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Undergraduate course

Visual Data Science

  • ECTS credits10
  • Teaching semesterAutumn
  • Course codeINF253
  • Number of semesters1
  • LanguageEnglish
  • Resources

Level of Study

Bachelor

Teaching semester

Autumn

Objectives and Content

Objectives
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.

Content
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. To achieve this, the course covers the necessary concepts in data science such as statistics, data preparation and management and machine learning. Furthermore, the course focuses in visualization theory for non-spatial information data, 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:

Knowledge
The student

  • 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
  • has acquired insight into the state of the part practices in data science such as statistics and machine learning

Skills
The student

  • 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

General competence
The student

  • 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

INF100 and INF101 (or a comparable education); MAT101 (or MAT111 or any other comparable course). The students are expected to be familiar vith Python JavaScript, Linear Algebra, basic statistics and data processing

Recommended Previous Knowledge

Recommended courses: INF250, INF161 Data Science

Credit Reduction due to Course Overlap

None

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 ecam (four hours). The exam is a closed-book ecam. The overall evaluation of the course is then a combination of the grading of the programming assignments and the exam.

Examination Support Material

None-programmable calculator, according to the faculty regulations.

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 June 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.

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.

Contact

Contact Information

Student adviser:

Student adviser

T: 55 58 42 00