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Computational social science (CSS), can be broadly understood as using computational techniques to analyze social science data or create social simulations,

  • whether that data be large scale data that requires high speed computing capacities (i.e., “big data”),
  • data that is of more modest scale but requires computationally-intensive processes (i.e., analyzing sets of texts, sounds, impacts, social networks, sensors, or sensory touch data),
  • data collected online through scraping or interaction (e.g., online experiments),
  • and data that is amenable to tools from machine learning or similar modalities, among other kinds of data

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About the Computational Social Science Certificate

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The faculty supporting this certificate unite for a common goal: to create an environment for social science Ph.D. students to acquire theoretical knowledge and mastery of computational skills necessary for analyzing large and complex data. Learning Outcomes: A.  Students need mastery of a computer language which enables them to utilize the computational tools available.  This would include Python, R, or some other recognized computer language appropriate for CSS research.  They must demonstrate that they can use these tools to execute their research.  B. Students need to develop and demonstrate proficiency in at least one method of computationally-intensive data collection, extraction, or analysis.  This would include mastery of machine learning, computational linguistics, network analysis, visualization data management, web scraping, agent based modeling, and advanced statistical modeling among other techniques.  C. Students need to understand CSS as a broad epistemology or approach to establishing credible… Read More