User:Groceryheist/drafts/Data Science Syllabus
- Data Science and Organizational Communication
- Principal instructor
- Nate TeBlunthuis
- Course Description
- Fundamental principles of data science and its implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness and accountability; data provenance, curation, preservation, and reproducibility; human computation; data communication and visualization; the role of data science in organizational context and the societal impacts of data science.
Overview and learning objectives
The rise of "data science" reflects a broad and ongoing shift in how many teams, organizational leaders, communities of practice, and entire industries create and use knowledge. This class teaches "data science" as experienced by data-intensive knowledge workers but also as it is positioned in historical, organizational, institutional, and societal contexts. Students will gain an appriciation for the technical and intellectual aspects of data science, consider critical questions about how data science is often practiced, and envision ethical and effective science practice in their current and future organiational roles.
The format of the class will be a mix of lecture, discussion, in-class activities, and qualitative and quantitative research assignments. Students will work in small groups for in-class activities, and work independently on all class project deliverables and homework assignments. Instructors will provide guidance in completing the exercises each week.
By the end of this course, students will be able to: `
- Analyze large and complex data effectively and ethically with an understanding of human, societal, and socio-technical contexts.
- Take into account the ethical, social, organizational, and legal considerations when designing algorithms and performing large-scale data analysis.
- Combine quantitative and qualitative research methods to generate critical insights into human behavior.
- Discuss and evaluate ethical, social and legal trade-offs of different data analysis, testing, curation, and sharing methods.
