• CD101A Imperative Programming (7.5 credits)
• CD102A Object-Oriented Programming (7.5 credits)
• CM152A Mathematical Statistics (7.5 credits)
• 3 credits in the course CM660E Mathematical Statistics for Data Science (7.5 credits)

The course is part of the degree requirements for a Master of Science in Engineering in Computer Science and Engineering (specialisation Applied Data Science)

The aim of the course is to enable the student to critically process collected data using graphical visualisation and various analytical methods, and to communicate results from collected data in a way that is easy to understand.

The course contains the following elements:

• organisation of data
• dimension 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.

Knowledge and understanding

Upon completion of the course, the student shall be able to:

1. explain analytical methods for handling large quantities of data,

2. describe plot techniques for visualization, and

3. explain basic techniques for data-based storytelling.

Competence and skills

Upon completion of the course, the student shall be able to:

4. identify and clarify ethical principles for communicating digital information in society,

5. demonstrate an open approach to collected data to understand its natural content,

6. find connections in data by applying and experimenting with different techniques,

7. verbally and in writing describe and discuss information and knowledge that data analysis provides, adapted for different kinds of stakeholders, and

8. communicate with the surrounding community effectively and in an easily understandable way with storytelling as a tool.

Judgement and approach

Upon completion of the course, the student shall be able to:

9. identify his or her own need for further knowledge and to take responsibility for his or her own development of knowledge.

Lectures, computer lab sessions, seminars, and self-study.

The following are required to pass the course

• passing grade on written assignments (3.5 credits, UA) (Intended learning outcomes 1–4, 9)
• passing grade on oral presentation and seminars (2.0 credit, Pass/Fail) (Intended learning outcomes 7, 8)
• passing grades on lab session work (2.0 credits, Pass/Fail) (Intended learning outcomes 5, 6)

For all assessments, the materials must be presented in a manner that makes it possible to discern individual performance.

The final grade corresponds to the grade of the written assignments.

• Martinez, W. L., Martines, A. R. & Solka, J. (2017) Exploratory Data Analysis with MATLAB (3rd edition), Chapman & Hall.
• Peng, R. D. (2015) Exploratory Data Analysis with R, Lulu.com.
• Riche, N.H, Hurter, C., Diakopoulos, N. & Carpendale, S. (2018) Data-driven storytelling, CRC Press.
• Tukey, J. W. (1977) Exploratory Data analysis, Addison-Wesley.

Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).

If a course is no longer offered, or has undergone significant changes, the students must be offered two opportunities for re-examination based on the syllabus that applied at the time of registration, for a period of one year after the changes have been implemented. The syllabus is a translation of a Swedish source text.

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.