Entry requirements

1. Bachelor of Science in computer science or related subjects.
2. At least 15 credits in programming.
3. At least 7.5 credits in mathematics.
4. Knowledge equivalent to English 6 at Swedish upper secondary level.

Progression level in relation to degree requirements

The course is part of the programme Computer Science: Applied Data Science, master’s programme, and can be included in the master's degree in computer science (120 credits).

Course contents

The course contains the following elements:

• organisation of data
• dimensionality reduction
• hidden patterns and clusters
• plotting techniques and mapping for visualisation of distributions, for relationships between variables, visualisation of categorical variables.
• data-based storytelling: influence, technique and ethics.

Learning outcomes

Knowledge and understanding
For a pass grade the student shall be able to:

• explain analytical methods for managing large quantities of data
• describe plot techniques for visualisation
• explain basic techniques for data-based storytelling
• identify and clarify ethical principles for communicating digital information in society.

Competence and abilities
For a pass grade the student shall be able to:

• demonstrate an open approach to collected data to understand its natural content
• find connections in data by applying and experimenting with different techniques
• verbally and in writing describe and discuss information and knowledge that data analysis provides, adapted for different kinds of stakeholders

Evaluation abilities and approach
For a pass grade the student shall be able to:

• identify his or her own need for further knowledge and to take responsibility for his or her own development of knowledge
• communicate with the surrounding community effectively and in an easily understandable way with storytelling as a tool

Learning activities

Lectures, computer laboratories, seminars

Assessment

The course is examined through verbal and written examination tasks, including active participation in seminars: The course is examined by:

• Written assignments (3.5 credits, UA)
• Oral presentation at seminars (2.0 credits, UG)
• Laboratory work (2.0 credits, UG)

An A-E pass requires that all parts have been completed and passed.

The final grade is based on written assignments.

Course literature

• Martinez, W &, Martines, A: Exploratory Data Analysis with MATLAB, Chapman & Hall 2005.
• Reiche, N al at, Data-driven storytelling, CRC Press, 2018.
• Peng, Roger D. Exploratory Data Analysis with R, 2015
• Tukey, J W.Exploratory Data analysis, Addison-Wesley 1977

Course evaluation

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).

Interim rules

When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.

Additional information

If a student has a Learning support decision, the examiner has the right to provide the student with an adapted test, or to allow the student to take the exam in a different format.

The syllabus is a translation of a Swedish source text.