Editing Human Data Interaction

'''Human Data Interaction''' (HDI) is a course offered by the [https://www.hcde.washington.edu/ UW Department of Human Centered Design and Engineering] as part of the core curriculum for the [https://escience.washington.edu/education/undergraduate/ UW Undergraduate Data Science Minor] as '''HCDE 410'''. 

The course curriculum is under development by [[User:Jtmorgan|Jonathan T. Morgan]] and [https://www.hcde.washington.edu/craft Brock Craft] for Spring quarter 2021.

Feel free to use any of the HDI course materials hosted on this wiki! We just ask that you [https://creativecommons.org/licenses/by-sa/3.0/ provide attribution] by noting that you adapted or adopted materials from this course (and if possible, link back to this wiki page). 

If you have questions or feedback related to the course, you are welcome to email Jonathan at jmo25 at uw dot edu.

== Overview ==
Human data interaction will be a 5-credit course that will build data science literacy among undergraduate students across a spectrum of educational backgrounds and professional goals, anchored in the principles and methods of human centered design. Students will gain critical understanding of data-driven algorithmic systems and their implications through readings and written reflections, collaborative in-class activities and group discussions, and hands-on research and software programming activities. 

=== Goals ===
The goals of the course are to foster...

* ''end-user'' and ''conversational'' data science competencies, and 
* a critical understanding of data science as a design practice, a mode of scientific inquiry, and a sociotechnical phenomenon

...among the next generation of UW graduates.

== Target audience ==
The target audience for this course is any student whose major area of study involves empirical research. The student need not be enrolled in a data- or computationally-intensive major (e.g. bioinformatics, epidemiology, computational linguistics). In fact, students in social sciences and humanities disciplines where the use of data science tools and methods are not yet in widespread use (e.g. social work, communications, digital humanities) may especially benefit from the course. 

=== Prerequisites ===
HDI will require a basic familiarity with programming in Python. Students with a wide range of programming goals and levels of familiarity with programming can benefit from the course. The required level and type of programming proficiency necessary for success within the class will be kept deliberately minimal, and the mechanism for satisfying these prerequisites will be flexible. Students who wish to take HDI will be required to have taken at least one (online or classroom-based) programming course or coding workshop—ideally one that teaches the basic syntax and operations of the Python programming language, which has become the de-facto industry standard for data science programming. Potential non-credit-bearing options that would satisfy this prerequisite include Data Carpentry workshops, the Community Data Science Workshop, or Coursera’s Introduction to Python.

== Proposed learning objectives ==
By the end of the course, students will be able to…

* Understand human centered design principles and the role of qualitative, quantitative, and design research methods across data-intensive research domains.
* Identify the ethical implications of data-driven technologies and deploy strategies for anticipating and addressing harmful consequences.
* Frame data-intensive research as a design activity, understand and demonstrate the importance of rationale, reflection, empathy, accountability, and effective communication in data science.
* Design data-driven technologies using a human-centered approach: articulate the assumptions and values embedded in their design choices, their advantages and limitations, tradeoffs involved in their development, and their implications for individuals and society. 
* Critically “read” data-driven technologies and data-intensive research activities across a wide range of real-world applications and articulate how data, software and technological processes shape, and are shaped by, individual decisions and social structures.

== See also ==
* [[DS4UX_(Spring_2016)|HCDE 598: Data science for UX researchers]]. A graduate level special topics class taught in 2016.
* [[Human_Centered_Data_Science|DATA 512: Human Centered Data Science]]. A course offered by the [https://escience.washington.edu/ University of Washington eScience Institute] as part of the core curriculum for the [https://www.datasciencemasters.uw.edu/ Master of Science in Data Science] program.