Tag: Data science

Key data points

The purpose of computing is insight, not numbers.

— Richard Hamming

Essentially, all models are wrong, but some are useful.

— George E. P. Box

Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom.

— Clifford Stoll

Personal approach for collecting Emacs usage statistics advice?

Lately I’ve been curious whether or not my actual Emacs keymapping usage actually reflects how I think I use it. What I mean is that I have a goal of mapping frequently used operations to easily-accessible keybindings on the keyboard. What I plan to do is to record my usage so that I can study it to find mapping decisions that I got right, and wrong, and also identify things that I use that I should be mapping closer to home.
The simplest approach would be to use a keylogger, or advice inside of Emacs.
What I am curious about is your approach if you had done, or would do, something like this, and your thoughts an ideas.
In my case I lay out my mappings for how far away from home they are, and that has worked well so far, but I would like some numbers to back up that claim though it is not too serious depending upon how you look at it.
Cross posted from help-gnu-emacs

Ten Simple Rules for Reproducible Computational Research

This link via irreal is another “must read” if you’ve never done systems work before (coming from a system person myself, not a data person).

Tidy Data

A huge amount of effort is spent cleaning data to get it ready for data analysis,
but there has been little research on how to make data cleaning as easy and effective
as possible. This paper tackles a small, but important, subset of data cleaning: data
“tidying”.

— Wickham
Tidy Data is a must-read paper.