Data Science Training and Collaboration
Data science is helping to make sense of the world across many different research disciplines and in different sectors of the economy. At the same time, because it involves a rapidly evolving set of skills and tools, it is challenging for instructors to equip students with the knowledge that they need to become effective data scientists. Furthermore, use of data science can carry significant societal and ethical implications, which need to be discussed in the contexts in which data science is taught. This interactive workshop will introduce the participants to modern tools and methods that are used to teach data science, as well as to considerations in planning and incorporating data science ideas into an existing curriculum. Participants will learn how to discuss the ethical and societal implications of data science with students. Finally, the workshop will present an opportunity to network and learn from other instructors who are thinking about the integration of data science into their teaching.
Learn about the whole summer series here:
Enrollment is free and is limited to 20 participants per workshop. It is hoped that the small workshop size will facilitate networking and promote collaboration across institutions by individuals who share common interests in research and education.
Participation priority is for current HSI faculty and staff who teach undergraduate STEM courses. Non-HSI faculty staff who teach undergraduate STEM courses are eligible to apply if they: 1) currently collaborate as PIs/co-PIs on a funded or pending NSF EHR/DUE grant that includes HSI faculty/staff as PI/co-PIs or 2) would like to network to find HSI partners for future collaborative projects in education or research.
Admission priority is for faculty within the first 10 years of their first academic tenure-track appointment. Applicants should be aware that the selection process strives for diverse geographical and institutional representation. The selection decision is final and summary reviews are not provided.
Submit your application at this link: https://www.surveymonkey.com/r/hsihub-2021STEMwkshops
- Participants will learn new ways that data science can be integrated into their curriculum.
- Participants will use techniques and tools for teaching data science.
- Participants will gain experience in teaching using approaches such as live coding, use of computational notebooks, version control, etc.
- Participants will learn about applications of data science in community engagement (e.g., Data Science for Social Good).
- Participants will incorporate ethics considerations into use of data science tools and methods.
- Participants will collaboratively develop concepts for further joint work.
Dr. Ariel Rokem leads a research program in neuroinformatics, the development of data science tools, techniques and methods and their application to the analysis of neural data. He directs the annual Summer Institute for Neuroimaging and Data Science (NeuroHackademy) and contributes to multiple open-source software projects in the scientific Python ecosystem.
Dr. Stone handles eScience operations and planning, develops research and training programs, participates in strategic planning, and serves as the primary contact for university and industry partners, funding agencies and the public.
Dr. Tan primarily helps researchers migrate their work to the cloud and facilitates strategies for open data access, effective data visualization and collaborative cloud-based tools. Amanda received her PhD in Civil and Environmental Engineering from the University of Washington with a focus on understanding water resources management in developing countries. She is currently building decision support frameworks through cloud-based delivery models.
Dr. Benson has worked primarily in the domain of human neuroscience and vision with an emphasis on understanding the relationship between the anatomical structure of the brain and its function. Noah has spent much of his research career writing and supporting software tools that enable other researchers to duplicate and extend his work.
Dr. Tanweer work incorporates a range of qualitative methods for studying the practice and culture of data-intensive computational work. She is passionate about sociotechnical thinking, collaborating with data science teams, and leveraging action research to foster reflexive, ethical data science practices. In particular, she engages with efforts to harness data for societal benefit and has both studied and helped develop the eScience Institute’s annual Data Science for Social Good program.
Dr. Dailey is working with research scientist Anissa Tanweer to understand how human centered design practices can be incorporated into data intensive research. She is the Human Centered Design Mentor for eScience’s Data Science for Social Good Program. She coaches DSSG teams to explore the social dimensions of their projects as a team, interact with stakeholders, and integrate that awareness into project work. She is also working with a coalition of Data for Good organizers to document better practices for running Data Science for Social Good Programs.
Dr. Fatland has formerly worked at NASA and Microsoft Research and has a PhD in Geophysics. His interests are in data science across the full spectrum from acquisition to collaboration with emphases on remote sensing, sensor networks, visualization, data systems and (domain-wise) the earth’s hydrosphere. He also devotes time and energy to K12 STEM education.
Dr. Holdgraf was previously a post-doctoral researcher in the Department of Statistics at UC Berkeley, and a Community Architect with the Division of Data Science at Berkeley. He is also a team member of Project Jupyter (particularly the JupyterHub and Binder teams), with a focus on how infrastructure can support interactive computing workflows in research and education. He is interested in the boundary between technology, open-source software, and research and education workflows, as well as how open communities can support and extend these workflows in a way that makes science more impactful and inclusive. His background is in cognitive and computational neuroscience, where he used predictive models to understand the auditory system in the human brain.
Workshop Sponsors and External Links