Laboratory: "data Visualization Narratives"
A.Y. 2023/2024
Learning objectives
The course aims to provide students with a comprehensive understanding of the fundamental principles of data visualization and the crucial role of storytelling in crafting effective data-driven narratives.
Learning objectives are articulated as follows:
- Understand the main principles of data visualisation and the relevance and role of storytelling in creating effective data narratives.
- Develop skills in selecting appropriate visual models for different data types and audiences.
- Familiarise with tools and software used for data visualisation.
- Analyse and critique existing data visualisation narratives to identify trends and best practices.
- Apply knowledge and skills to design a data visualisation project as a team.
Learning objectives are articulated as follows:
- Understand the main principles of data visualisation and the relevance and role of storytelling in creating effective data narratives.
- Develop skills in selecting appropriate visual models for different data types and audiences.
- Familiarise with tools and software used for data visualisation.
- Analyse and critique existing data visualisation narratives to identify trends and best practices.
- Apply knowledge and skills to design a data visualisation project as a team.
Expected learning outcomes
Upon compilation of this module, students will be able to:
- Explain the principles of data visualisation and how it contributes to effective storytelling in data narratives.
- Select appropriate visual models for different types of data and audiences.
- Combine the use of data visualisation tools.
- Analyse and critique existing data visualisation narratives.
- Collaboratively design a data visualisation project on a chosen topic.
- Explain the principles of data visualisation and how it contributes to effective storytelling in data narratives.
- Select appropriate visual models for different types of data and audiences.
- Combine the use of data visualisation tools.
- Analyse and critique existing data visualisation narratives.
- Collaboratively design a data visualisation project on a chosen topic.
Lesson period: First trimester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Course syllabus
This module focuses on understanding data visualization principles and their role in effective data storytelling. Students will learn to select appropriate visual models, use data visualization tools, analyze existing narratives, and design a small project collaboratively.
Assessment methods include discussions, case studies, hands-on activities, discussions, and a final small group project. Evaluation involves structured discussions or written tests, along with class participation.
Assessment methods include discussions, case studies, hands-on activities, discussions, and a final small group project. Evaluation involves structured discussions or written tests, along with class participation.
Prerequisites for admission
To enrol in the module, please fill out this Google Form https://forms.gle/ZHfVEpxBBHiB6DRu5 by the 1st of October, 2023 23:59 CET
Prerequisites include ability in data manipulation (using tools like Excel, Spreadsheet, Open Refine, R, or Python), capability to engage in class discussions, and ability to design presentations (using tools like Powerpoint, Google Drive, Keynote )
Prerequisites include ability in data manipulation (using tools like Excel, Spreadsheet, Open Refine, R, or Python), capability to engage in class discussions, and ability to design presentations (using tools like Powerpoint, Google Drive, Keynote )
Teaching methods
Lectures, group activities such as discussions and mini-workshops.
Teaching Resources
Slides will be uploaded and will be available to students after each lesson.
Main texts:
- Hil, Darjan, and Nicole Lachenmeier. Visualizing Complexity : Modular Information Design Handbook. Berlin: Walter de Gruyter GmbH, 2022. Web.
- Segel E and Heer J (2010) Narrative Visualization: Telling Stories with Data. IEEE Transactions on Visualization and Computer Graphics 16(6): 1139-1148. DOI: 10.1109/TVCG.2010.179.
- Bach, B., Stefaner, D., Boy, J., Drucker, S., Bartram, L., Wood, J., Ciuccarelli, P., Engelhardt, Y., Köppen, U., & Tversky, B. (2018). Narrative Design Patterns for Data-Driven Storytelling. In N. Riche, C. Hurter, N. Diakopoulos, & S. Carpendale (Eds.), Data-Driven Storytelling (pp. 107-133). CRC Press (Taylor & Francis). https://doi.org/10.1201/9781315281575-5
Main texts:
- Hil, Darjan, and Nicole Lachenmeier. Visualizing Complexity : Modular Information Design Handbook. Berlin: Walter de Gruyter GmbH, 2022. Web.
- Segel E and Heer J (2010) Narrative Visualization: Telling Stories with Data. IEEE Transactions on Visualization and Computer Graphics 16(6): 1139-1148. DOI: 10.1109/TVCG.2010.179.
- Bach, B., Stefaner, D., Boy, J., Drucker, S., Bartram, L., Wood, J., Ciuccarelli, P., Engelhardt, Y., Köppen, U., & Tversky, B. (2018). Narrative Design Patterns for Data-Driven Storytelling. In N. Riche, C. Hurter, N. Diakopoulos, & S. Carpendale (Eds.), Data-Driven Storytelling (pp. 107-133). CRC Press (Taylor & Francis). https://doi.org/10.1201/9781315281575-5
Assessment methods and Criteria
Course final examination for students attending in-class foresees an oral structured discussion, while the course final examination for online students foresees a written final test. Moreover, the performance and participation of each student during each classroom activity will contribute to the final approval.
SECS-S/01 - STATISTICS - University credits: 3
Laboratory activity: 20 hours
Professor:
Gobbo Beatrice
Professor(s)