Visual Data Science
Level of Study
Spring. First time spring 2022.
Objectives and Content
The course studies the human side of data science. In particular, the course discusses how principles from visualization, visual analytics, perceptual psychology and cognitive sceinces can be applied to data sicence in order to facilitate effective data exploration tools. Furthermore, the course introduces students to the principles of human computer interaction, interface design and effective data communication tailored for different audiences.
The course is designed to teach students the full pipeline of human centered data anaysis, from data acquisition, data preparation and management, data visualization, interaction and exploration and finally the effective communication of insights from the data. Furthermore, the course focuses in visualization theory for non-spatial information data, interaction models, principles of human computer interaction and aesthetics for visual design.
Upon completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
- has the ability to undestand and evaluate visual presentations of information data
- has thorough understanding of the visualization and interaction techniques for data science tasks
- has gained deep knowledge about data models, graphical perception and effective methods for visual encoding and data interaction
- has acquired knoledge about effective human computer interaction and use interface design
- is able to acquire, prepare and visualize data for effective findings sommunication
- is able to analyze data analysis tasks and is able to identify effective methods from the visualization field, statistics and machine learning suited for task requirements
- can evaluate the data quality and perform data cleaning
- can design effective user interfaces for data exploration and presentation solutions using modern programming techniques
- gains the ability to critically asses the quality and truthfulness of data representation
- can effectively communicate data insights through visual representations
- can independently plan, structure and implement smaller scale software projects
Required Previous Knowledge
Credit Reduction due to Course Overlap
Access to the Course
Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences.
Teaching and learning methods
The course is built upon lectures, programming tutorials and programming assignments as well as exercises. On average, students will meet up for lectures, tutorials and exercises for 5 hours per week.
Compulsory Assignments and Attendance
The exercises must be attended. The programming assignments will be evaluated and must be passed. An exam (about the content of the lectures) needs to be passed as well. Compulsory assignments are valid two semesters, the semester of the approval and the following semester.
Forms of Assessment
At the end of the semester there is a written digital exam (four hours). The exam is a closed-book exam. The overall evaluation of the course is then a combination of the grading of the programming assignments and the exam.
Examination Support Material
Non-programmable calculator, according to the faculty regulations.
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.
The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester.
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
Course coordinator and administrative contact person can be found on Mitt UiB, or contact Student adviser
The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.
T: 55 58 42 00