Welcome to the training and skills page of the Data Science and Leadership program, designed to equip public health professionals with essential skills in data-driven leadership and health equity. Our curated training resources empower public health professionals to lead with data-informed decisions, driving impactful change in communities.
Training Opportunities
Data for Health Leadership Lab
NNPHI in collaboration with Proximate Learning, LLC, has designed a training curated for public health professionals looking to expand their role, influence and leadership impact. The pilot training program blends asynchronous e-learning and applied labs, live learning sessions, mentorship, and an asynchronous community of practice.
Our program is closed and all participants have been selected. Please continue to visit this page for future opportunities.
Additional Data Science and Leadership Trainings
Storytelling with Data
Leading others—to motivate them and commit their energies and expertise to achieve the shared mission and goals of the emergency management system—is a necessary and vital part of every emergency manager, planner and responder’s job. The goal of this course is to improve your leadership and influence skills.
Leadership and Influence
Create compelling ways to communicate insights about public health data in the Storytelling with Data webinar series hosted by the Washington State Department of Health and IBM. The three one-hour sessions include lectures plus hands-on and practical activities.
Leading Change in Informatics and Data Analysis
Change is a constant in both our personal and professional lives. The idea that human beings naturally resist change is deeply ingrained into our culture and how we think about change. This course provides public health professionals with a foundation in change management and practical tools for utilizing formal change management for an informatics or data analytics project.
In the summer of 2023, as part of the Public Health LEADS program, NNPHI embarked on an exploratory evaluation to inquire into the constructs of data science and leadership within the public health workforce. A series of four listening sessions with pre- and post- participation surveys captured participants’ accounts in narratives about their individual work and training experiences. Using both qualitative and quantitative methods, the evaluation sought to understand the key concepts of public health data science and leadership, identify gaps and needs for data science and leadership in the current workforce capacity, accessibility, training and education, detect ways that systems undermine efforts to create a move diverse public health workforce, and align workforce needs and current public health programs with data science and leadership curricula.
The listening sessions suggest that data science efforts typically focus on and demand high levels of expertise in the “performance objectives” domain, data literacy skills under “transferable skills” domain and data-informed leadership skills to the “values” domain within the Essentials Framework. The tools used and the techniques employed will vary based on the given task itself.
Evaluation findings
Exploratory report on public health data science and leadership
Recommendations
Individual-level
- Coping with rejection
- Communicating data
Organizational level
- Recruitment
- Competency checklists
- Career pathways
- Skill-building in low-risk environments
- Centralized public health data teams
Systems level
- Federated architecture and accomplishing shared objectives
- Addressing the impacts of politics on data science
- Collaboration and cross-sector partnerships
- Improving training infrastructure
- Financial support
Participants shared innovative and best practice ideas to address the barriers and challenges for the governmental public health workforce needs in public health data science and public health leadership. Inspired by what was heard in the listening sessions, lead author Health Communications Consultants, Inc. also incorporated their recommendations within this section. To make these ideas actionable, they have been summarized and situated loosely across the levels of the socio-ecological model, guiding where interventions can occur.
Teaching them not only how to do the data analysis, but how to sort through what's data and what's noise.
As for on-the-job training, there is limitation of didactic training when information is verbally explained but never shown, important in training to explain and then apply and show, that is how information is learned.
In terms of training, I believe there is a disconnect in what higher education curriculum is offering in terms of data science training to incoming students to prepare them for practice. I find many students and working professionals utilizing services such as google analytics because the educational institutions did not prepare them for the work force in this particular area.