Projects sharing features with Simple Statistics

Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e

A Math framework for computation in JavaScript

A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

A general purpose recommender metrics library for fair evaluation.

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting label errors and increasing overall dataset quality. It implements a confident learning framework that iteratively refines label noise estimates by comparing model predictions with estimated label probabilities to identify mislabeled examples. The project provides specialized utilities for active learning optimization, allowing for the selection of the most impactful examples for labeling or re-labeling. It also includes an outlier detection tool to identify atypical data poin

Real-time Query for Hadoop; mirror of Apache Impala

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

source code from the book Genetic Algorithms with Python by Clinton Sheppard

:truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark

Java Scientific Computing Library - Signal Processing, FFT, Filters, PSD, DCT, SciPy-like APIs for JVM & Android used in Machine Learning and Data Science.

Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022

Ways of doing Data Science Engineering and Machine Learning in R and Python

splearn: package for signal processing and machine learning with Python. Contains tutorials on understanding and applying signal processing.

jStat - JavaScript Statistical Library

A Julia package for evaluating distances (metrics) between vectors.

Kernel density estimators for Julia

Local regression, so smooooth!

🐍 Quick reference guide to common patterns & functions in PySpark.

This project is a curated collection of technical reference materials and study guides designed for machine learning interview preparation. It provides comprehensive resources for candidates pursuing engineering roles, focusing on deep learning, production infrastructure, and large-scale system design. The repository distinguishes itself through an architecture that combines theoretical research with industrial case studies. It utilizes a pattern-based approach to system design, breaking down complex deployments—such as recommendation engines, search ranking, and ad click prediction—into reus

Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code.

Straight forward plotting built on D3

Interactive 3D plotting with a simple function call using Three.js

Distributed, masterless, high performance, fault tolerant data processing

This repository provides a curated collection of weekly datasets designed for data visualization practice, data science education, and statistical analysis. It serves as a central source for cleaned and structured real-world data, allowing practitioners to focus on analysis and visualization without the need to scrape or clean raw files. The project facilitates a community learning workflow where users can explore a wide variety of topics, ranging from global health spending and energy datasets to maritime logs and baby name popularity. Participants are encouraged to share their resulting vis