This is a follow up post to Describing what we do (link). The discussion in Describing centred on a colleague’s observation “the biggest challenge is how we develop and mentor these people” involved in data analysis.
I have been profoundly impressed by the #RLadies community of practice on Twitter (link). I see this as a model community of shared practice. RLadies has more than 50,000 members, with 165 groups in 47 countries. Dan Kopf has written about How R-Ladies made data science inclusive (link).
I regard the inclusive nature of #RLadies the source of its stickyness (link) as a digitally-enabled space. I think it can help us with the mentoring issues my friend mentioned and through a lens of a critical friend (link).
In their seminal paper, Arthur Costa and Bena Kallick (1993) (link) point out that “if you never change the lens, you limit your vision”. They observe:
A critical friend is a trusted person who asks provocative questions, provides data to be examined through another lens, and offers a critique of a person’s work as a friend.
A critical friend takes the time to fully understand the context of the work presented and the outcomes that the person or group is working toward.
Importantly, the friend is an advocate for the success of that work (my emphasis).
This friendship is built through trust. In my case, we have used unmeetings for this that involved lots of pizza and good coffee, sometimes even Irgachefe and Monsoon Malabar varieties. Hopefully these unmeetings meet Michael Garet and his colleagues’ (2001) (link) “high standards, content focus, and in-depth active learning opportunities” over an extended period of time.
There is evidence that investment in these unmeeting opportunities is very important. Michael and his colleagues, for example, conclude “in order to provide useful and effective professional development that has a meaningful effect on teacher learning and fosters improvements in classroom practice, funds should be focused on providing high-quality professional learning experiences. (My emphasis) (link).
I do think the support of a different lens on analyst and analytics roles is much needed. Long-term support for learning adds to the work underway in curriculum reform of performance analysis as it negotiates the growing influence of data science pedagogies and practices.
RLadies London (Twitter)