Data Science

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How TabPFN Leverages In-Context Learning to Achieve Superior Accuracy on Tabular Datasets Compared to Random Forest and CatBoost

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Data Science, Editors Pick, Knowledge Graphs, Machine Learning, Staff, Technology, Tutorials

Tabular data—structured information stored in rows and columns—is at the heart of most real-world machine learning problems, from healthcare records to financial transactions. Over the years, models based on decision trees, such as Random Forest, XGBoost, and CatBoost, have become the default choice for these tasks. Their strength lies in handling mixed data types, capturing […]

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An Implementation Guide to Building a DuckDB-Python Analytics Pipeline with SQL, DataFrames, Parquet, UDFs, and Performance Profiling

ai, AI (Artificial Intelligence), Artificial Intelligence, Big Data, Data Science, Databases, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we build a comprehensive, hands-on understanding of DuckDB-Python by working through its features directly in code on Colab. We start with the fundamentals of connection management and data generation, then move into real analytical workflows, including querying Pandas, Polars, and Arrow objects without manual loading, transforming results across multiple formats, and writing

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Conozca el coste real de su negocio

Advanced Analytics, ai, AI (Artificial Intelligence), Analytics, analytics platform, Artificial Intelligence, data, Data Analytics, Data Management, Data Science, digital transformation, innovation, SAS Viya, 全般

Optimice y modernice todo el proceso de planificación financiera con analítica El problema El ritmo de los cambios normativos, junto con el crecimiento explosivo de los datos financieros y operativos disponibles, ha hecho que las soluciones de contabilidad tradicionales sean incapaces de proporcionar la información crítica sobre los costes, la […] The post Conozca el

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Guide to Propensity Score Matching for Causal Inference to Estimate True Impact

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science

One of the core challenges of data science is drawing meaningful causal conclusions from observational data. In many such cases, the goal is to estimate the true impact of a treatment or behaviour as fairly as possible. This article explores Propensity Score Matching (PSM), a statistical technique used for that very purpose. Unlike randomized experiments

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A Coding Guide to Implement Advanced Differential Equation Solvers, Stochastic Simulations, and Neural Ordinary Differential Equations Using Diffrax and JAX

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we explore how to solve differential equations and build neural differential equation models using the Diffrax library. We begin by setting up a clean computational environment and installing the required scientific computing libraries such as JAX, Diffrax, Equinox, and Optax. We then demonstrate how to solve ordinary differential equations using adaptive solvers

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O potencial transformador da IA no Marketing

Advanced Analytics, ai, AI (Artificial Intelligence), AI series, Analytics, Artificial Intelligence, Customer Analytics, Customer Experience, Data Management, Data Science, data scientist, digital transformation, innovation, Innovations, 全般

Nos últimos anos, a IA deixou de ser uma tecnologia futurista para se tornar um elemento cada vez mais central na estratégia das empresas, inclusive para a área de Marketing ao ajudar na redefinição da forma como as marcas interagem e chegam até aos consumidores. Segundo, aliás, o estudo Marketers […] The post O potencial

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A Coding Guide to Build a Complete Single Cell RNA Sequencing Analysis Pipeline Using Scanpy for Clustering Visualization and Cell Type Annotation

ai, AI (Artificial Intelligence), Artificial Intelligence, Big Data, Data Science, Editors Pick, Technology, Tutorials

In this tutorial, we build a complete pipeline for single-cell RNA sequencing analysis using Scanpy. We start by installing the required libraries and loading the PBMC 3k dataset, then perform quality control, filtering, and normalization to prepare the data for downstream analysis. We then identify highly variable genes, perform PCA for dimensionality reduction, and construct

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Beyond Accuracy: Quantifying the Production Fragility Caused by Excessive, Redundant, and Low-Signal Features in Regression

ai, AI (Artificial Intelligence), Artificial Intelligence, Big Data, Data Science, Editors Pick, Staff, Technology, Tutorials

At first glance, adding more features to a model seems like an obvious way to improve performance. If a model can learn from more information, it should be able to make better predictions. In practice, however, this instinct often introduces hidden structural risks. Every additional feature creates another dependency on upstream data pipelines, external systems,

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How to Build Progress Monitoring Using Advanced tqdm for Async, Parallel, Pandas, Logging, and High-Performance Workflows

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Editors Pick, Machine Learning, Technology, Tutorials

In this tutorial, we explore tqdm in depth and demonstrate how we build powerful, real-time progress tracking into modern Python workflows. We begin with nested progress bars and manual progress control, then move into practical scenarios such as streaming downloads, pandas data processing, parallel execution, structured logging, and asynchronous tasks. Throughout this tutorial, we focus

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A Production-Style NetworKit 11.2.1 Coding Tutorial for Large-Scale Graph Analytics, Communities, Cores, and Sparsification

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we implement a production-grade, large-scale graph analytics pipeline in NetworKit, focusing on speed, memory efficiency, and version-safe APIs in NetworKit 11.2.1. We generate a large-scale free network, extract the largest connected component, and then compute structural backbone signals via k-core decomposition and centrality ranking. We also detect communities with PLM and quantify

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