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;Human Centered Data Science: [https://sdb.admin.uw.edu/timeschd/uwnetid/sln.asp?QTRYR=AUT+2017&SLN=23273 DATA 512] - [https://www.datasciencemasters.uw.edu/ UW Interdisciplinary Data Science Masters Program] - Thursdays 5:00-9:50pm in [http://www.washington.edu/maps/#!/den Denny Hall] 112.
<div style="float:left; width:100%; margin-right:2%;">
;Principal instructor: [http://jtmorgan.net Jonathan T. Morgan]
{{Link/Graphic/Main/2
;Co-instructor: Oliver Keyes
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;Course Website: ''This'' page is the canonical information resource for DATA512. We will use [https://canvas.uw.edu/courses/1174178 the Canvas site] for announcements, file hosting, and submitting reading reflections and graded in-class assignments. We will use Jupyter Hub (see Canvas for link) for turning in other programming and writing assignments, and Slack for Q&A and general discussion. All other course-related information will be linked on this page.
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|top text=This page is a work in progress.
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|bottom text=Last updated: 19:08, 15 July 2017 (EDT)
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;Course Description: Fundamental principles of data science and its human implications. Data ethics, data privacy, algorithmic bias, legal frameworks, provenance and reproducibility, data curation and preservation, user experience design and research for big data, ethics of crowdwork, data communication, and societal impacts of data science.<ref>https://www.washington.edu/students/crscat/data.html#data512</ref>


;Human Centered Data Science: DATA 512 - Interdisciplinary Data Science Masters Program
== Overview and learning objectives ==
;Instructor: [http://jtmorgan.net Jonathan T. Morgan]
;TA: Oliver Keyes
;Course Website: We will use Canvas for [https://canvas.uw.edu/courses/FIXME/announcements announcements] and [https://canvas.uw.edu/courses/FIXME 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.
 
== Goals and expectations ==
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.
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.


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


Line 35: Line 15:
* Discuss and evaluate ethical, social and legal trade-offs of different data analysis, testing, curation, and sharing methods
* Discuss and evaluate ethical, social and legal trade-offs of different data analysis, testing, curation, and sharing methods


== Grading ==
== Course resources ==
Grades will be determined as follows:  
''All pages and files on this wiki that are related to the Fall 2017 edition of DATA 512: Human-Centered Data Science are listed in [[:Category:HCDS (Fall 2017)]].''
 
=== Office hours ===
* Oliver: Monday (4pm-6pm) and Tuesday (4-7pm), Sieg 431, and by request.
* Jonathan: Google Hangout, by request


* 20% in-class work
=== Jupyter Hub ===
* 20% readings/reading groups
The course will use a [http://jupyter.org/ Jupyter Hub] provided by [http://westbigdatahub.org/ West Big Data Hub] and administered by [https://bids.berkeley.edu/people/yuvi-panda Yuvi Panda] at the Berkeley Institute for Data Science. Students use Jupyter notebooks for in-class and homework assignments that involve a combination of programming, analysis, documentation, and reflection. Allowing students to work in a shared, online environment reinforces best practices around open research such as transparency, iteration, and reproducibility. It also helps teaches them how to tell the story of their research using multiple media (code, data, prose, and visualizations), making it more accessible and impactful for a wider variety of audiences.
* 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.  
=== Datasets ===
For some examples of datasets you could use for your [[HCDS_(Fall_2017)/Assignments#A3:_Final_project_plan|final project]], see [[HCDS_(Fall_2017)/Datasets]].


Final projects cannot be turned in late.  
=== Lecture slides ===
Slides for most weekly lectures are available in PDF form.  


