This area consists of actual activities that brings people together to combine expertise from each of the four areas.
It is characterized by data science teams working together and with external parties to develop solutions and projects that are responsible, authentic, effective, and efficient. Many activities that are considered an essential part of data science, such as data wrangling, actually exist only in practice, combining expertise in systems, design (representation), and analytics, and they are not usually taught in distinct classes. Practice is also where the core areas of data science come into contact with domain knowledge and real world problems. Although practice is depicted in the center of the diagram, it could also be viewed as the wider environment within which the field of data science operates. Or, practice may be envisioned as the water surrounding and connecting the four areas as islands:
- Key tensions: goals vs resources; integration vs separation; planning vs play.
- Common theme: collaborative activity, praxis.
- Realm: umami (all four at once).
- Keywords: project management, stewardship, process, team sport, convergence, integration.
- Values: effectiveness, translation, impact, flow.
Subareas and Courses
- Project Management of Data Science
- Capstone Projects
- Ph.D. Research Projects
- Collaboratories
- Domain-specific courses