4) active participation in class, namely with tutorial sessions in groups of students and tutor,
5) effective communication and defence of the final solution proposal in front of a mixed jury made up of teachers and representatives of the APs.
Group presentation and written problem-solving report for the Hackathon “Future Cities from Vivid Reality: Inclusive and Sustainable Challenges in the Lisbon Metropolitan Area”. During the Hackathon a jury will decide on student groups who will win prizes. An individual final grade will be attributed by the teachers supported on the delivery on the Hackathon and in the active participation on the online and onsite sessions.
Bibliography
Mandatory Bibliography
Lewrick, M., Link, P., Leifer, L. (2018)The Design Thinking Playbook: Mindful Digital Transformation of Teams, Products, Services, Businesses and Ecosystems, ISBN-10: 1119467470, Wile
Ketonen-Oksi, S., Vigren, M. (2024). Methods to imagine transformative futures. An integrative literature review., Futures, https://doi.org/10.1016/j.futures.2024.103341.
Lima, M. (2017) The Book of Circles: Visualizing Spheres of Knowledge. Princeton Architectural Press. New York.
Evergreen, S. (2016). Effective Data Visualization: The Right Chart for the Right Data. SAGE Publications Ltd.
Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press.
Wickham, H. (2015). ggplot2: Elegant Graphics for Data Analysis, Springer, https://ggplot2-book.org/
Learning by Developing LbD, https://www.laurea.fi/en/laurea/laurea-as-a-university/learning-by-developing-lbd/
Optional Bibliography
Ware, C. (2012). Information Visualization: Perception for Design (3rd ed.), Morgan Kaufmann
Shneiderman, B. (1996). The eyes have it: a task by data type taxonomy for information visualizations. In Proceedings of the 1996 IEEE Symposium on Visual Languages (pp. 336-343).
I. Foster, R. Ghani, R. S. Jarmin, F. Kreuter, J. Lane. (2016). Big Data and Social Science: A Practical Guide to Methods and Tools, 1st Edition. CRC Press.
P. Tattar, T. Ojeda, S. P. Murphy B. Bengfort, A. Dasgupta. (2017). Practical Data Science Cookbook, Second Edition. Packt Publishing.
P. Mathur. (2018). Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance. Apress.
Cleveland, W.S. (1987b). Research in statistical graphics, Journal of the American Statistical Association, 82, 419-423.
Provost. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly.
O’Reilly. M. N. Jones, (2016). Big Data in Cognitive Science (Frontiers of Cognitive Psychology), Taylor & Francis. F.
Chapman & Hall. T. W. Miller. (2015). Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python