* [[:File:HCDS_Week_1_slides.pdf|Week 1 slides]]
* [[:File:HCDS_Week_2_slides.pdf|Week 2 slides]]
* [[:File:HCDS_Week_3_slides.pdf|Week 3 slides]]
* [[:File:HCDS_Week_4_slides.pdf|Week 4 slides]]
* [[:File:HCDS_Week_5_slides.pdf|Week 5 slides]]
* [[:File:HCDS_Week_6_slides.pdf|Week 6 slides]]
* [[:File:HCDS_Week_8_slides.pdf|Week 8 slides]]
* [[:File:HCDS_Week_10_slides.pdf|Week 10 slides]]


== Schedule ==
== Schedule ==
''[[HCDS (Fall 2017)/Schedule]]''
''[[HCDS (Fall 2017)/Schedule]]''
 
<div class="toccolours mw-collapsible">
Course schedule (click to expand)
<div class="mw-collapsible-content">
{{:HCDS (Fall 2017)/Schedule}}
{{:HCDS (Fall 2017)/Schedule}}
 
</div>
</div>


== Assignments ==
== Assignments ==
''[[HCDS (Fall 2017)/Assignments]]''
''For details on individual assignments, see [[HCDS (Fall 2017)/Assignments]]''


{{:HCDS (Fall 2017)/Assignments}}
{{:HCDS (Fall 2017)/Assignments}}


 
<!--== Readings ==
== Readings ==
''[[HCDS (Fall 2017)/Readings]]''
''[[HCDS (Fall 2017)/Readings]]''
 
<div class="toccolours mw-collapsible">
Course reading list (click to expand)
<div class="mw-collapsible-content">
{{:HCDS (Fall 2017)/Readings}}
{{:HCDS (Fall 2017)/Readings}}
</div>
</div>-->




== Policies ==
== Policies ==
The following general policies apply to this course:  
The following general policies apply to this course.
 
=== Respect ===
Students are expected to treat each other, and the instructors, with respect. Students are prohibited from engaging in any kind of harassment or derogatory behaviour, which includes offensive verbal comments or imagery related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, or religion. In addition, students should not engage in any form of inappropriate physical contact or unwelcome sexual attention, and should respect each others’ right to privacy in regards to their personal life. In the event that you feel you (or another student) have been subject to a violation of this policy, please reach out to the instructors in whichever form you prefer.
 
The instructors are committed to providing a safe and healthy learning environment for students. As part of this, students are asked not to wear any clothing, jewelry, or any related medium for symbolic expression which depicts an indigenous person or cultural expression re­appropriated as a mascot, logo, or caricature. These include, but are not limited to, iconography associated with the following sports teams:
 
# Chicago Blackhawks
# Washington Redskins
# Cleveland Indians
# Atlanta Braves
 
=== Attendance and participation ===
 
Students are expected to attend class regularly. If you run into a conflict that requires you to be absent (for example, medical issues) feel free to reach out to the instructors. We will do our best to ensure that you don’t miss out, and treat your information as confidential.
 
If you miss class session, please do not ask the professor or TA what you missed during class; check the website or ask a classmate (best bet: use Slack). Graded in-class activities cannot be made up if you miss a class session.
 
=== Grading ===


;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.  
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. Correspondingly, 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.


;Attendance: Students are expected to attend class regularly.  
The following grading scheme will be used to evaluate each of the 6 individual assignments (not reading reflections or graded in-class activities).  


;Late Assignments: Late assignments will not be accepted. If your assignment is late, you will receive a zero score.
;81-100% - Exceptional: The student demonstrated novelty or insight beyond the specific requirements of the assignment.
 
;61-80% - Competent: The student competently and confidently addressed requirements to a good standard.
 
;41-60% - Acceptable: The student met the absolute minimum requirements for the assignment.
 
;21-40% - Partial: The student submitted something, but only addressed some of the assignment requirements or they submitted work that was poor quality overall.
 
;1-20% - Submitted: The student submitted something.
 
Individual assignments will have specific requirements listed on the assignment sheet, which the instructor will make available on the day the homework is assigned. If you have questions about how your assignment was graded, please see the TA or instructor.
 
