Entry requirements

120 credits including 20 credits in computer science and 25 credits in mathematics or statistics. A second course in computer programming. Introduction to Data Science, alternatively both Data Mining I and an introductory course in machine learning. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course the student shall be able to:

• collect data about social interactions and behaviour from different sources,
• consider the implications of the features of social data on the analysis process,
• assess the validity of a social-data mining process,
• identify patterns in different types of social data,
• evaluate and compare the suitability of different social-data mining methods,
• judge the analysis of social data with respect to relevant scientific, social and ethical criteria,
• summarize and explain the scientific literature on analysis of social data,
• design and execute a social-data mining process.

Content

Sources, types, and features of social data. The social data mining process: role of computational methods and validity. Social network analysis: social structures and processes. Feature-rich social networks. Computational text analysis: text preprocessing, word frequencies, topic modelling (classic and deep learning-based). Analysis of social visual data (classic and deep learning-based).

Instruction

Lectures, seminars, laboratory sessions.

Assessment

Active participation in seminars (5 credits) and lab sessions (5 credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.