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Code
IT-VIZ1
Version
1.0
Offered by
ICT Engineering
ECTS
5
Prerequisites
Students taking the course are expected to have basic programming skills.
Main purpose
This course provides students with a comprehensive introduction to the fundamentals of data visualization. Students will learn how to transform data into effective graphical representations, explore data relationships, visually communicate insights, and support data-driven decision processes using industry-standard open-source tools and libraries.
Knowledge
After completing this course, it is expected that the student will be able to:
- Understand theoretical concepts of data visualization, including models for design and systematic approaches to implementation.
- Describe different data types, such as numerical, geospatial, temporal, relational, and textual, as well as their implications for visualization.
- Explain various visualization types and their affordances, including advanced techniques, such as heatmaps, treemaps, and network graphs.
- Reflect on key principles of data-related visual communication and design, including Gestalt theory and data storytelling.
- Account for both quantitative and qualitative empirical validation methods.
- Discuss the interdisciplinary aspects of data visualization encompassing statistics, psychology, design, communication, and computation.
Skills
After completing this course, it is expected that the student will be able to:
- Implement custom visualizations using tools and libraries such as Matplotlib, Seaborn, Plotly, and D3.js.
- Use valid data preprocessing techniques and tools such as Pandas and NumPy to ensure reliable representations.
- Apply appropriate visualization methods based on the type of data, the visualization requirements, and the target audience.
- Validate and improve visualization designs through theoretical analysis and empirical testing.
- Generate impactful visual narratives by applying data storytelling methods.
- Create interactive visualizations for user-driven data exploration.
Competences
After completing this course, it is expected that the student will be able to:
- Utilize visualization to analyze large, complex datasets.
- Develop visualization strategies for presenting actionable data insights to both technical and non-technical audiences.
- Evaluate visualization projects in terms of design, communicative impact, and implementation.
- Independently manage and justify complete visualization projects.
Topics
Teaching methods and study activities
The course is organized into 12 weekly sessions, each beginning with a theoretical introduction to different aspects of data visualization, before facilitating hands-on work related to the introduced theory, using relevant tools and techniques.
At the beginning of the course, students are encouraged and guided to select an area of interest with rich data sources to serve as the basis for their hands-on work. The resulting products are allowed and encouraged to be directly implemented as part of the final exam project.
The course emphasizes progressive learning, starting from basic concepts and implementations, moving towards creating professional-level visualizations.
Resources
- Selected readings on data visualization principles, such as research articles, book chapters, and blog posts.
- Hands-on guides for tools used in the course.
- Additional online resources, including videos and interactive content.
Evaluation
Examination
Exam prerequisites:
None.
Exam type:
20-minute oral examination based on the final exam project, which must be handed in before the deadline. The final project is a comprehensive visualization production covering a self-selected area of interest.
Assessment is based on the student demonstrating the expected knowledge, skills and competences through both the final project and the oral defense.
Tools allowed:
All tools and resources introduced throughout the course.
Re-exam:
Same as the ordinary exam.
Grading criteria
Grading based on the Danish 7-point scale.
Additional information
Students are encouraged to engage in discussions and collaborations throughout the course, as this will significantly contribute to their understanding of visualization design and its applications.
Responsible
Jakob Mørup Wang (jakm)
Valid from
8/1/2025 12:00:00 AM
Course type
6. semester
7. semester
Elective for the specialization Data Engineering
Electives
Web 6 og 7
Keywords
Data visualization, exploratory data analysis, visual perception, Gestalt principles, data storytelling, interactive design, Python, Matplotlib, Seaborn, Plotly, Pandas, NumPy, D3.js.