Learning Outcomes

  • LO1. Methods. Students will design, execute, document, and disseminate research that applies tools and methods from data science to address a social science research question.
    • 1.1. Computing. Students will use computational tools and best practices to execute and document data science projects.
    • 1.2. Data Design and Collection and Data Quality. Students will use modern data collection techniques and evaluate data quality.
    • 1.3. Database Management. Students will manage structured and unstructured data within an organized database.
    • 1.4. Inference. Students will make statistical inferences using computational tools.
    • 1.5. Modeling. Students will build prediction models and algorithms. 
  • LO2. Theory. Students will develop expertise in specific contemporary social science theories and data science approaches to tackling research questions related to these theories.
    • 2.1. Social Science theory. Students will develop expertise in social science theory within a specific contemporary social science field.
    • 2.2. Statistical theory. Students will develop expertise in the statistical theory that provides the foundation for analyses within data science methods.
  • LO3. Applications and Social Issues. Students will apply findings from social data science research to analyze the policy and design of socio-technical systems. Social topics will include issues in diversity, equity, and inclusion across outcomes.
    • 3.1. Turning Analysis into Actions. Students will use findings from quantitative analyses to motivate policy or decisions within social science contexts.
    • 3.2. Communication. Students will effectively communicate findings and applications of social data science projects.
    • 3.3. Social Impact. Students will develop data-driven analyses intended to effect social change.
  • LO4. Ethical Issues. Students will identify and analyze social, legal, and ethical issues in social data science, and follow data use practices which value and safeguard the ethical use of data in all aspects of the profession. Ethical issues will include issues in diversity, equity, and inclusion.
    • 4.1. Ethics in Data Management and Use. Students will ethically store, manage, and use social science data. 
    • 4.2. Ethics in Research Design and Analysis. Students will identify sources of bias that can arise from data science techniques and mitigate these biases.
    • 4.3. Ethics in Policy and Design of Systems. Students will incorporate fairness within systems.