ISSUE 449 · August 22, 2023
This analysis of the monthly "Who is Hiring" discussion on Hacker News explores 10 years of posts to understand the trends in the data job market, with a particular focus on data science. It's based on Hacker News posts, which aren't necessarily an accurate reflection of the job market but it's a great analysis, with useful insights along the way.
The single biggest argument about statistics: is probability frequentist or Bayesian? It's neither, and this post explains why. Buckle up — this is a deep-dive explanation.
The Palindrome | Tivadar Danka
Great post summarizing the top challenges facing large language models. If you're looking for problems to solve or are interested in understanding LLM weaknesses, this is a good place to start.
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LIDA is a new library for generating data visualizations and infographics using large language models. It's language agnostic and can work with any visualization library too. There's a lot here, including a notebook tutorial, a paper, and a code repo.
Microsoft Open Source
QGIS is a popular open-source geospatial data system that lets you create, edit, visualize, analyze and publish geospatial information from any platform. Now, with the new qgisprocess package, you can work with QGIS directly from R.
RSpatial | Dewey Dunnington, Floris Vanderhaeghe, et al.
There's a dark side to every superpower but you need to understand those dark sides to move past them. If you've ever felt stuck in your career, the insights here could help you get unstuck. Great post.
The Skip | Nikhyl Singhal
Great collection of posts and papers that dive into LLMs and how to work with them. This is an evolving resource by Vicki Boykis who's particularly interested in practical first-hand accounts.
This free online course by Andrew Heiss just started and it looks great. The course covers a wide variety of topics and includes video lectures, examples, assignments, and pointers to books, articles, and key resources that are all free.
Georgia State University | Andrew Heiss
The Nature of Code is a beginner-friendly creative coding tutorial that explores a range of programming strategies for developing computer simulations of natural systems—from elementary concepts in math and physics to sophisticated machine learning algorithms. This is the newly updated, 2nd edition of this popular book and it's free to read online.