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| The rise of "data science" reflects a broad and ongoing shift in how many teams, organizational leaders, communities of practice, and entire industries create and use knowledge. This class teaches "data science" as experienced by data-intensive knowledge workers but also as it is positioned in historical, organizational, institutional, and societal contexts. Students will gain an appriciation for the technical and intellectual aspects of data science, consider critical questions about how data science is often practiced, and envision ethical and effective science practice in their current and future organiational roles. | | The rise of "data science" reflects a broad and ongoing shift in how many teams, organizational leaders, communities of practice, and entire industries create and use knowledge. This class teaches "data science" as experienced by data-intensive knowledge workers but also as it is positioned in historical, organizational, institutional, and societal contexts. Students will gain an appriciation for the technical and intellectual aspects of data science, consider critical questions about how data science is often practiced, and envision ethical and effective science practice in their current and future organiational roles. |
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| The format of the class will be a mix of lecture, discussion, in-class activities, and qualitative and quantitative research assignments. Students will work in small groups for in-class activities, and work independently on all class project deliverables and homework assignments. Instructors will provide guidance in completing the exercises each week. | | The format of the class will be a mix of lecture, discussion, in-class activities, and qualitative and quantitative research assignments. Students will work in small groups for in-class activities, and work independently on all class project deliverables and homework assignments. The instructor will provide guidance in completing the exercises each week. |
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| By the end of this course, students will be able to: | | By the end of this course, students will be able to: |
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| * Combine quantitative and qualitative research methods to generate critical insights into human behavior. | | * Combine quantitative and qualitative research methods to generate critical insights into human behavior. |
| * 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. |
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| == Course resources ==
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| ''All pages and files on this wiki that are related to the Fall 2018 edition of DATA 512: Human-Centered Data Science are listed in [[:Category:HCDS (Fall 2018)]].''
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| === Office hours ===
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| * Os Keyes: Monday (5pm-7pm) and Wednesday (5-7pm), Sieg 431, and by request.
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| * Jonathan Morgan: Google Meet, by request
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| <!--
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| === Jupyter Hub ===
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| 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.
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| === Datasets ===
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| For some examples of datasets you could use for your [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A3:_Final_project_plan|final project]], see [[Human Centered Data Science/Datasets]].
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| === Lecture slides ===
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| Slides for weekly lectures will be available in PDF form on this wiki, generally within 24 hours of each course session
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| * [[:File:HCDS_2018_week_1_slides.pdf|Week 1 slides]]
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| * [[:File:HCDS_2018_week_2_slides.pdf|Week 2 slides]]
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| * [[:File:HCDS_2018_week_3_slides.pdf|Week 3 slides]]
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| * [[:File:HCDS_2018_week_4_slides.pdf|Week 4 slides]]
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| * [[:File:HCDS_2018_week_5_slides.pdf|Week 5 slides]]
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| * [[:File:HCDS_2018_week_6_slides.pdf|Week 6 slides]]
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| * [[:File:HCDS_2018_week_7_slides.pdf|Week 7 slides]]
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| * [[:File:HCDS_2018_week_8_slides.pdf|Week 8 slides]]
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| * [[:File:HCDS_2018_week_10_slides.pdf|Week 10 slides]]
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| == Schedule ==
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| ''Direct link: [[Human Centered Data Science (Fall 2018)/Schedule]]''
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| <div class="toccolours mw-collapsible">
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| Course schedule (click to expand)
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| <div class="mw-collapsible-content">
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| {{:Human Centered Data Science (Fall 2018)/Schedule}}
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| </div>
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| </div>
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| == Assignments ==
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| ''For details on individual assignments, see [[Human Centered Data Science (Fall 2018)/Assignments]]''
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| {{:Human Centered Data Science (Fall 2018)/Assignments}}
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| <!--== Readings ==
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| ''[[HCDS (Fall 2018)/Readings]]''
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| <div class="toccolours mw-collapsible">
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| Course reading list (click to expand)
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| <div class="mw-collapsible-content">
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| {{:HCDS (Fall 2018)/Readings}}
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| </div>
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| </div>-->
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| == Policies ==
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| The following general policies apply to this course.
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| === Respect ===
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| Students are expected to treat each other, and the instructors, with respect. Students are prohibited from engaging in any kind of harassment or derogatory behavior, 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.
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| 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:
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| # Chicago Blackhawks
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| # Washington Redskins
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| # Cleveland Indians
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| # Atlanta Braves
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| === Attendance and participation ===
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| 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.
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| 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.
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| === Grading ===
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| 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.
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| <!--
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| The following grading scheme will be used to evaluate each of the 6 individual assignments (not reading reflections or graded in-class activities).
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| ;81-100% - Exceptional: The student demonstrated novelty or insight beyond the specific requirements of the assignment.
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| ;61-80% - Competent: The student competently and confidently addressed requirements to a good standard.
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| ;41-60% - Acceptable: The student met the absolute minimum requirements for the assignment.
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| ;21-40% - Partial: The student submitted something, but only addressed some of the assignment requirements or they submitted work that was poor quality overall.
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| ;1-20% - Submitted: The student submitted something.
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| -->
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| 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.
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| === Assignments and coursework ===
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| Grades will be determined as follows:
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| * 20% in-class work
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| * 20% reading reflections
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| * 60% assignments
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| 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.
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| 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.
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| === Academic integrity and plagiarism ===
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| 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.
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| Please be aware of the HCDE Department's and the UW's policies on plagiarism and academic misconduct: [https://www.hcde.washington.edu/policies/plagiarism-and-academic-conduct HCDE Academic Conduct policy]. This policy will be strictly enforced.
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| Other academic integrity resources:
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| * [http://www.washington.edu/teaching/cheating-or-plagiarism/ Center for Teaching and Learning: Cheating or Plagiarism]
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| * [https://depts.washington.edu/grading/pdf/AcademicResponsibility.pdf University of Washington Student Academic Responsibility (PDF)]
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| === Disability and accommodations ===
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| 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, if asked ahead of time we can try to record the audio of individial lectures for students who have learning differences that make audiovisual notes preferable to written ones.
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| 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.
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| For more information on disability accommodations, and how to apply for one, please review [http://depts.washington.edu/uwdrs/current-students/accommodations/ UW's Disability Resources for Students].
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| == Disclaimer ==
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| This syllabus and all associated assignments, requirements, deadlines and procedures are subject to change.
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| == References ==
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| [[Category:Groceryheist drafts]] | | [[Category:Groceryheist drafts]] |
