SOCI1005
Programming for social scientists
Offer semester
Lecture time
Lecture venue
Credits awarded
2nd semester
Tuesday
15:00-16:50
CPD-LG.18
6
The course provides an introduction to the basic computational tools, skills, and methods used in Computational Social Science using Python. Python is the most popular programming language for data science, used widely in both academia and the industry. Students will learn to use common workflow and collaboration tools, design, write, and debug simple computer programs, and manage, summarize, and visualize data with common Python libraries. The course will employ interactive tutorials and hands-on exercises using real social data. Participants will work independently and in groups with guidance and support from the lecturers. The practical exercises are designed to demand more autonomy and initiative as the course progresses, culminating in an open-ended group project.
This is an introductory course and no prior experience with programming is required. A basic understanding of statistics and some scripting experience (e.g., from building web pages or statistical analysis programs such as R or Stata) will be helpful but not needed.
By the end of the course participants will:
CLO1 Knowledge and Understanding: Possess an understanding of the tools, methods, tasks, and goals of Computational Social Science.
CLO2 Intellectual skills: Design procedures and algorithms to solve data analysis tasks.
CLO3 Practical skills: Write simple programs in Python and work other popular Python modules and libraries for data science.
CLO4 Transferrable skills: Use bash, Jupyter Notebook, and GitHub to write, run, collaborate on, and share programming code.
Tasks
Weighting
Coursework
100%
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J.,
Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Alstyne, M. V. (2009). Computational social science. Science
Guttag, John V. (2016). Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press.
McLevey, John. (2021). Doing Computational Social Science: A Practical Introduction. Sage 323(5915), 721–723. https://doi.org/10.1126/science.1167742
Salganik, M. J. (2019). Bit by Bit: Social Research in the Digital Age. https://www.bitbybitbook.com/
Various authors. (2021). Special collection on Computational Social Science. Nature 595, 149– 222. https://www.nature.com/collections/cadaddgige/
Offer Semester | Lecture Day | Lecture Time | Venue | Credits awarded |
|---|---|---|---|---|
2nd semester | Tuesday | 15:00-16:50 | CPD-LG.18 | 6 |
Honorary Lecturer
