User:Groceryheist/drafts/Data Science Syllabus

From CommunityData
Data Science and Organizational Communication
Principal instructor
Nate TeBlunthuis
Course Catalog 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.

Course Description

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 practiced 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.

The course is designed around two high-stakes projects. In the first stage of the students will attend the Community Data Science Workshop (CDSC). I am one of the organizers and instructors of this three week intensive workshop on basic programming and data analysis skills. The first course project is to apply these skills together with the conceptual material from this course we have covered so far to conduct an original data analysis on a topic of the student's interest. The second high-stakes project is a critical analysis of an organization or work team. For this project students will serve as consultants to an organizational unit involved in data science. Through interviews and workplace observations they will gain an understanding of the socio-technical and organizational context of their team. They will then synthesize this understanding with the knowledge they gained from the course material to compose a report offering actionable insights to their team.

Learning Objectives

By the end of this course, students will be able to:

  • Understand what it means to analyze large and complex data effectively and ethically with an understanding of human, societal, organizational, and socio-technical contexts.
  • Consider the account ethical, social, organizational, and legal considerations of data science in organizational and institutional contexts.
  • Combine quantitative and qualitative data to generate critical insights into human behavior.
  • Discuss and evaluate ethical, social, organizational and legal trade-offs of different data analysis, testing, curation, and sharing methods.

Schedule

Course schedule (click to expand)

This page is a work in progress.


Week 1:

Introduction to Human Centered Data Science
What is data science? What is human centered? What is human centered data science?
Assignments due


Readings assigned
Homework assigned
  • Reading reflection
  • Attend week 2 of CDSW




Week 2:

Ethical considerations
privacy, informed consent and user treatment
Assignments due
  • Week 1 reading reflection


Readings assigned
Homework assigned




Week 3

Reproducibility and Accountability
data curation, preservation, documentation, and archiving; best practices for open scientific research
Assignments due
  • Week 2 reading reflection
  • Attend week 2 of CDSW


Readings assigned
Homework assigned
  • Reading reflection
  • Attend week 3 of CDSW






Week 4: October 18

Interrogating datasets
causes and consequences of bias in data; best practices for selecting, describing, and implementing training data


Assignments due
  • Reading reflection


Readings assigned (Read both, reflect on one)
  • Barley, S. R. (1986). Technology as an occasion for structuring: evidence from observations of ct scanners and the social order of radiology departments. Administrative Science Quarterly, 31(1), 78–108.
  • Orlikowski, W. J., & Barley, S. R. (2001). Technology and institutions: what can research on information technology and research on organizations learn from each other? MIS Q., 25(2), 145–165. https://doi.org/10.2307/3250927
Homework assigned


Resources




Week 5:

Technology and Organizing
Assignments due


Readings assigned
  • Passi, S., & Jackson, S. J. (2018). Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects. Proc. ACM Hum.-Comput. Interact., 2(CSCW), 136:1–136:28. https://doi.org/10.1145/3274405
Homework Assigned

Week 6:

Data science in Organizational Contexts
Assignments due
Readings assigned (Read both, reflect on one)

Week 7: October 25

Day 5 plan

Day 5 slides

Introduction to mixed-methods research
Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing


Assignments due
  • Reading reflection


Readings assigned (Read both, reflect on one)


Homework assigned






Week 6: November 1

Day 6 plan

Day 6 slides

Interrogating algorithms
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
  • Reading reflection


Readings assigned
Homework assigned
  • Reading reflection
Resources




Week 7: November 8

Day 7 plan

Day 7 slides

Critical approaches to data science
power, data, and society; ethics of crowdwork


Assignments due


Readings assigned (read both, reflect on one)
Homework assigned




Week 8: November 15

Day 8 plan

Day 8 slides

Human-centered algorithm design
algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems
Assignments due
  • Reading reflection


Readings assigned
Homework assigned
  • Reading reflection
Resources




Week 9: November 22 (No Class Session)

Day 9 plan

Data science for social good
Community-based and participatory approaches to data science; Using data science for society's benefit
Assignments due
  • Reading reflection
  • A4: Final project plan
Agenda
  • Reading reflections discussion
  • Feedback on Final Project Plans
  • Guest lecture: Steven Drucker (Microsoft Research)
  • UI patterns & UX considerations for ML/data-driven applications
  • Final project presentation: what to expect
  • In-class activity: final project peer review


Readings assigned
Homework assigned
  • Reading reflection
Resources





Week 10: November 29

Day 10 plan

Day 10 slides

User experience and big data
Design considerations for machine learning applications; human centered data visualization; data storytelling
Assignments due
  • Reading reflection
Agenda
  • Reading reflections discussion
  • Feedback on Final Project Plans
  • Guest lecture: Steven Drucker (Microsoft Research)
  • UI patterns & UX considerations for ML/data-driven applications
  • Final project presentation: what to expect
  • In-class activity: final project peer review


Readings assigned
  • NONE
Homework assigned
  • A5: Final presentation
Resources




Week 11: December 6

Day 11 plan

Final presentations
course wrap up, presentation of student projects


Assignments due
  • A5: Final presentation


Agenda
  • Student final presentations
  • Course wrap-up


Readings assigned
  • none!
Homework assigned
  • A6: Final project report (by 11:59pm)
Resources
  • one



Week 12: Finals Week (No Class Session)

  • NO CLASS
  • A6: FINAL PROJECT REPORT DUE BY 11:59PM
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