Human Centered Data Science (Fall 2018)/Schedule

From CommunityData
Revision as of 20:47, 15 August 2018 by Jtmorgan (talk | contribs) (Created page with "<noinclude> <div style="font-family:Rockwell,'Courier Bold',Courier,Georgia,'Times New Roman',Times,serif; min-width:10em;"> <div style="float:left; width:100%; margin-right:2...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
This page is a work in progress.


Week 1: September 28

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
  • Syllabus review
  • Pre-course survey results
  • What do we mean by data science?
  • What do we mean by human centered?
  • How does human centered design relate to data science?
  • Looking ahead: Week 2 assignments and topics


Readings assigned
Homework assigned
  • Reading reflection
Resources




Week 2: October 5

Day 2 plan

Day 2 slides

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


Assignments due
  • Week 1 reading reflection
Agenda
  • Intro to assignment 1: Data Curation
  • A brief history of research ethics
  • Guest lecture: Javier Salido and Mark van Hollebeke, "A Practitioners View of Privacy & Data Protection"
  • Guest lecture: Javier Salido, "Differential Privacy"
  • Contextual Integrity in data science
  • Week 2 reading reflection


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

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
  • Six Provocations for Big Data: Review & Reflections
  • A primer on copyright, licensing, and hosting for code and data
  • Introduction to replicability, reproducibility, and open research
  • Reproducibility case study: fivethirtyeight.com
  • Group activity: assessing reproducibility in data journalism
  • Overview of Assignment 1: Data curation


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

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
  • Final project: Goal, timeline, and deliverables.
  • Overview of assignment 2: Bias in data
  • Reading reflections review
  • Sources of bias in datasets
  • Introduction to assignment 2: Bias in data
  • Sources of bias in data collection and processing
  • In-class exercise: assessing bias in training data


Readings assigned


Homework assigned
  • Reading reflection
  • A2: Bias in data


Resources




Week 5: October 26

Day 5 plan

Day 5 slides

Machine learning
ethical AI, algorithmic transparency, societal implications of machine learning
Assignments due
  • Reading reflection
Agenda
  • Assignment 1 review & reflection
  • Week 4 reading reflection discussion
  • Survey of qualitative research methods
  • Mixed-methods case study #1: The Wikipedia Gender Gap: causes & consequences
  • In-class activity: Automated Gender Recognition scenarios
  • Introduction to ethnography
  • Ethnographic research case study: Structured data on Wikimedia Commons
  • Introduction to crowdwork
  • Overview of Assignment 3: Crowdwork ethnography


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


Resources




Week 6: November 2

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
  • Reading reflections
  • Ethical implications of crowdwork
  • Algorithmic transparency, interpretability, and accountability
  • Auditing algorithms
  • In-class activity: auditing the Perspective API



Readings assigned
Homework assigned
  • Reading reflection


Resources




Week 7: November 9

Day 7 plan

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


Assignments due
  • Reading reflection
  • A3: Final project plan


Agenda
  • Guest lecture: Rochelle LaPlante


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


Resources




Week 8: November 16

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
  • Final project overview & examples
  • Guest Lecture: Kelly Franznick, Blink UX
  • Reading reflections
  • Human-centered algorithm design
  • design process
  • user-driven evaluation
  • design patterns & anti-patterns


Readings assigned
Homework assigned
  • Reading reflection


Resources




Week 9: November 23

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
  • 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 30

Day 10 plan

Day 10 slides

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


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


Homework assigned
  • Reading reflection
  • A5: Final presentation
Resources




Week 11: December 7

Day 11 plan

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


Assignments due
  • Reading reflection
  • A5: Final presentation


Agenda
  • Student final presentations
  • Course wrap-up


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




Week 12: Finals Week

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