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78302-01 - Practical course: Introduction to Social Data Science Methods (3 CP)
| Semester | spring semester 2026 |
| Course frequency | Irregular |
| Lecturers | Rosa Lavelle-Hill (rosa.lavelle-hill@unibas.ch, Assessor) |
| Content | This course provides a hands-on introduction to social data science methods with a strong focus on applied research workflows in the social sciences. Students will learn how to design studies, collect data, and analyze social data using a range of quantitative and computational methods. The course emphasizes practical skills, collaborative group work, and the end-to-end research process—from formulating research questions to disseminating results. It is designed to complement the more theoretical lecture-based course Introduction to Social Data Science. Students will collect and analyze their own survey data in groups using core data analysis methods, including descriptive statistics and basic inferential statistics in Python (example scripts provided). The course further introduces computational social science techniques such as network analysis, decision tree analysis, natural language processing (NLP) for qualitative data, and digital data collection via web scraping and APIs. Exercise sessions are structured around guided group work and applied analysis tasks. Students will analyze data using provided scripts in Python (via Google Colab or Jupyter Notebooks) and external tools such as Gephi for network analysis. Throughout the course, participants progressively build their own data analysis project, culminating in a group presentation that integrates multiple methods covered in the course. |
| Learning objectives | To understand and critically evaluate social data science methods and their role in social science research. To design and implement surveys, including Likert-scale and open-ended measures, with appropriate validation and sampling strategies. To perform applied data analyses in Python (with the help of example scripts provided), including descriptive statistics, regression, and basic machine learning–based methods such as decision trees. To apply computational methods such as network analysis, NLP, and data collection via scraping and APIs to real social data. To communicate and disseminate data-driven research findings clearly and effectively. |
| Bibliography | Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199-231. https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.pdf Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. science, 323(5916), 892-895. https://www.science.org/doi/pdf/10.1126/science.1165821 Shmueli, G. (2010). To explain or to predict?. Statistical science, 289-310. https://www.jstor.org/stable/pdf/41058949 Programming with Python for Social Scientists, Philip D. Brooker, Published by SAGE, 2020 |
| Admission requirements | The number of participants is limited. In case of over-subscription, students of Digital Humanities will be given priority. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Tuesday | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
Dates
| Date | Time | Room |
|---|---|---|
| Tuesday 24.02.2026 | 14.15-16.00 | Fasnachtswoche |
| Tuesday 03.03.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 10.03.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 17.03.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 24.03.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 31.03.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 07.04.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 14.04.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 21.04.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 28.04.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 05.05.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 12.05.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 19.05.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Tuesday 26.05.2026 | 14.15-16.00 | Kollegienhaus, Hörsaal 117 |
| Modules |
Modul: Digital Humanities, Culture and Society (Master's degree subject: Digital Humanities) Module: Introduction to Digital Humanities (Master's degree subject: Digital Humanities) Module: Societal Approaches (Master's Studies: European Global Studies) |
| Assessment format | continuous assessment |
| Assessment registration/deregistration | Reg.: course registration; dereg.: not required |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Humanities and Social Sciences, studadmin-philhist@unibas.ch |
| Offered by | Digital Humanities Lab |