Course Schedule


Learning Cycle

Reading Materials by Week

Introduction to the course

Data Management

Concept Representation

Data Analysis

Scientific Communication


Weekly Details


Week 1: Course introduction Back2Top

In class

  • Course overview:
  • Helpful resources:
    • Open source communities (e.g., Stack Overflow)
    • ChatGPT. Discussion: How to effectively and responsibly use it? Your best practices.
    • CSS Empirical Studies Database. Discussion: Pick 2 studies of your interests, discuss with neighbors.

After class


Week 2: Computational Social Science: Why Research Design Approach Back2Top

Before class

  • Required readings:
    • CSSPrimer: Chapter 1 and 2.

In class

  • Discussion and lecture on readings. Key points:
    • Philosophical and epistemological fundamentals, research design overview, comparison between CSS and conventional approaches
    • Data management, concept representation, data analysis, and scientific communication
  • In-class review and prepare:
    • Group presentations.
    • Empirical studies for analysis.

If time allows: High-performance cloud computing with Chameleon

- How to set up a Jupyter Lab server

After class

  • Assignment 1 due on upcoming Monday.
  • Review tools and platforms for upcoming week, prepare to discuss how you plan to use them.

Week 3: Data Management: Methods and tools Back2Top

Before class

  • Review Assignment 3: Gathering Literature in Your Field

In class

  • File and data format: API, JSON, and relational database.
  • Efficiency and automation.
  • Tools review:
    • OpenAlex
    • Draw.io
    • MySQL Workbench
  • Prepare Assignment 3: Gathering Literature in Your Field

After class

  • Group presentation slides and annotated bibliography on Data Management due upcoming Monday.

Week 4: Data Management: Background and Purposes (group presentation) Back2Top

Before class

  • Required readings
    • Leonelli, Sabina. “Scientific Research and Big Data.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta, Summer 2020. Metaphysics Research Lab, Stanford University, 2020. https://plato.stanford.edu/archives/sum2020/entries/science-big-data/.
    • Wickham, Hadley. “Tidy Data.” The Journal of Statistical Software 59, no. 10 (2014). http://www.jstatsoft.org/v59/i10/.
    • Goble, Carole, and David De Roure. “The Impact of Workflow Tools on Data-Centric Research.” In The Fourth Paradigm: Data-Intensive Scientific Discovery, edited by Tony Hey, Stewart Tansley, Kristin Tolle, and Jim Gray. Microsoft Research, 2009. https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/.
    • Fidler, Fiona, and John Wilcox. “Reproducibility of Scientific Results.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta, Summer 2021. Metaphysics Research Lab, Stanford University, 2021. https://plato.stanford.edu/archives/sum2021/entries/scientific-reproducibility/.

In class

  • Student-lead group presentation and instructor lecture.
  • Group discussion on annotated bibliography.
  • Prepare Assignment 3: Gathering Literature in Your Field.

After class

  • Assignment 3: Gathering Literature in Your Field due upcoming Monday.
  • Group presentation slides and annotated bibliography on Concept Representation due in two weeks.

Week 5: Data Management Exercise: Gathering Literature in Your Field Back2Top

Before class

  • Complete the Assignment and submit to OSF.

In class

  • Presentation and discussion of Assignment.
  • Review peer assignments and provide feedback.
  • Preview next exercise.

After class

  • Revise assignments according to feedback.
  • Group presentation slides and annotated bibliography on Concept Representation due in upcoming Monday.
    • Read required readings.
    • Prepare group presentation.
    • Write annotated bibliography.

Week 6: Concept Representation: Background and Purposes (group presentation) Back2Top

Before class

  • Required readings
    • Creswell, John W. “The Selection of a Research Approach.” In Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th ed. Thousand Oaks: SAGE Publications, 2014.
    • Ragin, Charles C., and Lisa M. Amoroso. “The Goals of Social Research.” In Constructing Social Research: The Unity and Diversity of Method, 135–62. Pine Forge Press, 2011.
    • Grimmer, Justin, and Brandon M. Stewart. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21, no. 3 (2013): 267–97. https://doi.org/10.1093/pan/mps028.

In class

  • Student-lead group presentation and instructor lecture.
  • Discussion on annotated bibliography.
  • Prepare Assignment 4: Automated Coding, due in two weeks.

After class


Week 7: Concept Representation: Methods and tools Back2Top

Before class

  • Prepare Assignment 4: Automated Coding

In class

After class

  • Group presentation slides and annotated bibliography on Data Analysis due in two weeks.
  • Complete Assignment 4: Automated Coding, due upcoming Monday.

Week 8: Concept Representation Exercise: Automated Coding Back2Top

Before class

  • Complete the Assignment and submit to OSF.

In class

  • Presentation and discussion of Assignment.
  • Review peer assignments and provide feedback.
  • Preview next exercise.

After class

  • Revise assignments according to feedback.
  • Group presentation slides and annotated bibliography on Data Analysis due in upcoming Monday.
    • Read required readings.
    • Prepare group presentation.
    • Write annotated bibliography.

Week 9: Data Analysis: Background and Purposes (group presentation) Back2Top

Before class

  • Required readings
    • Hofman, Jake M., Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, et al. “Integrating Explanation and Prediction in Computational Social Science.” Nature 595, no. 7866 (July 2021): 181–88. https://doi.org/10.1038/s41586-021-03659-0.
    • Ludwig, Jens, and Sendhil Mullainathan. “Machine Learning as a Tool for Hypothesis Generation.” Working Paper. Working Paper Series. National Bureau of Economic Research, March 2023. https://doi.org/10.3386/w31017.

In class

  • Student-lead group presentation and instructor lecture.
  • Discussion on annotated bibliography.
  • Prepare Assignment 5: Simulation and Regression.

After class