I recently completed a Data Science course via General Assembly (GA). In that course, I learned all of the requisite skills to get started down a path in Data Science, be it for individual curiosity, career pursuits, or both. We were taught the techniques and the tools for everything from data gathering, to cleansing, model selection, feature tuning, and much more. I am grateful for that course and the baseline knowledge that GA gave me in Data Science. I wanted to pursue this course because too many models and algorithms are being created as black boxes with no transparency and lack the needed inclusion of minority voices in their design and feedback. I touch on the need for wide array of voices participating in Data Science in a panel I participated in at the Women Who Code CONNECT Conference.
Just applying technology to a problem is not a Silver Bullet towards a solution. Complexity is bound to arise. Essential Complexity is a requirement that, without it, the complete picture cannot be seen. Accidental Complexity happens when we introduce concepts that do not serve the overall design. Therefore, it is imperative to justify the complexity, by demonstrating where it fits into the big picture and how it helps accomplish the larger vision.