Entry requirements

• English 6
• A total of 30 credits in one of the main subject areas: Information Science, Informatics, Computer Science, or Computer Engineering, of which at least 7.5 credits must be in programming

The student is expected to have knowledge of database systems

Progression level in relation to degree requirements

G1F First cycle, has less than 60 credits in first-cycle course/s as entry requirements

Course contents

Students become familiar with and apply the methods and algorithms that data analysts work with within data science as an interdisciplinary field. This includes collecting and exploring data through data management and data analysis, visualizing and identifying patterns in data, as well as reporting results in an engaging way. The focus of application lies in data‑driven development opportunities in business systems and social innovations based on the global goals for sustainable development. The aim is to search for new approaches and solutions to social needs or societal challenges.

The course includes the following components:

• The data mining process
• Exploratory data analysis and data visualization of openly available data
• Introduction to social innovation and the global goals for sustainable development
• Opportunities and challenges of data analysis for data‑driven business development and social innovation
• Basic concepts and orientation in digital methods and their applications using various tools
• Programming with or without code (graphical programming) of different models for decision‑making, prediction, or categorization
• Reporting results with the help of storytelling

Learning outcomes

Knowledge and understanding

After completing the course, the student shall be able to:

1. identify challenges and opportunities for the application of data science in concrete problem situations related to business development and social innovations.
2. describe their own data‑driven approach when applying data science to individual projects within business development and social innovations.
3. describe and compare the most common digital methods and algorithms within the data mining process.

Competence and skills

After completing the course, the student shall be able to:

1. select, assess, and collect publicly available data based on an overarching problem description.
2. manage, analyze, explore, and visualize large amounts of data using appropriate digital tools in order to extract relevant information based on a problem formulation.
3. apply the data mining process with suitable tools and methods as part of data‑driven business development or social innovation.
4. integrate and report the results of a data‑driven development process using storytelling.

Judgement and approach

After completing the course, the student shall be able to:

1. assess and argue for the choice of relevant digital methods when applying data science in business development and social innovation.
2. problematize and discuss the relationships between data selection, the opportunities and challenges of data analysis, and data‑driven development from societal perspectives.

Learning activities

Problem‑based learning based on real‑world case studies supported by lectures, seminars, laboratory sessions, and workshops with supervision. Students are expected to work individually as well as collaborate with others in projects. A substantial amount of self-study is required for carrying out the project work, as well as for the preparations and assignments for seminars, laboratory sessions, and workshops.

Assessment

To obtain a passing grade, the student must participate actively in all parts of the course and complete and pass the following parts:

• Seminars, theoretical concepts (2 HE credits, Pass/Fail) – learning outcomes 1–3, 4, 8, 9
• Laboratory sessions with continuous presentations (5 HE credits, Pass/Fail) – learning outcomes 5–7
• Project work with written final report (5 HE credits, Pass/Fail) – learning outcomes 1–9
• Final seminar with project presentation and individual review of another group’s project (3 HE credits, UA) – learning outcomes 1–9

The final course grade (UA) corresponds to the grade awarded for the final seminar.

Course literature

Course evaluation

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).

Interim rules

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.

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.