Principles of data science I learned during my master’s degree:
- Smaller models are almost always better
- You can’t outrun bad data with more data

I find it interesting that companies with a bunch of PhD degrees are just ignoring those two most basic rules of data science

Data science x Clojure

https://clojurecivitas.github.io/conferences/scinoj_light_1/notes/hallway_track.html

The phenomenon of data scientists using models (LLM or otherwise) on data that they don't understand -- and don't care to learn about -- is not just limited to tech bros: my own former colleagues were like that. On top of that, our technical manager, a PhD in Computer Science and expert in machine learning, perpetuated the idea that understanding the domain-specific data was NOT the job of his data scientists #datascience
https://www.propublica.org/article/inside-ai-tool-doge-veterans-affairs-contracts-sahil-lavingia

When people first started talking about interpretability in ML models, I was like, "yeah that's a good idea, so at least you acknowledge the model is not based on fundamental principles," but most of the interpretability work have still missed the mark for me. #datascience
https://arxiv.org/pdf/2505.13510

Posting this again because it's a programming/design issue I come up against repeatedly and I know others have as well.

"Human Name Variations in Databases"

https://www.becomingadatascientist.com/2015/09/19/human-name-variations-in-databases/ #DataScience #UX

Dr. Ellie Murray ScD @epiellie has been breaking down the MAHA report, piece by piece.

If you enjoy looking at bad data, or are interested in learning what is in the report and would rather read a review, take a look. I have been thoroughly enjoying it, as a data nerd.

Parts I & II are currently available:
https://epiellie.substack.com/p/the-maha-report-i-read-it-so-you

You know when I said this? I mean things like this: https://fly.io/blog/youre-all-nuts/

#datascience

https://toot.wales/@Sbectol/114612422982229305

I asked a Harvard postdoc which skills are essential to thrive as a researcher in AI & Biomedicine:

His answer:
Cultivate abstract thinking.
Build solid foundations instead of chasing hypes.
Think independently, embrace a do-it mindset, stay curious and persistent.

Knowledge is accessible. Thinking is up to us.

What skills are you trying to develop?

I definitely got out of bed the wrong way this morning, but what the heck is going on with #datascience techbros talking enthusiastically about "vibe coding" whilst laughing at those who know how to do it properly.

Did they learn anything at university? It's the kind of bullying I last saw from teenagers in school who made fun of other students who "worked too hard"

Garbage in, garbage out – even Agentic AI can’t save you from yourself.

Artificial intelligence is only as brilliant as the data it’s spoon-fed – and spoiler alert: your data is often trash.
Whether it’s traditional machine learning, generative models, or your shiny new agentic systems, the pattern remains insultingly consistent:
• Bad data? Expect bad decisions.
• Incomplete data? Enjoy half-baked ideas.
• Outdated data? Say hello to irrelevant nonsense.

I often talk about what AI can or tragically still can’t do.
But here’s the real twist: the problem isn’t the system. It’s you. Or more specifically, the glorious mess you call your “data foundation.”

You don’t have a lack of innovation.
You have a lack of clean data structures, maintained knowledge bases, and basic contextual awareness.
And then you expect the AI to magically fill gaps that should never have existed in the first place.

Hmmmm

#artificialintelligence #ai #machinelearning #aiart #digitalart #technology #art #aiartcommunity #midjourney #datascience #generativeart #innovation #tech #deeplearning #python #midjourneyart #aiartwork #aiartist #programming #robotics #bigdata #artoftheday #coding #aiartists #SkyNet is Us

https://bsky.app/profile/sterlingworldrecs.bsky.social/post/3lpyv5niu322s

A #sql cheatsheet

May be handy in #datascience #dataengineering or #dataanalytics contexts

Cascadia R Workshops – June 20 in Portland!

Join us the day before the conference for a full day of hands-on workshops taught by experienced R developers and educators.

Topics include:
• GitHub for R Users
• Rust for R Developers
• Intermediate Shiny
• Intro to Positron
• Intro to GIS & Mapping in R

Location: Portland State University
Workshops tickets going fast!— space is limited!

Full details + tickets: https://cascadiarconf.com
#rstats #Portland #DataScience #OpenSource

New Bioconductor Blog Post!

We’ve just launched the first in a new series by Vince Carey, founding contributor to Bioconductor and core developer, exploring the evolving complexity and value of the Bioconductor ecosystem.

Check it out https://blog.bioconductor.org/posts/2025-05-26-ask

In case you missed it: NEW scholarship opportunities to attend GBCC2025 are now open! We are thrilled to offer this opportunity to our community and hope to see you there!

Apply now: https://forms.gle/WKoLb5NUR6FVmeRM9

R FOR DATA SCIENCE

Looking to take your #Rskills to the next level as a #UniversityofCopenhagen #researcher, employee, or #PhD student? We've got the #course for you!

R for Data Science
16-18 June 2025
Panum, University of Copenhagen

Register here as an employee: https://lnkd.in/dyQx6K63
Register here for ECTS credits as a PhD student: https://lnkd.in/da47HJ3f

#excel #rstudio #r #datascience #data #phd #research #datascience #UCPH