Schedule

Course Schedule

We may have field visits and guest speakers over the semester to be arranged, so the schedule here is TBD.

Week 1 (1/13): Course introduction

In class

• Course overview:
  • Context of this course.
  • Course sites: Syllabus website, Open Science Framework, Canvas.
• 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

• Due Next Week:
  • Readings and annotated bibliography for CSSPrimer: Preface and Introduction
  • Assignment 2 sign up
• Work In Progress:
  • Assignment 1: Plagiarism Test
• Register accounts:
  • GitHub / Free GitHub Pro (GitHub Education)
  • Chameleon Cloud
  • TACC Analysis Portal Pilot
  • Open Science Framework (optional)
• How to use Cloud computing resources:
  • TACC Analysis Portal Pilot - Student Guide
  • Review “Getting started with Chameleon Cloud”
• Review Vibe Coding 101 (not graded).

Week 2 (1/20): Computational Social Science: Why Research Design Approach

In class

• Discussion and lecture on readings. Key points:
  • Theoretical 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, introducing supercomputer resources:

Cloud computing with Chameleon

• Start an instance on Chameleon Cloud
• Install Anaconda Python and Jupyter Notebook.
• Snapshot the instance as an image.
• You can also watch the video recordings below:
  • How to use Chameleon Cloud: Set up a new instance

• How to set up a Jupyter Lab server

TACC Analysis Portal Pilot - Student Guide

After class

• Due Next Week: Assignment 1: Plagiarism Test
• Work In Progress:
  • Assignment 2: Group presentation and annotated bibliography on Data Management
  • Review Assignment 3: Gathering Literature in Your Field (i.e., CSSPrimer: Chapter 3)
  • Review tools and platforms for upcoming week, prepare to discuss how you plan to use them.

Week 3 (2/3): Data Management: Methods and tools

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
• Discuss Perigon’s research proposal.

After class

• Due Next Week:
  • Assignment 2: Group presentation and annotated bibliography on Data Management
• Work In Progress:
  • Assignment 3: Gathering Literature in Your Field (i.e., CSSPrimer: Chapter 3)

Week 4 (2/10): Data Management: Background and Purposes (group presentation)

Before class

• Required readings
  • CSSPrimer: Chapter 2.
  • 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

• Work In Progress:
  • Assignment 3: Gathering Literature in Your Field
  • Assignment 2: Group presentation and annotated bibliography on Concept Representation

Week 5 (2/17): Data Management Exercise: Gathering Literature in Your Field

In class

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

After class

• Due Next Week:
  • Assignment 3: Gathering Literature in Your Field
  • Assignment 2: Group presentation and annotated bibliography on Concept Representation

Week 6 (2/24): Concept Representation: Background and Purposes (group presentation) + Methods and tools

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.
• Preview Assignment 4: Automated Coding.
• Inductive coding with topic modeling:
  • Top2Vec
  • BERTopic
• Deductive coding (text classification):
  • Fine-tune model with training dataset.
  • Prompt-based classification with LLMs: OpenAI API, Models (Ollama service), deploy local LLM and API (liteLLM + Ollama).
• Prepare Assignment 4: Automated Coding.

After class

• Work In Progress:
  • Review Assignment 4: Automated Coding
  • Assignment 2: Group presentation and annotated bibliography on Data Analysis

Week 7 (3/3): Independent work week, no class

Work on Assignment 4: Automated Coding.

Prepare a draft of Assignment 4 for presenting in class.

After class

• Work In Progress:
  • Assignment 4: Automated Coding
  • Assignment 2: Group presentation and annotated bibliography on Data Analysis

Week 8 (3/10): Concept Representation Exercise: Automated Coding

In class

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

After class

• Due Next Week:
  • Assignment 4: Automated Coding
  • Assignment 2: Group presentation and annotated bibliography on Data Analysis

Week 9 (3/24): Data Analysis: Background and Purposes (group presentation)

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.
• Review Assignment 5: Network Analysis.

After class

• Work In Progress:
  • Review Assignment 5: Network Analysis
  • Assignment 2: Group presentation and annotated bibliography on Scientific Communication

Week 10 (3/31): Data Analysis: Methods and tools

In class

• Network analysis as a representation and analysis method
• Process of network analysis
• Visualization tool: Gephi

After class

• Work In Progress:
  • Review Assignment 5: Network Analysis
  • Assignment 2: Group presentation and annotated bibliography on Scientific Communication

Week 11 (4/7): Data Analysis Exercise: Network Analysis

In class

• Presentation and discussion of Assignment.
• Review peer assignments and provide feedback.
• Prepare Assignment 5 (Network Analysis).

After class

• Due Next Week:
  • Review Assignment 5: Network Analysis
  • Assignment 2: Group presentation and annotated bibliography on Scientific Communication

Week 12 (4/14): Scientific Communication: Background and Purposes (group presentation)

Before class

• Required readings
  • Wickham, H. (2014). Tidy data. The Journal of Statistical Software, 59(10). http://www.jstatsoft.org/v59/i10/
  • Kirk, A. (2019). The Visualisation Design Process. In Data Visualisation: A Handbook for Data Driven Design (2nd edition, pp. 31–58). SAGE Publications Ltd.
  • Kirk, A. (2019). Working With Data. In Data Visualisation: A Handbook for Data Driven Design (2nd edition, pp. 95–117). SAGE Publications Ltd.

In class

• Student-lead group presentation and instructor lecture.
• Discussion on annotated bibliography.

After class

• Work In Progress:
  • Assignment 6: Data Dashboards or Final Project

Week 13 (4/21): Scientific Communication: Methods and tools

Before class

• Required readings
  • Kirk, A. (2019). The Visualisation Design Process. In Data Visualisation: A Handbook for Data Driven Design (2nd edition, pp. 31–58). SAGE Publications Ltd.
  • Kirk, A. (2019). Working With Data. In Data Visualisation: A Handbook for Data Driven Design (2nd edition, pp. 95–117). SAGE Publications Ltd.

In class

• Basic principles of visualization.
• Review tools:
  • Programming language-based tools: Plotly and Dash for Python, Shiny for R
  • Off-the-shelf tools: Tableau, PowerBI, Excel.