phunge

Altair is amazing. I've been using matplotlib going on 12 years now -- Altair didn't feel more productive at first (more typing), but the longer you work with it, the more you realize how the API is a cohesive system. This leads to a more iterative workflow and eventually to better visualizations.

A hazard rate can be thought of as a patient's one day probability of mortality. A hazard ratio is a ratio of two hazard rates, so 1.25 would mean that people in one group were dying at a 1.25x rate relative to a comparison group.

For Bayesian stats, "Statistical Rethinking" by McElreath is a masterpiece.

Have a look at duckdb, it's is another interesting tool that's columnar and embedded.

It would be amazing if the world of open-source column stores matured a little bit relative to where we are today...

+1 to this, I've had multiple teams that have shied away from airflow because of its operability & deployment story, despite needing something that has all its features.

In terms of radically different takes on workflow engines, I'm very interested in reflow. I haven't used it enough to know if the rough edges are a deal breaker.

They're also used in spectral clustering, which is a powerful method for clustering data (see Ng 2001 for some pictures)

I think it's pretty easy for this discussion to become about personalities and not methodologies. Gelman is pretty brash and speaks his mind, so he could be correct on methodology and have that get lost in the noise.

So I'll give my opinion as a datascientist: null hypothesis significance testing is broken, the whole thing needs to get chucked. It's not fixable, and it's not only p-hacking that's the problem. Go read Frank Harrell, I like his writings on the topic more than Gelman's.

Classic post! This post is like a gentle gateway to the world of Bayesian statistics -- check out Cameron Davidson Pilon's free book if you want to go deeper.

Highly recommended! The first course was the first thing I came across that helped me contextualize the DL field into something that might be relevant for my work. It's a great way to get your hands dirty.

One point of comparison is Cam Davidson Pilon's Bayesian Methods for Hackers, they have a similar vibe: practical applied advice from a field that tilts towards the academic...

Logistic regression can easily be made online too, keep in mind! sklearn has an implementation of online gradient descent, and vowpal wabbit is also excellent at those problems.

Naive bayes can be parallelized in ways that SGD can't, that's a whole other conversation.

If you're interested in getting parse trees from regexes (ie all the submatch locations), check out Kleenex, it looks really interesting and is based on some new research. Full parse trees are a harder problem than matching, and one that is not a primary focus in RE2 performance AFAICS.

I'm super excited for this! Good dashboarding for python has been a huge gap in the ecosystem, and none of the existing options (bowtie/pyxley/spyre) really came close to the functionality of shiny.

The closest IME has been ipywidgets+jupyter and the jupyter dashboards server. Jupyter is really nice for developing, but deploying is another question. The dashboards server works but development on it has been stalled, and deploying it is a pain. Also, if dash can leverage the react ecosystem, that could make it pretty compelling compared to ipywidgets.