Tag
Tweet promoting two machine learning books: Managing Machine Learning Projects and Machine Learning Design Patterns, with a detailed description of the former for project management techniques.
A software engineer reflects on how scientists often lack software engineering skills, using an example of optimizing an astrophysics simulation postprocessing tool and advocating for a 'missing semester' for scientists.
freeCodeCamp published a comprehensive handbook on clustering in Python, covering K-means, hierarchical, and DBSCAN clustering with implementations and visualizations.
Kirk Borne shares a book about machine learning for financial risk management with Python, highlighting how ML models can improve financial modeling and adapt to changing data patterns.
Alexander Rakhlin has been named director of the MIT Statistics and Data Science Center, succeeding Ankur Moitra, and will lead interdisciplinary statistics and AI research efforts.
A comprehensive guide for software developers entering the data field, explaining the data tools landscape, key concepts, and workflows from ingestion to visualization.
This paper introduces UrbanDS, a graph-guided LLM multi-agent system designed for data-intensive urban tasks, along with UrbanDS-Bench, a benchmark for evaluating such systems. Experiments show it outperforms existing data science agents and has been deployed in a real-world urban operations platform.
The article compares ChatGPT and Claude for data science workflows in 2026, noting that both models now rely on native Python execution for calculations. ChatGPT excels in raw data processing and statistical modeling, while Claude dominates interactive visualization and large-context integration.
Kirk Borne shares a recommendation for a textbook on linear algebra for data science, machine learning, and signal processing, with links to Amazon and praise from academics.
Perplexity AI announces the Bumblebee Pipeline, a tool for big data and AI workflows, shared by prominent AI influencer gp_pulipaka.
Roman Vershynin's textbook 'High-Dimensional Probability' second edition is available as a free PDF download from the author's website, targeting doctoral students and researchers in data science.
Promotion of the book 'Mathematical Methods in Data Science' which bridges theory and applications using Python, available on Amazon.
Advice tweet: Build only two key AI/DS projects—a real ML prediction app and a RAG+agent app—to land a high-paying career.
A comprehensive guide to mastering Kaggle data science competitions, covering techniques in machine learning, GenAI, and LLMs, updated with new chapters on time series and generative AI.
Promotion for the book 'Outlier Detection in Python' from Manning Publications, covering various outlier detection techniques with an emphasis on explainability.
Promotion of the book 'Graph Algorithms for Data Science' which teaches graph algorithms and their applications using Neo4j, covering topics like knowledge graphs, social network analysis, and node embeddings.
A statistician proposes a workflow where AI is used for brainstorming and presentation formatting, while human experts handle the core quantitative analysis to maintain accuracy.
A free PDF of the Linear Algebra textbook by David Cherney, Tom Denton, and Andrew Walton from UC Davis is shared, emphasizing its importance for AI and data science.
A cheat sheet for choosing the right statistical test, shared by KirkDBorne.
Promotional tweet about learning NumPy for Python, linking to the official guide book and the numpy.org/learn site.