Ill mention a few I tend to go back for sheer joy. I'm a data scientist, so the ones I really like tend to be statistics focused since (I don't do DL):
- Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes [1]
Brought to me by [this](https://simplystatistics.org/posts/2017-09-04-deep-dive-ogat...) blogpost a while ago, for me it shows what a really good statistical analysis based on a model developed for the task at hand looks like. Great stuff.
- Conducting highly principled data science: A statistician’s job and joy [2]
Meng is such a good writter. This one really puts into perspective the job of good principles when tackling any kind of task that a data scientist will face. Principles, theoretical principles, are what avoids messing up a task.
1. https://amstat.tandfonline.com/doi/abs/10.1080/01621459.1988...
2. https://www.sciencedirect.com/science/article/pii/S016771521...
