Daniel Gutierrez
@ddgutierrez.bsky.social
110 followers 59 following 370 posts
Data scientist, Principal AI Industry Analyst and Influencer - http://radicaldatascience.ai, having a blast teaching data science at UCLA #AI #DataScience #MachineLearning #Rstats
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ddgutierrez.bsky.social
The part of data science no one glamorizes:
— Cleaning timestamps
— Untangling legacy SQL
— Explaining why null ≠ 0
Yet this is where real value is created. Modeling is easy compared to wrangling chaos into clarity.

#DataScience #MachineLearning #AI #RStats
ddgutierrez.bsky.social
Writing the 2nd edition of my data science book has reminded me of this: Students don't struggle with advanced topics. They struggle with fundamentals explained too abstractly. Teaching requires empathy, not just expertise.

#DataScience #MachineLearning #AI #RStats
ddgutierrez.bsky.social
MLOps isn’t a luxury. It’s a survival mechanism. If your team can’t track changes, monitor drift, or reproduce results, you're not doing machine learning. You’re doing science fiction.

#MachineLearning #MLOps #AI
ddgutierrez.bsky.social
The data never “speaks for itself.” It reflects your choices:
— What to measure
— What to clean
— What to ignore
Good analysis isn’t neutral. It’s aware of its own filters.

#DataScience #MachineLearning #AI #RStats
ddgutierrez.bsky.social
What’s the first thing you check when your model suddenly drops in performance? (And if your answer is “retrain,” you're already too late.)

#SignalOverNoise #DataScience #MachineLearning #AI #RStats
ddgutierrez.bsky.social
When I see a report filled with p-values and no effect sizes, I worry. When I see model metrics and no baseline comparison, I worry. Statistics is more than math. It’s the discipline of contextual honesty.

#DataScience #MachineLearning #AI #RStats
ddgutierrez.bsky.social
I’ve interviewed hundreds of data scientists. The most hireable trait? They don’t just “build models.” They define problems, challenge assumptions, and think statistically. Tools change. That mindset doesn’t.

#DataScience #MachineLearning #AI #RStats
ddgutierrez.bsky.social
If your model works in the lab but fails in production, you don’t have a model—you have a demo. Success isn't accuracy on test data. It's sustained utility under real-world pressure.

#DataScience #MachineLearning #AI
ddgutierrez.bsky.social
After 25+ years in the field, I’ve seen trends rise and fall.
But the best data scientists still do 3 things well:
— Ask the right questions
— Validate the assumptions
— Communicate with clarity

#DataScience #MachineLearning #AI #RStats
ddgutierrez.bsky.social
As LLMs evolve, most companies won’t build their own models. They’ll build interfaces—tailored, vertical, context-aware. The frontier isn’t raw capability. It’s alignment with workflows.

#AI #GenAI #LLM
ddgutierrez.bsky.social
Confidence intervals are not optional. If you're making claims without uncertainty estimates, you're storytelling—not science. Model outputs need error bars just like measurements do.

#DataScience #MachineLearning #AI #RStats