Visual Data Science

Postgraduate course

Course description

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 sciences can be applied to data science 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 analysis, 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 on visualization, interaction models, principles of human computer interaction and aesthetics for visual design.

Learning Outcomes

Upon completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

The student

  • has the ability to understand 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 knowledge about effective human computer interaction and user interface design

The student

  • is able to acquire, prepare and visualize data for effective findings communication
  • 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

General competence
The student

  • gains the ability to critically assess 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

Level of Study


Semester of Instruction

Required Previous Knowledge
INF100 and INF101 (or a comparable education); MAT101 (or MAT111 or any other comparable course). The students are expected to be familiar with Python, Linear Algebra, basic statistics and data processing.
Recommended Previous Knowledge
Recommended courses: INF250, INF161 Data Science
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.
Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Assessment Semester
Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.
Reading List
The reading list will be available within July 1st for the autumn semester and December 1st for the spring semester.
Course Evaluation
The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.
Examination Support Material

Non-programmable calculator, according to the faculty regulations.


Programme Committee
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.
Course Coordinator
Course coordinator and administrative contact person can be found on Mitt UiB, or contact Student adviser
Course Administrator
The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.