Guest post by the Data Science @ NLM Training Program team.
As part of our effort to advance Goal 3 of the NLM Strategic Plan (“Build a workforce for data driven research and health”), NLM launched the Data Science @ NLM (DS@NLM) Training Program in 2019 to help ensure that all staff are prepared to engage with and participate in NLM’s developing data science efforts.
Our efforts have stayed on track despite the changes caused by the COVID-19 pandemic, and we’re proud to highlight DS@NLM events held during the past year. We’re also sharing lessons learned throughout the training program, which are applicable to any individual or organization trying to help develop data science skills in the fields of health and biomedical information.
Earlier this month, we marked two years of the DS@NLM Training Program with a Spring Fling series of virtual events celebrating the data science training achievements of NLM staff.
Our Spring Fling kicked off with “lightning talk” presentations featuring several graduates of our intensive Data Science Fundamentals course, who shared their final class projects with NLM colleagues. Participants in our year-long Data Science Mentorship program also had the opportunity to present their Capstone projects. Our program mentees, who were mentored by NLM staff members, developed their data science skills by completing projects that applied data science techniques to help improve NLM operations.
What We’ve Learned:
Be responsive to specific needs; one size does NOT fit all.
Data plays a role in virtually everything we do at NLM, and as we aim to provide data training opportunities for staff working in many different areas, we recognize that different staff members have unique training needs. New training opportunities for some staff, such as our researchers, may hinge on their knowledge of machine learning. Metadata specialists may have more need for data cleaning or text processing skills, while administrators may benefit more from learning about data visualization.
People also learn in different ways, be it through shorter webinars and workshops, longer intensive courses, or self-directed learning. The DS@NLM program provides a variety of activities to meet these needs, including opportunities for various skill levels and topics, from short webinars to on-demand classes to ten-week intensive training courses.
Be responsive to staff feedback; give people what they ask for.
To help us determine what to offer, we engaged directly with our audience, asking NLM staff what they needed and listening to their responses. Because of the wide variety of work done at NLM, receiving feedback from staff helped us better understand their specific training needs. While we cannot always offer individualized programs to meet every need, staff feedback always helps us discover new ideas for future programming.
Teaching skills is just the beginning; applying new skills is essential.
A key lesson learned from staff feedback is that teaching new data skills is important, but that’s not enough on its own; teaching how to put newly acquired data skills to use in the real world or applying it to their work is just as important. Helping staff learn to apply data science techniques to their work transforms this new knowledge from theoretical to practical. The Data Science Mentorship Program, with its concluding Capstone project, is a great example of an opportunity for staff to both develop skills and practice applying them.
We applaud and celebrate all the hardworking staff from across NLM who have taken advantage of these training opportunities to advance the goal of building a workforce for data driven research and health, both at NLM and throughout the biomedical and health sciences information world.
Share with us and others how you are helping your staff apply data science skills in your organization—do you have any lessons learned?