HCDS (Fall 2017)

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Last updated on 08/08/2017 by Jtmorgan
Human Centered Data Science
DATA 512 - UW Interdisciplinary Data Science Masters Program - Thursdays 5:00-9:50pm in Denny Hall 112.
Instructor
Jonathan T. Morgan
TA
Oliver Keyes
Course Website
We will use Canvas for announcements and turning in reading reflections, PAWS for turning in code, and Slack for Q&A and general discussion. All other course-related information will be linked on this page.
Course Description
Fundamental principles of data science and its human implications. Data ethics, data privacy, differential privacy, algorithmic bias, legal frameworks and intellectual property, provenance and reproducibility, data curation and preservation, user experience design and usability testing for big data, ethics of crowdwork, data communication and societal impacts of data science.[1]

Overview and learning objectives[edit]

The format of the class will be a mix of lecture, discussion, analyzing data, in-class activities, short essay assignments, and programming exercises. Students will work in small groups. Instructors will provide guidance in completing the exercises each week.

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

  • Analyze large and complex data effectively and ethically with an understanding of human, societal, and socio-technical contexts.
  • Develop algorithms that take into account the ethical, social, and legal considerations of large-scale data analysis.
  • Discuss and evaluate ethical, social and legal trade-offs of different data analysis, testing, curation, and sharing methods


Schedule[edit]

HCDS (Fall 2017)/Schedule

Course schedule (click to expand)


Week 1: September 28[edit]

Assignments due
  • fill out the pre-course survey
Agenda


Homework


Resources



Week 2: October 5[edit]

Assignments due


Agenda


Homework


Resources



Week 3: October 12[edit]

Assignments due


Agenda


Homework
Resources
  • go here



Week 4: October 19[edit]

Assignments due


Agenda


Homework


Resources



Week 5: October 26[edit]

Assignments due
Agenda


Homework


Resources



Week 6: November 2[edit]

Assignments due


Agenda


Homework


Resources



Week 7: November 9[edit]

Assignments due


Agenda


Resources
  • go here



Week 8: November 16[edit]

Assignments due
Agenda


Resources



Week 9: November 23[edit]

NO CLASS


Agenda


Resources




Week 10: November 30[edit]

Assignments due
Agenda
Resources
  • one



Week 11: December 7[edit]

Assignments due



Agenda
Resources
  • one



Week 12: December 14[edit]

FINALS WEEK - NO CLASS - ALL ASSIGNMENTS DUE BY FIXME

Assignments[edit]

HCDS (Fall 2017)/Assignments

Graded assignments (click to expand)


coming soon

Readings[edit]

HCDS (Fall 2017)/Readings

Course reading list (click to expand)


coming soon

Administrative notes[edit]

Grading[edit]

Grades will be determined as follows:

  • 20% in-class work
  • 20% readings/reading groups
  • 60% assignments

Late assignments will not be accepted after the first week of class. In-class work and class participation cannot be made up. If you miss a class, you will receive a zero for the work done in class that day. Please do not ask the professor or TA what you missed during class; check the website or ask a classmate. Required posts to the class discussion board must be made before the due date or you will receive a zero for that work.

Final projects cannot be turned in late.

Policies[edit]

The following general policies apply to this course:

Respect
If there were only one policy allowed in a course syllabus, I would choose the word respect to represent our goals for a healthy and engaging educational environment. Treating each other respectfully, in the broadest sense and in all ways, is a necessary and probably sufficient condition for a successful experience together.
Attendance
Students are expected to attend class regularly.
Late Assignments
Late assignments will not be accepted. If your assignment is late, you will receive a zero score.
Participation
Active participation in class activities is one of the requirements of the course. You are expected to engage in group activities, class discussions, interactions with your peers, and constructive critiques as part of the course work. This will help you hone your communication and other professional skills.
Collaboration
Working in groups or on teams is an essential part of all data science disciplines. As part of this course, you will be asked to provide feedback of your peers' work.
Academic Integrity
Simply stated, academic integrity means that you are to do your own work in all of your classes, unless collaboration is part of an assignment as defined in the course. In any case, you must be responsible for citing and acknowledging outside sources of ideas in work you submit. Please be aware of the HCDE Department's and the UW's policies on this: HCDE Academic Conduct. These will be strictly enforced.
Assignment Quality
You are expected to produce work in all of the assignments that reflects the highest standards of professionalism. For written documents, this means proper spelling, grammar, and formatting.
Privacy
Students have the right for aspects of their personal life that they do not wish to share with others to remain private. Please respect that policy.
Accommodations
To request academic accommodations due to a disability, please contact Disabled Student Services: 448 Schmitz, 206-543-8924 (V/TTY). If you have a letter from DSS indicating that you have a disability which requires academic accommodations, please present the letter to me so you can discuss the accommodations you might need in the class.
Permissions
Unless you notify me otherwise, I will assume you will allow me to use samples from your work in this course in future instructional settings.
Disclaimer
This syllabus and all associated assignments, requirements, deadlines and procedures are subject to change.

References[edit]