Ask HN: As a data scientist, what should be in my toolkit in 2018?
Mathematics.  Which branch of math is domain dependent.  Stats come up everywhere.  Graphs do too. In addition to baseline math, you really need to understand the problem domain and goals of the analysis.<p>Languages and libraries are just tools: knowing APIs doesn’t tell you at all how to solve a problem.  They just give you things to throw at a problem.  You need to know a few tools, but to be honest, they’re easy and you can go surprisingly far with few and relatively simple ones.  Knowing how, when, and where to apply them is the hard part: and that often boils down to understanding the mathematics and domain you are working in.<p>And don’t over use viz.  Pictures do effectively communicate, but often people visualize without understanding.  The result is pretty pictures that eventually people realize communicate little effective domain insight.  You’d be surprised that sometimes simple and ugly pictures communicate more insight than beautiful ones do.<p>My arsenal of tools: python, scipy/matplotlib, Mathematica, Matlab, various specialized solvers (eg, CPLEX, Z3).  Mathematical arsenal: stats, probability, calculus, Fourier analysis, graph theory, PDEs, combinatorics.<p>(Context: Been doing data work for decades, before it got its recent “data science” name.)