Data science community: Difference between revisions
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[[File:Mako-Emails GMail Over Time-201405.png|thumb|400px|Results of [https://mako.cc/copyrighteous/google-has-most-of-my-email-because-it-has-all-of-yours some personal data analysis] on how much of non-Google email ends on Google Servers conducted by [[Benjamin Mako Hill]], the faculty supervisor of the project.]] | |||
In today's increasingly data-driven world, the ability to ask and answer questions with data is incredibly important. Effectively working with data requires both technical skills like programming and statistical knowledge as well as non-technical skills like the ability to ask good questions and form arguments. However, formal training programs and resources often emphasize technical skills but only rarely support the development of non-technical capacities. How can we design to help people learn these skills via informal, participatory, and creative online interactions? | In today's increasingly data-driven world, the ability to ask and answer questions with data is incredibly important. Effectively working with data requires both technical skills like programming and statistical knowledge as well as non-technical skills like the ability to ask good questions and form arguments. However, formal training programs and resources often emphasize technical skills but only rarely support the development of non-technical capacities. How can we design to help people learn these skills via informal, participatory, and creative online interactions? | ||
In this research project, we | In this research project, we are studying the practices and challenges in online data science hobbyist communities and explore design solutions to support question-asking, exploration, and communication with data. Specifically, we will: (1) conduct research interviews with members of online data science hobbyist communities, (2) analyze interview data and contribute our findings to human computer interaction and learning science literatures, and (3) brainstorm design solutions. | ||
=== Call for participation === | |||
We are looking for people interested in participating in this study by spending some time talking with! Participants must be adults (i.e., at least 18 years old if they are from the United States and/or above the age of majority in their locale if they are not) and can conduct an interview in English. Please see the link for the Google Form Questionnaire below. We would love to talk to you and learn more about your experience participating in online data science communities. You will receive a $20 gift card in return for your participation and you will contribute to academic research one or more publications as a result. | |||
To participate, fill out our [https://forms.gle/j5G5wrEEi9y5b4G86 Google Form Questionnaire]! | |||
= | === People === | ||
[ | [[File:Regina hime.JPG|thumb|200px|Project leader Regina Cheng.]] | ||
The research team is led by PhD student [https://reginachangzhou.github.io/ Regina Cheng] at the [https://www.uw.edu University of Washington] and the research is being supervised by [[User:Benjamin Mako Hill|Prof. Benjamin Mako Hill]]. If you've got any questions about the study, you should reach out to Regina and/or Mako. Interviews are also being conducted by student researchers Elliana Beberness, Helen Xu, Andy Shaw, Josephine Hoy, Joyce Chang, Katlyn Greene, and Caroline Rygg at UW. Dr. Sayamindu Dasgupta, assistant professor at University of North Carolina at Chapel Hill is collaborating with the University of Washington team. | |||
The research team | |||
Latest revision as of 20:06, 23 October 2021
In today's increasingly data-driven world, the ability to ask and answer questions with data is incredibly important. Effectively working with data requires both technical skills like programming and statistical knowledge as well as non-technical skills like the ability to ask good questions and form arguments. However, formal training programs and resources often emphasize technical skills but only rarely support the development of non-technical capacities. How can we design to help people learn these skills via informal, participatory, and creative online interactions?
In this research project, we are studying the practices and challenges in online data science hobbyist communities and explore design solutions to support question-asking, exploration, and communication with data. Specifically, we will: (1) conduct research interviews with members of online data science hobbyist communities, (2) analyze interview data and contribute our findings to human computer interaction and learning science literatures, and (3) brainstorm design solutions.
Call for participation[edit]
We are looking for people interested in participating in this study by spending some time talking with! Participants must be adults (i.e., at least 18 years old if they are from the United States and/or above the age of majority in their locale if they are not) and can conduct an interview in English. Please see the link for the Google Form Questionnaire below. We would love to talk to you and learn more about your experience participating in online data science communities. You will receive a $20 gift card in return for your participation and you will contribute to academic research one or more publications as a result.
To participate, fill out our Google Form Questionnaire!
People[edit]
The research team is led by PhD student Regina Cheng at the University of Washington and the research is being supervised by Prof. Benjamin Mako Hill. If you've got any questions about the study, you should reach out to Regina and/or Mako. Interviews are also being conducted by student researchers Elliana Beberness, Helen Xu, Andy Shaw, Josephine Hoy, Joyce Chang, Katlyn Greene, and Caroline Rygg at UW. Dr. Sayamindu Dasgupta, assistant professor at University of North Carolina at Chapel Hill is collaborating with the University of Washington team.
