What physics does and doesn't teach you about data science

Paraphrasing Leo Breiman: almost nobody can claim their childhood ambition was to be a data scientist. Other fields have “dedicated travelers” - people who can say that ever since childhood they wanted to be a physicist, or a doctor, or an engineer. Not so for data science in 2017, which is a convergence point for people from many backgrounds.

To enter a young, rapidly evolving, ill-defined field requires a sense of adventure and curiosity, and a humbling realization that no field of study is sufficient preparation for the breadth of data science.

In every field, the hardest problems require us to adapt how we think. Richard Feynman remarked that he wasn’t necessarily smarter than classmates, but had developed a broader mental toolbox. Career paths in data science can leverage all of the analytical tools mastered in a physics education, but excellence on the hardest problems requires mastering new tools, borrowed from other fields or invented anew.

From experiences spanning the NSA to Goldman Sachs to Uber, tackling problems in cryptanalysis, web search, medicine, finance, and transportation, I’d like to share some thoughts on finding and tackling challenges in data science, and how to chart an adventure that leverages or complements your preparation.