laGrenouille

Yes, this (as the even shorter c.colname) and the fact that you can do var= in place of assign in with_columns/agg changed my whole outlook on polars. Have been using it as my main driver for the past year.

Great visualizations. Really enjoyed having a well-written example where mathematical proofs directly help with understanding a practical application.

I wonder what would happen with this analysis if a momentum term was added to the gradient descent. It seems that it would fix the specific failure modes in the examples, but I wonder if there's a corresponding mathematical way of categorizing what kinds of functions can(not) be quickly optimized with GD + momentum.

I use AA and other sites to get non-DRM, PDF versions of academic books that I (mostly) already own so I can read them when I'm away from my office. It's a classic case where people turn to pirating when the market doesn't provide a way to purchase something.

Same thing with movies. Ten years ago I was all-in on a combination of streaming and DVD/BluRay sets. The market has completely collapsed for me with region locking and overly aggressive DRM. So, I've started pirating those again as well when it's not possible to get through another route.

I have read the quoted GitHub docs page before and also found it somewhat odd. Not because it shouldn't be allowed to post public code without a LICENSE (or with a restrictive one), but because GitHub has a "Fork" button on every repository. It's strange to me that GitHub has a one-click button that can violate the default terms of code uploaded to the site.

> His fame may outlive Foch and Ludendorff, Wilson and Clemenceau.

Funny to think how this has aged since 1915. Over a century later Einstein is an almost universally known figure. The others on this list are, particularly outside of France, names that I would not expect the median person to be able to say something interesting about.

Hotels with similar amenities are usually priced at absurdly high rates for corporate clients.

The place you linked to has the equivalent of a studio apartment with no laundry machine going for over 9000 CAD for a month. AirBnB has plenty of one bedrooms going for a third of that.

I completely agree that it's an open question about what exactly constitutes data science and what should (or at least could) be covered in a standard introduction. For me, a fairly reasonable—though certainly not definitive—set of topics are five items listed on this course's syllabus. And that's what makes this so frustrating, personally. The instructors actually have a good proposal of what should be taught, but then just turn around and teach a classical course in statistical learning.

You're thinking too narrowly about what "schema design" could mean. No, data scientists do not typically design back-end, production database systems. But defining and organizing a multi-sheet spreadsheet for manual data collection is what many data scientists spend much of their time doing (i.e., in the biomedical space). Doing that well definitely requires some understanding of concepts such as functional dependency, normal forms, and data types.

These notes might be a great source for what they cover, but as a whole I find this to be a good example of what is currently wrong with data science education. While the syllabus has bullet points that include "1. data collection", "2. data management", and "5. communication", the content and schedule have a 90%+ overlap with a standard machine learning course. They even use a statistical learning textbook (a good one, but still).

Statistics departments keep trying to latch on to the excitement (and money) around data science by changing the superfluous things like department names and course titles without actually adjusting what they teach. I would love to see a version of this that actually engages at a non-superficial level with topics such as database design, theory(ies) of data visualization, methods for storytelling with data, and interactive design.

LexisNexis, which does other things now but started in the legal space, offers a huge collection of legal opinions with a fairly good search and linking capabilities. Most clerks and law professionals would have access to it.

I think the benefit of Wikipedia is not access to materials so much as it is the succinct summarization of the legal opinions. Perhaps now NLP could help with this, but it's a very complicated problem to provide a summary of the important bits from a 100+ page legal document.

But arguably the EU is even more motivated by the security issues of an emboldened Russia. Combined with the much stronger environmental push from groups in Europe, it seems reasonable that they would push as much or more for energy-based sanctions against Russia.

Yes, that's what I learned as well. And that's actually close to what Harvard reported using last year ($2.1 billion from the endowment; 40% of the budget).

I was only using the more aggressive number as a thought experiment to the original poster about what it would take the cover the entire operating budget.