=== Assignments and coursework ===
Grades will be determined as follows:
 
* 20% in-class work
* 20% reading reflections
* 60% assignments


;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.
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.


;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.  
Late assignments will not be accepted; if your assignment is late, you will receive a zero score. Again, if you run into an issue that necessitates an extension, please reach out. Final projects cannot be turned in late and are not eligible for any extension whatsoever.


;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: [https://www.hcde.washington.edu/policies/plagiarism-and-academic-conduct HCDE Academic Conduct]. These will be strictly enforced.  
Students are expected to adhere to rules around 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: [https://www.hcde.washington.edu/policies/plagiarism-and-academic-conduct 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.
=== Disability and accommodations ===


;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.  
As part of ensuring that the class is as accessible as possible, the instructors are entirely comfortable with you using whatever form of note-taking method or recording is most comfortable to you, including laptops and audio recording devices. The instructors will do their best to ensure that all slides and scripts/notes are immediately available online after a lecture has concluded. In addition, we are going to try and record the audio of lectures for students who are more comfortable with audiovisual notes than written ones.


;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.  
If you require additional accommodations, 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 the instructors so we can discuss the accommodations you might need in the class. If you have any questions about this policy, reach out to the instructors directly.


;Disclaimer: This syllabus and all associated assignments, requirements, deadlines and procedures are subject to change.  
== Disclaimer ==
This syllabus and all associated assignments, requirements, deadlines and procedures are subject to change.


== References ==


[[Category:HCDS (Fall 2017)]]
[[Category:HCDS (Fall 2017)]]

Latest revision as of 18:21, 8 August 2018

Human Centered Data Science
DATA 512 - UW Interdisciplinary Data Science Masters Program - Thursdays 5:00-9:50pm in Denny Hall 112.
Principal instructor
Jonathan T. Morgan
Co-instructor
Oliver Keyes
Course Website
This page is the canonical information resource for DATA512. We will use the Canvas site for announcements, file hosting, and submitting reading reflections and graded in-class assignments. We will use Jupyter Hub (see Canvas for link) for turning in other programming and writing assignments, 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, algorithmic bias, legal frameworks, provenance and reproducibility, data curation and preservation, user experience design and research 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

Course resources[edit]

All pages and files on this wiki that are related to the Fall 2017 edition of DATA 512: Human-Centered Data Science are listed in Category:HCDS (Fall 2017).

Office hours[edit]

  • Oliver: Monday (4pm-6pm) and Tuesday (4-7pm), Sieg 431, and by request.
  • Jonathan: Google Hangout, by request

Jupyter Hub[edit]

The course will use a Jupyter Hub provided by West Big Data Hub and administered by Yuvi Panda at the Berkeley Institute for Data Science. Students use Jupyter notebooks for in-class and homework assignments that involve a combination of programming, analysis, documentation, and reflection. Allowing students to work in a shared, online environment reinforces best practices around open research such as transparency, iteration, and reproducibility. It also helps teaches them how to tell the story of their research using multiple media (code, data, prose, and visualizations), making it more accessible and impactful for a wider variety of audiences.

Datasets[edit]

For some examples of datasets you could use for your final project, see HCDS_(Fall_2017)/Datasets.

Lecture slides[edit]

Slides for most weekly lectures are available in PDF form.

Schedule[edit]

HCDS (Fall 2017)/Schedule

Course schedule (click to expand)


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.

Assignments[edit]

For details on individual assignments, see HCDS (Fall 2017)/Assignments


Assignments are comprised of weekly in-class activities, weekly reading reflections, written assignments, and programming/data analysis assignments. Weekly in-class reading groups will discuss the assigned readings from the course and students are expected to have read the material in advance. In class activities each week are posted to Canvas and may require time outside of class to complete.

Unless otherwise noted, all assignments are due before 5pm on the following week's class.

Unless otherwise noted, all assignments are individual assignments.

