Towards Data Science

@TDataScience

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towardsdatascience.com

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Towards Data Science
@TDataScience
1h
"I can’t promise you 'study A, B, and C, and you’ll be able to get a good job and build a career that will carry you to retirement' because that’s just not the world we live in." Stephanie Kirmer offers pragmatic advice for learners interested in starting a career in data
Starting a Career in Data Science in the Age of AI
From towardsdatascience.com
Towards Data Science
@TDataScience
3h
So you've built an entire workflow around a foundation model. Then the model changes, or gets deprecated, and nothing works as it used to. What's next? Pratik Rupareliya explains how to ensure consistency and continuity in an ever-evolving landscape.
We Pinned Our Model Version to Stay Safe. The Provider Deprecated It Anyway.
From towardsdatascience.com
3
Towards Data Science
@TDataScience
6h
"If our app is broken, asking agents to debug often leads to false promises and remaining broken code." @ReyaVir explains how we can catch these silent failures in an effective and timely manner.
Coding Agents Keep Shipping Silent Failures — Here Is How to Catch Them
From towardsdatascience.com
1
Towards Data Science
@TDataScience
8h
Between plain RAG, graph RAG, and a frontier model's context window, which approach packs the most punch? Arijit Ghoshal sets out to find out with a comprehensive series of tests and benchmarks.
When Does Graph RAG Actually Add Value? A Hands-On Experiment
From towardsdatascience.com
1
Towards Data Science
@TDataScience
14h
If our intuition is so often based on Bayesian principles, why do we, as data practitioners, so often struggle to break away from frequentist models? Spyros Georgopoulos shares a thoughtful attempt to unpack this conundrum.
Why You Think Like a Bayesian but Were Taught Like a Frequentist
From towardsdatascience.com