Data Science: Celebrating My Academic Bias
In a recent post, I introduced my Data Science Portfolio. After describing the high-level organization of the Portfolio, I noted:
At the end, and for now, there is a section on my academic background – a background that has shaped so much of those intersections between science and technology that have been captured in the preceding sections of the portfolio.
Even in this earliest of drafts, I knew that I was somewhat uncomfortable with a section dedicated to academics in my Portfolio. After all shouldn’t a portfolio place more emphasis on how my knowledge and skills, academic or otherwise, have been applied to produce some tangible artifact?
Upon further reflection, I currently believe what’s material in the context of a portfolio is some indication of the bias inherent in the resulting curated showcase of one’s work. Of course to some degree the works presented, and the curation process itself, will make self-evident such personal bias.
Whereas it may make sense for an artist not to overtly disclose any bias with respect to their craft, or a curated collection their work, I currently perceive absolutely no downside in sharing my personal bias – a bias that in my own case, I believe reflects only in positive ways on the Portfolio as well as the individual items included in it.
To this end, and in the spirit of such a positive self-disclosure, my personal bias reflects my formative years in science – a background to which I well recall significant contributions from high school, that were subsequently broadened and deepened as an undergraduate and then graduate student. Even more specifically in terms of personal bias was my emphasis on the physical sciences; a bias that remains active today.
As I’ve started to share, through such posts as the one on the mathematical credentials I bring to Data Science, my choice to pursue the physical sciences was an excellent one – even through the self-critical lens of personal hindsight. An excellent choice, but albeit a biased one.
The very nature of Data Science is such that each of us carries with us our own, wonderfully unique personal bias. As we necessarily collaborate in team, project and organizational settings, I believe it’s important to not only ensure each of us preserves their personal bias, but that we leverage this perspective as fully and appropriately as possible. As a consequence it is much more likely that everyone we work with, and everything we work on, will derive maximal value.