Mining of Social Data
Course, Master's level, 1DL465
Expand the information below to show details on how to apply and entry requirements.
Autumn 2025
Autumn 2025,
Uppsala, 67%, On-campus, English
- Location
- Uppsala
- Pace of study
- 67%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 3 November 2025–18 January 2026
- Language of instruction
- English
- 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.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 24,167
- Total tuition fee: SEK 24,167
- Application deadline
- 15 April 2025
- Application code
- UU-11012
Admitted or on the waiting list?
- Registration period
- 20 October 2025–9 November 2025
- Information on registration from the department
Autumn 2025
Autumn 2025,
Uppsala, 67%, On-campus, English
For exchange students
- Location
- Uppsala
- Pace of study
- 67%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 3 November 2025–18 January 2026
- Language of instruction
- English
- 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.
Admitted or on the waiting list?
- Registration period
- 20 October 2025–9 November 2025
- Information on registration from the department
About the course
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).
Reading list
No reading list found.