QuestDB is an open-source time-series database engineered for low latency. It uses a column-oriented, time-partitioned storage engine with memory-mapped files and vectorized execution for high-throughput ingestion and millisecond-level analytical queries. SQL is extended with time-series operators such as SAMPLE BY, LATEST ON, ASOF JOIN, WINDOW JOIN and HORIZON JOIN.
MEKA is an open source implementation of methods for multi-label learning and evaluation, based on the WEKA Machine Learning Toolkit. It includes dozens of multi-label methods from the scientific literature and a wrapper to the MULAN framework. The project is distributed via SourceForge, with code hosted on GitHub.
Privatiser anonymizes IPs, API keys, passwords, PII and cloud identifiers before text is pasted into AI tools. It runs in the browser with local processing, preserves structure, and offers a reversible mapping to restore values in replies. It is free and open source.
This resource offers a downloadable, printable poster outlining 15 common data fallacies as part of data literacy lessons, with a monthly random giveaway for free mailed copies of the poster.
tweetopic is a Python library for topic modelling of Tweets and other short texts. Its documentation site provides an API reference and a usage guide covering installation, the Dirichlet Multinomial Mixture Model, the Biterm Topic Model, pipelines, visualization and model persistence.
Stanford CS336: Language Modeling from Scratch is the official course website for a Stanford class offered in Spring 2026. The course walks students through the process of developing a language model, including data collection and cleaning for pre-training, transformer model construction, model training, and evaluation before deployment. The site provides logistics, schedule, assignments, and course materials.
An open source, MIT-licensed Python library imported as imblearn that relies on scikit-learn. It provides tools for classification with imbalanced classes. The documentation includes installation guides, a user guide, an API reference and a gallery of examples.
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MEKA is an open source implementation of methods for multi-label learning and evaluation, based on the WEKA Machine Learning Toolkit. It includes dozens of multi-label methods from the scientific literature and a wrapper to the MULAN framework. The project is distributed via SourceForge, with code hosted on GitHub.
Privatiser anonymizes IPs, API keys, passwords, PII and cloud identifiers before text is pasted into AI tools. It runs in the browser with local processing, preserves structure, and offers a reversible mapping to restore values in replies. It is free and open source.
This resource offers a downloadable, printable poster outlining 15 common data fallacies as part of data literacy lessons, with a monthly random giveaway for free mailed copies of the poster.
tweetopic is a Python library for topic modelling of Tweets and other short texts. Its documentation site provides an API reference and a usage guide covering installation, the Dirichlet Multinomial Mixture Model, the Biterm Topic Model, pipelines, visualization and model persistence.
Stanford CS336: Language Modeling from Scratch is the official course website for a Stanford class offered in Spring 2026. The course walks students through the process of developing a language model, including data collection and cleaning for pre-training, transformer model construction, model training, and evaluation before deployment. The site provides logistics, schedule, assignments, and course materials.
An open source, MIT-licensed Python library imported as imblearn that relies on scikit-learn. It provides tools for classification with imbalanced classes. The documentation includes installation guides, a user guide, an API reference and a gallery of examples.
