More Advice For Data Scientists

Charles Parker provides more Dijkstra-style wisdom for budding data scientists:

Raise your standards as high as you can live with, avoid wasting your time on routine problems, and always try to work as closely as possible at the boundary of your abilities. Do this because it is the only way of discovering how that boundary should be moved forward.

Readers of this blog post are just as likely as anyone to fall victim to the classic maxim, “When all you have is a hammer, everything is a nail.” I remember a job interview where my interrogator appeared disinterested in talking further after I wasn’t able to solve a certain optimization using Lagrange multipliers. The mindset isn’t uncommon: “I have my toolbox.  It’s worked in the past, so everything else must be irrelevant.”

There’s some good advice in here.

Related Posts

Neural Networks From Scratch

Ilia Karmanov explains neural nets and shows how to build one in R: Hence, my motivation for this post is two-fold: Understanding (by writing from scratch) the leaky abstractions behind neural-networks dramatically shifted my focus to elements whose importance I initially overlooked. If my model is not learning I have a better idea of what […]

Read More

Using bsts In R

Steven L. Scott explains what the bsts package does: Time series data appear in a surprising number of applications, ranging from business, to the physical and social sciences, to health, medicine, and engineering. Forecasting (e.g. next month’s sales) is common in problems involving time series data, but explanatory models (e.g. finding drivers of sales) are […]

Read More