DV2650 - Project in Visual Data Analytics - Blekinge Institute of Technology

1. Descision

This course is established by Dean 2023-06-01. The course syllabus is approved by Head of Department of Computer Science 2024-04-18 and applies from 2024-04-18.

2. Entry requirements

Admission to the course requires 90 completed credits in Computer Science or Information Systems or Media Technology of which 5 credits must be in Programming and 5 credits in a Project course. English 6.

3. Objective and content

3.1 Objective

The purpose of the course is to provide students with knowledge and skills to effectively develop systems for visual analytics (VA) by integrating interactive visualization and complex algorithms for data-driven decision-making. As a prerequisite, students must have a basic understanding of agile methods to facilitate teamwork. The course covers key aspects of data analysis, visualization tools, and evaluation methodology to ensure the successful design, implementation, and presentation of VA projects.

3.2 Content

This course centers on the practical application of visual data analytics principles, integrating insights from information visualization and data analysis to extract meaningful insights from complex datasets. Students will delve into the significance of information visualization and visual analytics in addressing analytical questions, alongside exploring real-world projects where these principles are applied. Through a combination of theoretical lectures and hands-on projects, participants will navigate challenges and opportunities at the intersection of human analysis, computational models, and visual display. Key topics covered include:

The significance of data visualization for analytical questions.
The use of information visualization and visual analysis in real-world projects.
Examples of advanced systems for visual analysis.
Data analysis and processing in real-world projects.
Tools, services, and libraries for data analysis and visualization, such as D3 and Bokeh.
Challenges and opportunities at the interface between analysts, computational models, and visual displays.
Evaluation methods for visualizations in real-world projects.
Types of bias in data, analysis, and visualization.

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

On completion of the course, the student will be able to:

A1. Understand the role of information visualization and visual analytics in data analysis and the characteristics of leading visual analytics systems.

A2. Understand the ethical implications and user impact of visualization choices.

4.2. Competence and skills

On completion of the course, the student will be able to:

B1. Independently manage and execute projects within data analysis.

B2. Master the use of tools, methods, and libraries to create and implement systems for visual analytics.

B3. Determine analytical requirements and tasks, then choose appropriate visualizations, data analyses, and interaction methods (design goals and choices).

B4. Innovate and develop advanced applications for visual analytics with a focus on technical creativity.

B5. Evaluate visual analytics for effectiveness, usability, ethical responsibility, and alignment with user needs.

5. Learning activities

The course employs a combination of lectures and supervision meetings to support student learning. Lectures introduce key concepts and tools, while supervision meetings provide personalized guidance.

Learning Activities:

Lectures: Interactive sessions cover foundational theories and methodologies in visual data analytics.
Hands-on Projects: Students work in teams to apply concepts through practical projects, enhancing problem-solving and collaboration skills.
Supervision Meetings: Regular meetings offer individualized feedback and support from instructors.
Seminars: Students present their project outcomes, fostering discussion and skill refinement.

Through these activities, students gain a strong theoretical understanding and practical experience in visual data analytics.

6. Assessment and grading

Modes of examinations of the course

Code	Module	Credit	Grade
2410	Project Proposal	1.0 credits	GU
2420	Project	3.5 credits	GU
2430	Report	2.0 credits	GU
2440	Presentation	1.0 credits	GU

The course will be graded G Pass, UX Failed result, a little more work required, U Fail.

Written examination may be followed up orally.

The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

The students are expected ot find suitable literature on their own. The list below can be used as a starting point.

• Keim, Daniel Kohlhammer, Jörg, Elis, Geoftrey, and Mansmann, Florian, Mastering the Information Age: Solving Problems with Visual Analytics, Eurographics, latest edition.

• Munzner, Tamara, Visualization Analysis and Design, CRC Press, latest edition.

Course syllabus