Difference between revisions of "Human Centered Data Science (Fall 2019)/Schedule"

From CommunityData
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* Jess Holbrook. ''[https://medium.com/google-design/human-centered-machine-learning-a770d10562cd Human Centered Machine Learning].'' Google Design Blog. 2017.
 
* Jess Holbrook. ''[https://medium.com/google-design/human-centered-machine-learning-a770d10562cd Human Centered Machine Learning].'' Google Design Blog. 2017.
 
* Anderson, Carl. ''[https://medium.com/@leapingllamas/the-role-of-model-interpretability-in-data-science-703918f64330 The role of model interpretability in data science].'' Medium, 2016.
 
* Anderson, Carl. ''[https://medium.com/@leapingllamas/the-role-of-model-interpretability-in-data-science-703918f64330 The role of model interpretability in data science].'' Medium, 2016.
 
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*Fabien Girardin. ''[https://medium.com/@girardin/experience-design-in-the-machine-learning-era-e16c87f4f2e2 Experience design in the machine learning era].'' Medium, 2016.
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* Xavier Amatriain and Justin Basilico. ''[https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429 Netflix Recommendations: Beyond the 5 stars].'' Netflix Tech Blog, 2012.
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* Jess Holbrook. ''[https://medium.com/google-design/human-centered-machine-learning-a770d10562cd Human Centered Machine Learning].'' Google Design Blog. 2017.
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* Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. ''[https://pure.tue.nl/ws/files/3484177/724656348730405.pdf Explaining the user experience of recommender systems].'' User Modeling and User-Adapted Interaction 22, 4-5 (October 2012), 441-504. DOI=http://dx.doi.org/10.1007/s11257-011-9118-4
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* Patrick Austin, ''[https://gizmodo.com/facebook-google-and-microsoft-use-design-to-trick-you-1827168534 Facebook, Google, and Microsoft Use Design to Trick You Into Handing Over Your Data, New Report Warns].'' Gizmodo, 6/18/2018
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* Cremonesi, P., Elahi, M., & Garzotto, F. (2017). ''[https://core.ac.uk/download/pdf/74313597.pdf User interface patterns in recommendation-empowered content intensive multimedia applications].'' Multimedia Tools and Applications, 76(4), 5275-5309.
 
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Revision as of 23:37, 15 September 2019

This page is a work in progress.


Week 1: September 26

Day 1 plan

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

HCDS (Fall 2019)/Day 1 plan

Homework assigned
  • Read and reflect on both:
Resources




Week 2: October 3

Day 2 plan


Reproducibility and Accountability
data curation, preservation, documentation, and archiving; best practices for open scientific research
Assignments due
  • Week 1 reading reflection
  • A1: Data curation
Agenda

HCDS (Fall 2019)/Day 2 plan

Homework assigned
Resources





Week 3: October 10

Day 3 plan

Interrogating datasets
causes and consequences of bias in data; best practices for selecting, describing, and implementing training data
Assignments due
  • Week 2 reading reflection
Agenda

HCDS (Fall 2019)/Day 3 plan

Homework assigned
  • Read both, reflect on one:
Resources




Week 4: October 17

Day 4 plan


Introduction to mixed-methods research
Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing
Assignments due
  • Week 3 reading reflection
  • A2: Bias in data
Agenda

HCDS (Fall 2019)/Day 4 plan

Homework assigned
Qualitative research methods resources
Crowdwork research resources




Week 5: October 24

Day 5 plan

Research ethics for big data
privacy, informed consent and user treatment
Assignments due
  • Reading reflection
Agenda

HCDS (Fall 2019)/Day 5 plan

Homework assigned
  • Read and reflect: Gray, M. L., & Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Eamon Dolan Books. (PDF available on Canvas)
Resources




Week 6: October 31

Day 6 plan

Data science and society
power, data, and society; ethics of crowdwork
Assignments due
  • Reading reflection
  • A3: Crowdwork ethnography
Agenda

HCDS (Fall 2019)/Day 6 plan

Homework assigned
  • Read both, reflect on one:
Resources




Week 7: November 7

Day 7 plan

Human centered machine learning
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
  • Reading reflection
  • A4: Project proposal
Agenda

HCDS (Fall 2019)/Day 7 plan

Homework assigned
Resources




Week 8: November 14

Day 8 plan

User experience and data science
algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems
Assignments due
  • Reading reflection
  • A5: Final project plan
Agenda

HCDS (Fall 2019)/Day 8 plan

Homework assigned
Resources




Week 9: November 21

Day 9 plan

Data science in context
Doing human centered datascience in product organizations; communicating across roles and disciplines; data science for social good
Assignments due
  • Reading reflection
Agenda

HCDS (Fall 2019)/Day 9 plan

Homework assigned
Resources




Week 10: November 28 (No Class Session)

Assignments due
  • Reading reflection
Readings assigned
  • NONE
Homework assigned
  • NONE
Resources




Week 11: December 5

Final presentations
presentation of student projects, course wrap up
Assignments due
  • Reading reflection
  • A5: Final presentation
Readings assigned
  • NONE
Homework assigned
  • NONE
Resources
  • NONE




Week 12: Finals Week (No Class Session)

  • NO CLASS
  • A7: FINAL PROJECT REPORT DUE BY 5:00PM on Tuesday, December 10 via Canvas
  • LATE PROJECT SUBMISSIONS NOT ACCEPTED.