Linear Digressions

Feature engineering is ubiquitous but gets surprisingly difficult surprisingly fast. What could be so complicated about just keeping track of what data you have, and how you made it? A lot, as it turn...

6 Loka 201921min

If you’re a data scientist or data engineer thinking about how to store data for analytics uses, one of the early choices you’ll have to make (or live with, if someone else made it) is how to lay out ...

30 Syys 201915min

Data scientists and software engineers both work with databases, but they use them for different purposes. So if you’re a data scientist thinking about the best way to store and access data for your a...

23 Syys 201916min

There are a few things that seem to be very popular in discussions of machine learning algorithms these days. First is the role that algorithms play now, or might play in the future, when it comes to ...

16 Syys 201918min

When a big, established company is thinking about their data science strategy, chances are good that whatever they come up with, it’ll be somewhat at odds with the company’s current structure and proc...

9 Syys 201915min

This is a re-release of an episode that originally aired on July 29, 2018. The stars aligned for me (Katie) this past weekend: I raced my first half-marathon in a long time and got to read a great ar...

1 Syys 201930min

When data science is hard, sometimes it’s because the algorithms aren’t converging or the data is messy, and sometimes it’s because of organizational or business issues: the data scientists aren’t pos...

25 Elo 201923min

We talk often about which features in a dataset are most important, but recently a new paper has started making the rounds that turns the idea of importance on its head: Data Shapley is an algorithm f...

19 Elo 201916min