Data

Data Science as Engineering: Foundations, Education, and Skilled Identity

is having an identity crisis. Indications of this crisis have been around for years. As an example, the inaugural issue of found it easier to define what data science is just not reasonably than...

Causal ML for the Aspiring Data Scientist

: Limitations of Machine Learning As an information scientist in today’s digital age, it's essential to be equipped to reply quite a lot of questions that go far beyond easy pattern recognition. Typical machine learning...

Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code

road in Lao PDR. The varsity is 200 meters away. Traffic roars, smoke from burning garbage drifts across the trail, and youngsters walk straight through it. What are they respiratory today? Without local...

Optimizing Data Transfer in Distributed AI/ML Training Workloads

a part of a series of posts on optimizing data transfer using NVIDIA Nsight™ Systems (nsys) profiler. Part one focused on CPU-to-GPU data copies, and part two on GPU-to-CPU copies. On this post, we turn our attention...

Constructing a Self-Healing Data Pipeline That Fixes Its Own Python Errors

AM on a Tuesday (well, technically Wednesday, I suppose), when my phone buzzed with that familiar, dreaded PagerDuty notification. I didn’t even must open my laptop to know that the daily_ingest.py script had failed....

If You Wish to Grow to be a Data Scientist in 2026, Do This

are you sitting on right away? 10? 50? Possibly you’ve crossed 100 and also you’re beginning to wonder when you’ll ever break in. Well, I’ve been there myself. I sent over 400 applications before securing my first...

Google Trends is Misleading You: How one can Do Machine Learning with Google Trends Data

. What a present to society that is. If not for google trends, how would we've ever known that more Disney movies released within the 2000s led to fewer divorces within the UK. Or that drinking...

The era of agentic chaos and the way data will save us

Models: The underlying AI systems that interpret prompts, generate responses, and make predictions Tools: The mixing layer that connects AI to enterprise systems, equivalent to APIs, protocols, and connectors ...

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