HCDS (Fall 2017)/Schedule

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Week 1: September 28[edit]

Day 1 plan

Day 1 slides

Course overview
What is data science? What is human centered? What is human centered data science?
Assignments due
  • fill out the pre-course survey
Agenda
  • Course overview & orientation
  • What do we mean by "data science?"
  • What do we mean by "human centered?"
  • How does human centered design relate to data science?


Readings assigned
Homework assigned
  • Reading reflection
Resources




Week 2: October 5[edit]

Day 2 plan

Day 2 slides

Ethical considerations in Data Science
privacy, informed consent and user treatment


Assignments due
  • Week 1 reading reflection
Agenda
  • Informed consent in the age of Data Science
  • Privacy
    • User expectations
    • Inferred information
    • Correlation
  • Anonymisation strategies


Readings assigned
  • Read: Markham, Annette and Buchanan, Elizabeth. Ethical Decision-Making and Internet Researchers. Association for Internet Research, 2012.
  • Read: Barocas, Solan and Nissenbaum, Helen. Big Data's End Run around Anonymity and Consent. In Privacy, Big Data, and the Public Good. 2014. (PDF on Canvas)
Homework assigned
  • Reading reflection
Resources




Week 3: October 12[edit]

Day 3 plan

Day 3 slides

Data provenance, preparation, and reproducibility
data curation, preservation, documentation, and archiving; best practices for open scientific research
Assignments due
  • Week 2 reading reflection
Agenda
  • Final project overview
  • Introduction to open research
  • Understanding data licensing and attribution
  • Supporting replicability and reproducibility
  • Making your research and data accessible
  • Working with Wikipedia datasets
  • Assignment 1 description


Readings assigned
Homework assigned
Examples of well-documented open research projects
Examples of not-so-well documented open research projects
Other resources





Week 4: October 19[edit]

Day 4 plan

Day 4 slides

Study design
understanding your data; framing research questions; planning your study


Assignments due
  • Reading reflection
  • A1: Data curation
Agenda
  • How Wikipedia works (and how it doesn't)
  • guest speaker: Morten Warnke-Wang, Wikimedia Foundation
  • Sources of bias in data science research
  • Sources of bias in Wikipedia data


Readings assigned


Homework assigned
  • Reading reflection
  • A2: Bias in data


Resources




Week 5: October 26[edit]

Day 5 plan

Day 5 slides

Machine learning
ethical AI, algorithmic transparency, societal implications of machine learning
Assignments due
  • Reading reflection
Agenda
  • Social implications of machine learning
  • Consequences of algorithmic bias
  • Sources of algorithmic bias
  • Addressing algorithmic bias
  • Auditing algorithms


Readings assigned
Homework assigned
  • Reading reflection
  • A3: Final project plan


Resources




Week 6: November 2[edit]

Day 6 plan

Day 6 slides

Mixed-methods research
Big data vs thick data; qualitative research in data science


Assignments due
  • Reading reflection
  • A2: Bias in data


Agenda
  • Guest speakers: Aaron Halfaker, Caroline Sinders (Wikimedia Foundation)
  • Mixed methods research
  • Ethnographic methods in data science
  • Project plan brainstorm/Q&A session


Readings assigned
Homework assigned
  • Reading reflection


Resources




Week 7: November 9[edit]

Day 7 plan

Human computation
ethics of crowdwork, crowdsourcing methodologies for analysis, design, and evaluation


Assignments due
  • Reading reflection
  • A3: Final project plan


Agenda
  • the role of qualitative research in human centered data science
  • scaling qualitative research through crowdsourcing
  • types of crowdwork
  • ethical and practical considerations for crowdwork
  • Introduction to assignment 4: Mechanical Turk ethnography


Readings assigned (read both, reflect on one)
Homework assigned
  • Reading reflection
  • A4: Crowdwork ethnography


Resources




Week 8: November 16[edit]

Day 8 plan

Day 8 slides

User experience and big data
user-centered design and evaluation of recommender systems; UI design for data science, collaborative visual analytics


Assignments due
  • Reading reflection
Agenda
  • HCD process in the design of data-driven applications
  • understanding user needs, user intent, and context of use in recommender system design
  • trust, empowerment, and seamful design
  • HCD in data analysis and visualization
  • final project lightning feedback sessions


Readings assigned
Homework assigned
  • Reading reflection


Resources




Week 9: November 23[edit]

Day 9 plan

Human-centered data science in the wild
community data science; data science for social good
Assignments due
  • Reading reflection
  • A4: Crowdwork ethnography
Agenda
  • NO CLASS - work on your own


Readings assigned
Homework assigned
  • Reading reflection
Resources




Week 10: November 30[edit]

Day 10 plan

Day 10 slides

Communicating methods, results, and implications
translating for non-data scientists


Assignments due
  • Reading reflection


Agenda
  • communicating about your research effectively and honestly to different audiences
  • publishing your research openly
  • disseminating your research
  • final project workshop


Readings assigned


Homework assigned
  • Reading reflection
  • A5: Final presentation
Resources




Week 11: December 7[edit]

Day 11 plan

Future of human centered data science
course wrap up, final presentations


Assignments due
  • Reading reflection
  • A5: Final presentation


Agenda
  • future directions of of human centered data science
  • final presentations


Readings assigned
  • none!
Homework assigned
  • none!
Resources
  • one




Week 12: Finals Week[edit]

  • NO CLASS
  • A6: FINAL PROJECT REPORT DUE BY 11:59PM on Sunday, December 10
  • LATE PROJECT SUBMISSIONS NOT ACCEPTED.