Data science is a very technical and complicated type of work. We often focus on very specific problems, which is good. We add most of our value by combining our focused attention and problem-solving skills. But I think it’s good practice to take a step back from time to time and try to take a broader view.
Studying philosophy is a tool that I have found quite effective in helping me think deeply about data science. As a casual student of philosophy, I have observed that some fields of philosophical thought are closely intertwined with data science. Specifically, I found that metaphysics, causality, and epistemology have many theories that are very applicable.
This is the first installment of a multi-part series discussing various philosophical viewpoints and their implications on data and data science. I’ll start with the fascinating metaphysical theory of determinism.
Determinism is a philosophical theory about the nature of our universe. There are multiple different and nuanced versions of determinism¹, but the general idea is that there is no randomness in our universe. Every event has a set of causes that explain it entirely, and these causes in turn have a set of causes. The chain of causes is unbroken since the beginning of the universe (or perhaps there is no beginning of the universe²?).
Below is a quote from Laplace that summarizes a deterministic view of the physical world:
“We can consider the current state of the universe as the effect of its past and the cause of its future. An intellect that at a given moment knew all the forces that set nature in motion, and all the positions of all the elements that compose it, if this intellect were also vast enough to subject these data to analysis, would encompass in a single formulates the movements of the largest bodies in the universe and those of the smallest atom; For such an intellect nothing would be uncertain and the future, like the past, would be present before its eyes.”
Pierre-Simon Laplace, Philosophical Essay on Probabilities (1814)
