#machine learning

arXiv AI submissions grew 18.9x in a decade. We decomposed the growth: it's ~28x more researchers, each publishing just 9% more. And only 0.51% of 84,420 papers have a near-duplicate.

Read the people, not the postings: national-lab data scientists build ML like Big Tech, while civil-service agency data scientists do statistics-and-reporting. Two different jobs under one title — and neither ever touches frontier tech.

Python appears in 29% of tech postings (2x SQL) and uniquely dominates both edges of our knowledge graph — most-required by jobs AND third-most-mentioned in docs. Plus the salary twist: Python is the floor, not the premium.

We analyzed 3,277 job postings for Data Scientists, ML Engineers, and AI Engineers across four seniority levels. The biggest compensation jump isn't between mid and senior — it's Senior to Staff, where ML Engineers earn a median $59K more. And most 'hot' AI skills (Generative AI, prompt engineering) disappear as premium drivers once you control for level. Here's the data.

We analyzed 77,735 job postings and found that the Bay Area tech market is statistically distinct from everywhere else. Rest-of-US and non-US skill profiles are indistinguishable for Data Scientists and AI Engineers. Here's what the chi-square tests reveal.

Discover the most popular Python packages for data science, including essentials like Numpy and Pandas along with hidden gems like Seaborn and spaCy.