Data Science: week 2

Lesson two in data science class #2 challenged students to ask the right questions. I majored in philosophy in undergrad, and I am proud to say that I am majoring in Philosophy in ‘life,’ insofar as philosophy is a love of knowledge and a perpetual open-mindedness that asks ‘why?’Philosophy has proved the most useful, valuable, and rigorous crucible because it has challenged me to ask the right questions.

Of course, the buoyant and heady feeling born of studying the fundamental questions that humans have grappled with since the beginning of time is not an unwelcome effect.

However, philosophy’s ‘dirty laundry,’ so to speak, is that it is not well defined: philosophers shift between making sweeping generalizations (general theories of “everything”) and making infinitesimal distinctions.  But, the reason why I do it and love it is because I need to understand my world and the many ways of looking at it.

And, of course, “the unexamined life is not worth living.” We must ask ourselves: Is what we’re doing worth doing? What would you do if you were just trying to make yourself happy? It a question that merits our attention.

A conscientious scientist, investigator, and engaged citizen needs critical thinking and analytic skills. A big shout out to epistemology, philosophy of science, and skepticism generally for “tracking down…prejudices in the hiding places where priests, the schools, the government, and all long-established institutions had gathered and protected them.” Evidence is the foundation on which we make decisions in our organizations, how we justify our arguments, and how we make sense of the world. Data-driven decision-making fuels solutions for business and organizational problems. Problems best answered with evidence rather than on a hunch.

Bayesian formulas Because of philosophy, I am relentless. Even if the only words I understand in a technical manual are ‘the’ ‘and’ or ‘maybe’, I will still beat my head against the wall until understanding, finally, laboriously, leaks in. Because the only way you are going to a) contribute something valuable b) be able to read 85 pages and write 19 single spaced in 2 weeks is if you are intrinsically motivated. If what you do is because of internal reward, not grades or ca$h money.

So problem solving skills are not irreconcilable with a degree in Philosophy, by any means. In fact, the late tycoon Max Palevsky once reported to The Atlantic:

Many of us early workers in computers were philosophy majors. You can imagine our surprise at being able to make rather comfortable livings. (The Atlantic, http://www.theatlantic.com/technology/archive/2011/10/is-philosophy-the-most-practical-major/246763/)

Be employable. Study philosophy…

…and study Data Science! Identifying data problems is the first step a data scientist takes and the process requires a firm grounding in fundamental principles of philosophy. Asking: what is the purpose (of data architecture, collection, analysis, and archiving)? This question drives every stage of data gathering, information retrieval, data analysis, and data preservation.

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One thought on “Data Science: week 2

  1. As Jeff Stanton states in Ch. 2 of his free open source ebook “Intro to Data Science”: having a nose for identifying data problems requires openness, curiosity, creativity, and a willingness to ask a lot of questions.

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