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Promotional tweet about learning NumPy for Python, linking to the official guide book and the numpy.org/learn site.
DSWorld introduces a Data Science World Model that predicts environment state transitions to reduce costly trial-and-error in autonomous agents, achieving 14x acceleration in RL training and 3-6x in inference while maintaining competitive performance.
A comprehensive guide for software developers unfamiliar with data tools, covering the data lifecycle, data professions, and the tool landscape, with insights from the author's experience at Deepnote and Metabase.
This book covers the mathematical foundations of data science, including high-dimensional analysis, SVD, PCA, regression, graphs, clustering, deep learning, and more.
Explains the difference between traditional RAG and Agentic RAG, highlighting that Agentic RAG adds a decision layer for iterative retrieval and multi-source reasoning, and mentions an upcoming bootcamp.
A GitHub repository providing a comprehensive, modular roadmap with free resources for learning AI, ML, deep learning, and related fields from scratch, including math foundations, data science, and generative AI.
Promotes an 'AI Expert Roadmap' repository that provides structured learning paths for AI, ML, deep learning, data engineering, big data, and data science.
A curated list of 880 awesome open-source machine learning projects in Python covering various domains like NLP, data visualization, and time series.
Introduces CausalDS, a benchmark for evaluating causal reasoning in LLM-based data science agents, using synthetic structural causal models and natural language stories to test associational, interventional, and counterfactual reasoning along with tool use and abstention.
This paper investigates whether LLM-generated reusable skill files improve performance of AI data scientists across data science workflows. Through extensive ablation experiments involving over 9,000 runs, the authors find no significant improvement from generated skills over baseline prompting, cautioning against their default use.
An interactive exploration of Benford's Law across real datasets, explaining the mathematical phenomenon where the digit 1 appears as the first digit about 30% of the time, and its applications in fraud detection.
This article explains hazard ratios in health studies, why they cannot be directly converted to life expectancy changes without considering risk distribution over time, and clarifies the difference between hazard ratios and relative risks.
A tweet discusses how many data scientists misuse Claude Code and promotes a CLAUDE.md file inspired by Andrej Karpathy's observations to improve AI coding behavior.
Recommend a free open-source ebook "Applied Machine Learning in Python", which combines mathematical derivation and Python implementation, covers 30+ algorithms, and provides interactive visualizations, suitable for systematic learning of machine learning principles and practice.
A developer published his ML interview prep guide on GitHub for free, based on real questions he received from Google, LinkedIn, Snap, Coupang, and StitchFix, covering study plans, coding, stats, system design, and more; the repo now has 12.4k stars.
Introduces a new coding agent designed to assist with data engineering, analytics, and data science tasks.
Introduces AgenticDataBench, a comprehensive benchmark for evaluating LLM-based data agents across diverse domains with fine-grained skill-based metrics, including real-world B2B use cases and synthetic tasks.
A curated list of 10 free, open-source tools developed by universities and research institutions that rival expensive commercial software, covering reference management, databases, big data, scientific computing, machine learning, document preparation, online learning platforms, and statistical analysis.
This article demonstrates how to model the COVID-19 outbreak using the SEIRS model implemented in the J programming language, explaining the states and variables involved.