Why the standard groupBy function isn’t enough

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What each one solves, and runnable code you can paste into a notebook.

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Quick and simple tips to help you write better agent instructions

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How to apply the latest context engineering guidelines to your day-to-day data science work

The post Context Engineering Is Changing. Here’s What It Means for Data Scientists appeared first on Towards Data Science.

As AI handles more of the execution, what work should belong to agents vs humans and why does that distinction matter?

The post Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch appeared first on Towards Data Science.

Bagging hits a wall no amount of trees can break — here's the equation that explains why, and the experiment that proves it

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When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels,...

When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels, and capturing NVIDIA CUDA graphs. For large models, initialization can take several minutes, during which surviving workers must absorb the displaced traffic. Shadow engine recovery, available as a preview feature in NVIDIA Dynamo…

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We're excited to share some big news: We completely rebuilt the TDS website and our contributor portal. Whether you come here to read, to write, or both, here's what you can expect from our new site.

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For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain, and...

For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain, and maintain bindings back to Python, which most people never did; or move up the stack and let someone else’s library do it, namely PyTorch, CuPy, or RAPIDS. The second option is why the Python GPU ecosystem thrives. But it has limits.

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What moving a Dockerized pipeline off my laptop taught me about containers, networking, and hidden assumptions.

The post I Deployed My Data Pipeline to AWS. Then Everything That Was “Local” Broke. appeared first on Towards Data Science.

A practical guide to navigate hierarchies, find routes, detect cycles and calculate degrees of separation

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From Kaplan-Meier curves to hazard ratios with runnable Python Code throughout

The post Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide appeared first on Towards Data Science.

Dimensions are one of the two main object types in dimensional modelling. But what are the different types of dimensions? And how can you use them?

The post The Types of Dimensions in a Star Schema, and How to Use Them appeared first on Towards Data Science.

From Kaplan-Meier curves to hazard ratios with runnable Python Code throughout

The post Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide appeared first on Towards Data Science.

Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor...

Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor loadings, and structural-break signals at single-GPU and multi-node scale Quant strategies routinely group instruments for portfolio construction, risk aggregation, statistical arbitrage, and trade surveillance. Incorrect groupings can make…

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Dimensions are one of the two main object types in dimensional modelling. But what are the different types of dimensions? And how can you use them?

The post The Types of Dimensions in a Star Schema, and How to Use Them appeared first on Towards Data Science.

A production account of scaling an enterprise integration pipeline from 500 to 8,000 events per second, and the two correctness guarantees the throughput work was never allowed to trade away.

The post How to Scale an Integration Pipeline Without Breaking Correctness appeared first on Towards Data Science.

Modern vision-language models (VLMs) can support tasks such as visual question answering, captioning, and image-text reasoning. In practice, however, the data...

Modern vision-language models (VLMs) can support tasks such as visual question answering, captioning, and image-text reasoning. In practice, however, the data needed to adapt these models may be distributed across institutions or organizations that cannot centralize their raw records. Federated learning provides a way to coordinate training across these data-local sites. For VLMs…

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Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications...

Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications range across exploratory data analysis, topic modeling, and single-cell analysis. Many of these workflows are iterative and exploratory, requiring UMAP to be run repeatedly as users analyze their data or tune parameters. As datasets grow…

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For years, web agents have worked one click at a time—and often fallen apart on long tasks. Microsoft Research’s Webwright makes a different bet: give the model a terminal and let it write the program instead. On long-horizon tasks, the same GPT-5.4 model jumps from 33.5% to 60.1% success. And instead of leaving behind a click trace, it leaves something you can actually use again: a command-line tool.

The post Webwright: Why AI Web Agents Should Write Code, Not Click appeared first on Towards Data Science.