About the Webinar
This webinar will focus on the on-going need for information professionals to be well-versed in data science skills in order to successfully support the work of the students, scholars, and other professionals you serve. While you may be quite familiar with the research data lifecycle, what additional tools or support do you need in order to extract, wrangle, analyze, and present data for, from, and about your organization? What management challenges does your organization face in ensuring that staff are sufficiently prepared and supported? What skills should be looked for when hiring? How and why should data science education be implemented? What policies or practices have organizations found useful? These questions and more will be answered by our panel of experts from across the information community.
Confirmed speakers include
- Stephanie Labou, Data Science Librarian, University of California, San Diego.
- Courtney Butler, Data Science Librarian, Federal Reserve Bank -Kansas City
- Julie Goldman, Countway Research Data Services Librarian, Harvard
- Ron Daniel, Director, Elsevier Labs, Elsevier
Data Science and the Library at UC San Diego
As data science becomes a hot topic in academia across disciplines, university libraries may find themselves in a position to extend their data services support to aspects of data science. This talk will focus on two facets of this broader issue: (1) what skills are needed in order to fill a support role for patrons, and how might this vary based on support needs and (2) what might it look like in practice, and what skills are needed, when the library begins to use data science methodologies for its own internal processes.
Building Data Science Skills: Enhancing Core Capabilities and Understanding Unique Value
This presentation will focus on ways to develop data science skills by building upon core capabilities such as reference and data management and by understanding the unique value that librarians bring to these types of partnerships. The emphasis will be less on technical skills and more on how to attain the conceptual knowledge necessary to support the data-driven work of our patrons. It will include several illustrative examples within topics such as data licensing and reproducibility and will discuss various resources and strategies for strengthening services.
Collaboratively Build Data Science Services and Skills
This short talk will build on the concepts that were previously discussed during Part One: Labor and Capacity for Research Data Management. New opportunities for engagement in data-intensive research provoke qualitative changes to service and staffing models. In order for the academic library to provide high quality, campus-level support in data science with in-depth consultation services that require considerable time and expertise, the library must identify partners and foster skilled personnel. This presentation will provide tools and training examples, management challenges and opportunities for collaboration in supporting data science through the library and across institutions.
Building Data Science and Data Engineering Skills
Data Science has been the subject of many articles where it is pitched as the silver bullet dream career of the future. But what about people who have to actually build and run a data science team and deliver results? Where is their silver bullet? Unfortunately, no such silver bullet exists. Too many factors affect how data science skills are needed for such a simple solution. This talk reviews a number of those factors and concludes that there is no substitute for good management and concentrating on the needs of the customer.
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