Editing HCDS (Fall 2017)

;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. 
;Principal instructor: [http://jtmorgan.net Jonathan T. Morgan]
;Co-instructor: Oliver Keyes 
;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.

;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>

== Overview and learning objectives ==
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 ==
''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

=== Jupyter Hub ===
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.

=== 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]].

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

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

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''[[HCDS (Fall 2017)/Readings]]''
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=== Grading ===
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 ==
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 ===

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 ===
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: [https://www.hcde.washington.edu/policies/plagiarism-and-academic-conduct HCDE Academic Conduct]. These will be strictly enforced. 


=== Disability and accommodations ===

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

== References ==

[[Category:HCDS (Fall 2017)]]