Open-source project that applies AI to data analysis, extraction, visualization, or business intelligence.
A paid monthly recap newsletter for AI engineers covering December 2023 developments. It frames AI industry activity around four areas: Data Wars, the GPU Rich/Poor War, the Multimodality War, and the RAG/Ops War, and also recaps top items for AI engineers.
dc.js is a JavaScript charting library with native crossfilter support for exploring large multi-dimensional datasets. It uses d3 to render charts in CSS-friendly SVG format. Charts are data driven and reactive, providing instant feedback to user interaction. The library is described as usable in the browser and on mobile devices.
Redash is an open source tool for connecting to data sources, querying, visualizing and sharing data. It supports SQL, NoSQL, Big Data and API sources including PostgreSQL, MySQL, Redshift, BigQuery and MongoDB. Users build dashboards, set alerts and manage access.
A book by Roger D. Peng and Elizabeth Matsui, dated 2017, about data analysis as an art. It discusses the thought process behind data analysis and covers R software development for building data science tools, including handling complex data, building R packages and custom data visualizations.
A book by Brian Caffo, Roger D. Peng and Jeffrey Leek on assembling and leading a data science enterprise, based on the Johns Hopkins Executive Data Science Specialization. It covers a crash course in data science, recruiting and evaluating a team, and the structure of the data science pipeline. Available as PDF and EPUB; printed copies are sold through Lulu.
Think Stats is an introduction to probability and statistics for Python programmers, emphasizing simple techniques for exploring real data sets and answering questions. It includes case studies using datasets from the National Institutes of Health and other sources, with exercises using short programs. The second edition is no longer current and is available under a Creative Commons license.
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A paid monthly recap newsletter for AI engineers covering December 2023 developments. It frames AI industry activity around four areas: Data Wars, the GPU Rich/Poor War, the Multimodality War, and the RAG/Ops War, and also recaps top items for AI engineers.
dc.js is a JavaScript charting library with native crossfilter support for exploring large multi-dimensional datasets. It uses d3 to render charts in CSS-friendly SVG format. Charts are data driven and reactive, providing instant feedback to user interaction. The library is described as usable in the browser and on mobile devices.
Redash is an open source tool for connecting to data sources, querying, visualizing and sharing data. It supports SQL, NoSQL, Big Data and API sources including PostgreSQL, MySQL, Redshift, BigQuery and MongoDB. Users build dashboards, set alerts and manage access.
A book by Roger D. Peng and Elizabeth Matsui, dated 2017, about data analysis as an art. It discusses the thought process behind data analysis and covers R software development for building data science tools, including handling complex data, building R packages and custom data visualizations.
A book by Brian Caffo, Roger D. Peng and Jeffrey Leek on assembling and leading a data science enterprise, based on the Johns Hopkins Executive Data Science Specialization. It covers a crash course in data science, recruiting and evaluating a team, and the structure of the data science pipeline. Available as PDF and EPUB; printed copies are sold through Lulu.
Think Stats is an introduction to probability and statistics for Python programmers, emphasizing simple techniques for exploring real data sets and answering questions. It includes case studies using datasets from the National Institutes of Health and other sources, with exercises using short programs. The second edition is no longer current and is available under a Creative Commons license.
