HCDS (Fall 2017)/Schedule: Difference between revisions

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* Tene, Omer and Polonetsky, Jules. [https://www.stanfordlawreview.org/online/privacy-paradox-privacy-and-big-data/ ''Privacy in the Age of Big Data.''] Stanford Law Review, 2012.
* Tene, Omer and Polonetsky, Jules. [https://www.stanfordlawreview.org/online/privacy-paradox-privacy-and-big-data/ ''Privacy in the Age of Big Data.''] Stanford Law Review, 2012.
* National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. [https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html ''The Belmont Report.''] U.S. Department of Health and Human Services, 1979.
* National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. [https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html ''The Belmont Report.''] U.S. Department of Health and Human Services, 1979.
* Fiesler, Casey. [https://medium.com/@cfiesler/law-ethics-of-scraping-what-hiq-v-linkedin-could-mean-for-researchers-violating-tos-787bd3322540 ''Law & Ethics of Scraping: What HiQ v LinkedIn Could Mean for Researchers Violating TOS.''] Medium, 2017.
* [http://www.eugdpr.org/the-regulation.html ''EU General Data Protection Regulation.'']
* [http://www.eugdpr.org/the-regulation.html ''EU General Data Protection Regulation.'']
* Green, Matthew. [https://blog.cryptographyengineering.com/2016/06/15/what-is-differential-privacy/ ''What is Differential Privacy?''] A Few Thoughts on Cryptographic Engineering, 2016.
* Green, Matthew. [https://blog.cryptographyengineering.com/2016/06/15/what-is-differential-privacy/ ''What is Differential Privacy?''] A Few Thoughts on Cryptographic Engineering, 2016.
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* Christensen, Garret. [https://github.com/garretchristensen/BestPracticesManual/blob/master/Manual.pdf ''Manual of Best Practices in Transparent Social Science Research.''] 2016.
* Christensen, Garret. [https://github.com/garretchristensen/BestPracticesManual/blob/master/Manual.pdf ''Manual of Best Practices in Transparent Social Science Research.''] 2016.
* Chapman et al. [ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf ''Cross Industry Standard Process for Data Mining'']. IBM, 2000.
* Chapman et al. [ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf ''Cross Industry Standard Process for Data Mining'']. IBM, 2000.
* Fiesler, Casey. [https://medium.com/@cfiesler/law-ethics-of-scraping-what-hiq-v-linkedin-could-mean-for-researchers-violating-tos-787bd3322540 ''Law & Ethics of Scraping: What HiQ v LinkedIn Could Mean for Researchers Violating TOS.''] Medium, 2017.


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Revision as of 22:14, 2 October 2017

This page is a work in progress.
Last updated on 08/08/2018 by Jtmorgan


Week 1: September 28

Day 1 plan

Assignments due
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

Day 2 plan

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
Homework assigned
  • Reading reflection
  • A1: privacy and anonymity
Resources




Week 3: October 12

Day 3 plan

Data provenance, preparation, and reproducibility
data curation, preservation, documentation, and archiving; best practices for open scientific research
Assignments due
  • Week 2 reading reflection
  • A1: Privacy and anonymity
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
  • Reading reflection
  • A2: Data curation


Resources




Week 4: October 19

Day 4 plan

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


Assignments due
  • Reading reflection
  • A2: 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
  • A3: Measuring bias in data


Resources




Week 5: October 26

Day 5 plan

Machine learning
ethical AI, algorithmic transparency, societal implications of machine learning
Assignments due
  • Reading reflection
  • A3: Measuring bias in data
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
  • A4: Auditing algorithms


Resources




Week 6: November 2

Day 6 plan

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


Assignments due
  • Reading reflection
  • A4: Auditing algorithms


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
  • A5: Final project plan


Resources




Week 7: November 9

Day 7 plan

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


Assignments due
  • Reading reflection
  • A5: 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
Homework assigned
  • Reading reflection
  • A6: Self-ethnography


Resources
  • go here




Week 8: November 16

Day 8 plan

User experience and big data
prototyping and user testing; benchmarking and iterative evaluation; UI design for data science


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

Day 9 plan

Human-centered data science in the wild
community data science; data science for social good
Assignments due
  • Reading reflection
  • A6: Crowdwork self-ethnography
Agenda
  • NO CLASS - work on your own


Readings assigned
Homework assigned
  • Reading reflection
Resources




Week 10: November 30

Day 10 plan

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
  • A7: Final presentation
Resources
  • one




Week 11: December 7

Day 11 plan

Future of human centered data science
case studies from research, industry, and policy; final presentations


Assignments due
  • Reading reflection
  • 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

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