Assignment timeline[edit]

Assignments due every week
  • In-class activities - 2 points (weekly): In-class activity output posted to Canvas (group or individual)
  • Reading reflections - 2 points (weekly): Reading reflections posted to Canvas (individual)


Scheduled assignments
  • A1 - 5 points (due Week 4): Data curation (programming/analysis)
  • A2 - 10 points (due Week 6): Sources of bias in data (programming/analysis)
  • A3 - 10 points (due Week 7): Final project plan (written)
  • A4 - 10 points (due Week 9): Crowdwork self-ethnography (written)
  • A5 - 10 points (due Week 11): Final project presentation (oral, written)
  • A6 - 15 points (due by 11:59pm on Sunday, December 10): Final project report (programming/analysis, written)

more information...



Policies[edit]

The following general policies apply to this course.

Respect[edit]

Students are expected to treat each other, and the instructors, with respect. Students are prohibited from engaging in any kind of harassment or derogatory behaviour, which includes offensive verbal comments or imagery related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, or religion. In addition, students should not engage in any form of inappropriate physical contact or unwelcome sexual attention, and should respect each others’ right to privacy in regards to their personal life. In the event that you feel you (or another student) have been subject to a violation of this policy, please reach out to the instructors in whichever form you prefer.

The instructors are committed to providing a safe and healthy learning environment for students. As part of this, students are asked not to wear any clothing, jewelry, or any related medium for symbolic expression which depicts an indigenous person or cultural expression re­appropriated as a mascot, logo, or caricature. These include, but are not limited to, iconography associated with the following sports teams:

  1. Chicago Blackhawks
  2. Washington Redskins
  3. Cleveland Indians
  4. Atlanta Braves

Attendance and participation[edit]

Students are expected to attend class regularly. If you run into a conflict that requires you to be absent (for example, medical issues) feel free to reach out to the instructors. We will do our best to ensure that you don’t miss out, and treat your information as confidential.

If you miss class session, please do not ask the professor or TA what you missed during class; check the website or ask a classmate (best bet: use Slack). Graded in-class activities cannot be made up if you miss a class session.

Grading[edit]

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. Correspondingly, 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.

The following grading scheme will be used to evaluate each of the 6 individual assignments (not reading reflections or graded in-class activities).

81-100% - Exceptional
The student demonstrated novelty or insight beyond the specific requirements of the assignment.
61-80% - Competent
The student competently and confidently addressed requirements to a good standard.
41-60% - Acceptable
The student met the absolute minimum requirements for the assignment.
21-40% - Partial
The student submitted something, but only addressed some of the assignment requirements or they submitted work that was poor quality overall.
1-20% - Submitted
The student submitted something.

Individual assignments will have specific requirements listed on the assignment sheet, which the instructor will make available on the day the homework is assigned. If you have questions about how your assignment was graded, please see the TA or instructor.

Assignments and coursework[edit]

Grades will be determined as follows:

  • 20% in-class work
  • 20% reading reflections
  • 60% assignments

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.

Late assignments will not be accepted; if your assignment is late, you will receive a zero score. Again, if you run into an issue that necessitates an extension, please reach out. Final projects cannot be turned in late and are not eligible for any extension whatsoever.

Students are expected to adhere to rules around 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.


Disability and accommodations[edit]

As part of ensuring that the class is as accessible as possible, the instructors are entirely comfortable with you using whatever form of note-taking method or recording is most comfortable to you, including laptops and audio recording devices. The instructors will do their best to ensure that all slides and scripts/notes are immediately available online after a lecture has concluded. In addition, we are going to try and record the audio of lectures for students who are more comfortable with audiovisual notes than written ones.

If you require additional accommodations, 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 the instructors so we can discuss the accommodations you might need in the class. If you have any questions about this policy, reach out to the instructors directly.

Disclaimer[edit]

This syllabus and all associated assignments, requirements, deadlines and procedures are subject to change.

References[edit]

  1. ↑ https://www.washington.edu/students/crscat/data.html#data512
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