Extracting full value out of data requires people – a community of people with the knowledge and skills to develop and utilize methods and tools. The methods and tools of today are based on the investments made in the past – investments in time and money; future discoveries and developments rely upon a continued investment in human capital. The Office of the Associate Director for Data Science is committed to support and highlight the NIH’s investment in the Data Science workforce, through extramural and intramural training opportunities.
New training information coming soon!
The NIH Data Science Workforce Development Center will be both an organizational structure for finding online educational resources in biomedical data science and the subset of those educational resources that are held at NIH or funded by BD2K. The NIH Data Science Workforce Development Center includes the development of open course materials and online courses, the hosting of in-person courses, and the discovery of educational resources.
The Big Data to Knowledge Program has issued a collection of funding opportunity announcements to address a variety of needs, all related to training a biomedical workforce able to take full advantage of Big Data. The need for data science knowledge and skills varies by scientific domain and project, particularly where the project falls along the continuum of developing or consuming new data science methods and tools. BD2K has many different funding opportunities designed to cover the diverse Data Science training needs of the biomedical science community.
Data Science training opportunities occur all across the Institutes and Centers at the NIH. The Office of the Associate Director for Data Science is committed to highlighting and supporting ongoing activities in this area. Some examples of existing programs include:
National Institutes of Health Library Training Program
The NIH Library provides training programs in more than just literature search! Past programs have included classes on clinical and experimental data access, data visualization tools, programming languages like R, and workshops by Software Carpentry.
National Cancer Institute Statistical Code Review Group
Reviewing someone else’s code is one of the best ways to learn how to do something new and code review is a great way to make sure you have taken a good approach. This collaborative at the National Cancer Institute is open to investigators writing statistical code from all NIH Institutes and Centers.
NIH MOOC Study Hall
Are you taking a Data Science MOOC but wish you could have face-to-face discussion too? Meet up with other online!