Being a Data Scientist: My Experience and Toolset
These types of posts validate my concern about the people entering my field right now.<p>Data science, as a line of work, is distinct from other technical roles in its focus on <i>creating business value using machine learning and statistics</i>. This quality is easily observed in the most successful data scientists I've worked with (whether at unicorn startups, big companies like my current employer, or "mission-driven" companies).<p>Implicit in this definition is <i>avoiding the destruction of business value by misapplying ML/statistics</i>. In that sense, I am concerned about blog posts like these (which list 50 libraries and zero textbooks or papers) and those who comment arguing the relevance of "real math" in the era of computers.<p>Speaking bluntly: if you are a "data scientist" that can't derive a posterior distribution or explain the architecture of a neural network in rigorous detail, you're only going to solve easy problems amenable to black-box approaches. This is code for "toss things into pandas and throw sklearn at it". I would look for a separate line of work.