Statistics
Showing 36 of 54 projects
A powerful Python library for data manipulation and analysis, providing fast, flexible data structures.
The fundamental package for scientific computing with Python, providing powerful N-dimensional arrays and mathematical functions.
An introduction to Bayesian inference and probabilistic programming using Python and PyMC, with a computational-first approach.
A command-line benchmarking tool that performs statistical analysis across multiple runs to accurately measure and compare shell command execution times.
A free, self-taught curriculum following undergraduate Data Science guidelines using MOOCs from top universities.
A pure Go library for reading and writing Microsoft Excel™ spreadsheets (XLAM/XLSM/XLSX/XLTM/XLTX).
A topic-wise curated list of machine learning and deep learning tutorials, articles, and resources for developers and data scientists.
A curated collection of tutorials, articles, and resources for learning machine learning and deep learning topics.
A fast and accurate command-line tool that counts lines of code, comments, and blanks across over 150 programming languages.
Generate comprehensive data quality profiling and exploratory data analysis reports for Pandas and Spark DataFrames with a single line of code.
Generate comprehensive data quality profiles and exploratory data analysis reports for Pandas and Spark DataFrames with a single line of code.
A comprehensive study plan and resource collection for preparing for machine learning engineering interviews at top tech companies.
A powerful .NET library for transforming methods into benchmarks, tracking performance, and sharing reproducible measurement experiments.
A comprehensive set of numeric libraries for Go, providing matrices, statistics, optimization, and graph algorithms.
A very fast and accurate code counter with complexity calculations, COCOMO/LOCOMO estimates, and unique line metrics written in pure Go.
A command-line tool that provides simple and efficient access to various statistics in git repositories.
A collection of over 230 pure-Python utilities that extend the standard library with missing functionality.
A practical guide for researchers on how to properly structure and share data with statisticians to ensure efficient analysis.
A command-line tool that generates GitHub-like contribution calendars from local git commit history.
A standard library for JavaScript and TypeScript with an emphasis on numerical and scientific computation.
A curated collection of Python tutorials and resources for data science, machine learning, and natural language processing.
A robust JavaScript benchmarking library that provides statistically significant results, used by jsPerf.com.
A statistics-driven microbenchmarking library for Rust that provides rigorous performance analysis.
An open-source book teaching data science using R, covering data import, transformation, visualization, and modeling.
A Python library for lightning-fast univariate time series forecasting with optimized statistical and econometric models.
A curated collection of Python libraries, tutorials, and tools for data science, from data wrangling to machine learning and visualization.
A comprehensive .NET framework for machine learning, computer vision, statistics, and scientific computing.
A library for probabilistic reasoning and statistical analysis integrated with TensorFlow and JAX.
A self-hosted dashboard that tracks your Spotify listening history and provides detailed statistics.
Course materials for the Johns Hopkins Data Science Specialization on Coursera.
Course materials for the Johns Hopkins Data Science Specialization on Coursera.
A Java dataframe and visualization library for data loading, cleaning, transformation, and analysis.
An open-source numerical library for .NET and Mono providing algorithms for scientific computing, linear algebra, statistics, and more.
A lightweight, dependency-free JavaScript library for descriptive, regression, and inference statistics.
A Laravel package for retrieving Google Analytics data, including pageviews, visitors, and top pages, with a fluent API.
A high-performance, easy-to-use, and scalable machine learning package for linear models, factorization machines, and field-aware factorization machines.
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