Concise, hands-on introduction to collecting, documenting, and analyzing digital data for social science research. Focus on platform data (APIs), web scraping, sensors/digital traces, and responsible research practices.
Note: Details, schedule, and readings are subject to change.
- Identify opportunities and limits of digital data in social research
- Collect data from platforms/APIs and the open web (ethically & legally)
- Document, clean, and store data reproducibly
- Navigate GDPR/ethics and platform policy constraints
- Prototype analyses of text, networks, and trace data
- Digital traces & platform data; APIs after the “APIcalypse”
- Web scraping; rate-limits & consent considerations
- Sensor/field data capture; logging & documentation
- Data cleaning, storage, and reproducibility
- Ethics, GDPR, and platform terms
- Light-touch text/network/trace analytics
- Interactive lectures, live demos, and guided labs
- Team exercises and a small final project
- Assessment: participation + project deliverables
- Credits: 5 ECTS
- Language: English
- Course code: COS-D439
- Platform: Moodle (login required)
| Päivämäärä | Aika | Opetuspaikka |
|---|---|---|
| pe 5.9.2025 | 14.15–15.45 | Päärakennus, U3040 |
| pe 12.9.2025 | 14.15–15.45 | Päärakennus, U3040 |
| pe 19.9.2025 | 14.15–15.45 | Päärakennus, U3040 |
| pe 26.9.2025 | 14.15–15.45 | Päärakennus, U3040 |
| pe 3.10.2025 | 14.15–15.45 | Päärakennus, U3040 |
| pe 10.10.2025 | 14.15–15.45 | Päärakennus, U3040 |
| pe 17.10.2025 | 14.15–15.45 | Päärakennus, U3040 |
- Moodle (materials, announcements): https://moodle.helsinki.fi/course/view.php?id=65706
- Previous edition (archive): https://opetus.mante.li/datacollection/
- Research data management (UH): https://researchdata.helsinki.fi/
- Software Carpentry – Databases & SQL: https://swcarpentry.github.io/sql-novice-survey/
- Reading (open access): Bruns, A. (2019). After the “APIcalypse”. PDF: https://snurb.info/files/2019/After%20the%20%27APIcalypse%27.pdf
Additional reading
A useful companion for this course is The Handbook of Computational Social Science (Engel, Quan-Haase, Liu & Lyberg, 2021, Routledge, Vols. 1–2). Especially relevant are chapters such as “Digital Trace Data: Modes of Data Collection, Applications, and Errors at a Glance” (Vol. 1) and “A Brief History of APIs,” “Application Programming Interfaces and Web Data for Social Research,” and “Web Data Mining: Collecting Textual Data from Web Pages Using R” (Vol. 2). These readings show how researchers collect, validate, and analyze digital and web data—topics that closely match the practical work in this course.