Course Schedule

Week 0 Week 1 (Sep 9) Week 2 (Sep 16) Week 3 (Sep 23) Week 4 (Sep 30) Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Week 15

Reading Materials

Week 0 Pre-course

Week 1 Sep 9

Learning topic: Why this course?

  • Readings:
    • Lazer, David, Alex Pentland, Lada Adamic, Sinan Aral, Albert-László Barabási, Devon Brewer, Nicholas Christakis, et al. 2009. “Computational Social Science.” Science 323 (5915): 721–23.
    • Cioffi-Revilla, Claudio. 2017. Introduction to Computational Social Science. Texts in Computer Science. Cham: Springer International Publishing.
  • Course review: Syllabus, assignments, final project.

Week 2 Sep 16

Learning topic: Knowledge graph and Resource Description Framework

  • Readings:
    • Miller, Eric J. 2001. “An Introduction to the Resource Description Framework.” Journal of Library Administration 34 (3–4): 245–55.
  • Software packages:
    • Yu, Shih Yuan, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, and Mohammad Abdullah Al Faruque. 2019. “Pykg2vec: A Python Library for Knowledge Graph Embedding.” ArXiv:1906.04239 [Cs, Stat], June.
    • Costabello, Luca, Sumit Pai, Chan Le Van, Rory McGrath, and Nick McCarthy. 2019. “AmpliGraph: A Library for Representation Learning on Knowledge Graphs,” March.
  • Group discussion: How can you use knowledge graph and RDF to solve a real-world problem?
  • Hands-on:

Week 3 Sep 23

Learning topic: Open data policy and good enough research practice for social (data) scientists

  • Readings:
    • Gentzkow, Matthew, and Jesse M. Shapiro. 2014. Code and Data for the Social Sciences: A Practitioner’s Guide.
    • Wilson, Greg, D. A. Aruliah, C. Titus Brown, Neil P. Chue Hong, Matt Davis, Richard T. Guy, Steven H. D. Haddock, et al. 2014. “Best Practices for Scientific Computing.” PLOS Biology 12 (1): e1001745.
    • Wilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K. Teal. 2017. “Good Enough Practices in Scientific Computing.” PLOS Computational Biology 13 (6): e1005510.

Data topic:

Week 4 Sep 30

Learning topic: High-performance cloud computing and parallel computing

Data topic:

Week 5 Oct 7

Learning topic: TBD

Data topic

Week 6 Oct 14

Learning topic: Network analysis: basic concepts

Data topic

Week 7 Oct 21

Learning topic: Network analysis: analysis of nodes

Data topic

Week 8 Oct 28

Learning topic: Network analysis: analysis of communities

Data topic

Week 9 Nov 4

Learning topic: Text analysis: regular expression

  • Complete all lessons on RegexOne
  • Readings:
    • Jurafsky, Daniel, and James H. Martin. 2017. “Regular Expressions, Text Normalization, Edit Distance.” In Speech and Language Processing, 3rd draft.

Data topic

Week 10 Nov 11

Learning topic: Text analysis: basic NLP

Data topic

Week 11 Nov 18

Learning topic: Text analysis: basic topic modeling

Data topic:

Week 12 Nov 25

Learning topic: Text classification using Naive Bayes

Data topic:

Week 13 Dec 2

Learning topic: Text classification using neural networks

Data topic:

Week 14 Dec 9

Learning topic:

Data topic: