{"id":13806939,"url":"https://github.com/widged/awesome-julia-datasciences","last_synced_at":"2026-01-21T19:42:53.119Z","repository":{"id":137874248,"uuid":"154650337","full_name":"widged/awesome-julia-datasciences","owner":"widged","description":"Resources about Julia for DataSciences / Machine Learning","archived":false,"fork":false,"pushed_at":"2018-10-25T10:55:01.000Z","size":20,"stargazers_count":14,"open_issues_count":1,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-05-08T22:02:20.692Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/widged.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2018-10-25T10:12:11.000Z","updated_at":"2024-02-07T19:40:55.000Z","dependencies_parsed_at":"2024-01-15T20:46:49.439Z","dependency_job_id":"9125927f-f969-4515-9bee-6949039a22c1","html_url":"https://github.com/widged/awesome-julia-datasciences","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/widged%2Fawesome-julia-datasciences","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/widged%2Fawesome-julia-datasciences/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/widged%2Fawesome-julia-datasciences/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/widged%2Fawesome-julia-datasciences/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/widged","download_url":"https://codeload.github.com/widged/awesome-julia-datasciences/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254043218,"owners_count":22004912,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-04T01:01:18.350Z","updated_at":"2026-01-21T19:42:53.074Z","avatar_url":"https://github.com/widged.png","language":null,"funding_links":[],"categories":["Programming Language Lists","Summary"],"sub_categories":["Julia Lists"],"readme":"# awesome-julia-datasciences\nResources about Julia for DataSciences / Machine Learning\n\nI really find it easier to maintain bookmarks in a datasheet format:\n\nhttps://airtable.com/invite/l?inviteId=invleOL88hv5OVxC0\u0026inviteToken=29040a8d2357b8bc7a83c66a48729e99e7037ea558cd41185dc92d5f0416f2d1\n\nIf you want to help maintain that list, simply ask. \n\nBelow is a copy and paste of the Julia resources found in other awesome lists. My own list includes these and others in an airtable file. I will convert them to an awesome format from time to time.\n\n## Table of Contents\n\n\u003c!-- MarkdownTOC depth=4 --\u003e\n\n- [General-Purpose Machine Learning](#julia-general-purpose)\n- [Natural Language Processing](#julia-nlp)\n- [Data Analysis / Data Visualization](#julia-data-analysis)\n- [Misc Stuff / Presentations](#julia-misc)\n\n\u003c!-- /MarkdownTOC --\u003e\n\n\u003ca name=\"apl\"\u003e\u003c/a\u003e\n## APL\n\n * [Julia](http://julialang.org) – high-level, high-performance dynamic programming language for technical computing\n * [IJulia](https://github.com/JuliaLang/IJulia.jl) – a Julia-language backend combined with the Jupyter interactive environment\n\n\u003ca name=\"julia-general-purpose\"\u003e\u003c/a\u003e\n### General-Purpose Machine Learning\n\n* [MachineLearning](https://github.com/benhamner/MachineLearning.jl) - Julia Machine Learning library.\n* [MLBase](https://github.com/JuliaStats/MLBase.jl) - A set of functions to support the development of machine learning algorithms.\n* [PGM](https://github.com/JuliaStats/PGM.jl) - A Julia framework for probabilistic graphical models.\n* [DA](https://github.com/trthatcher/DiscriminantAnalysis.jl) - Julia package for Regularized Discriminant Analysis.\n* [Regression](https://github.com/lindahua/Regression.jl) - Algorithms for regression analysis (e.g. linear regression and logistic regression).\n* [Local Regression](https://github.com/JuliaStats/Loess.jl) - Local regression, so smooooth!.\n* [Naive Bayes](https://github.com/nutsiepully/NaiveBayes.jl) - Simple Naive Bayes implementation in Julia.\n* [Mixed Models](https://github.com/dmbates/MixedModels.jl) - A Julia package for fitting (statistical) mixed-effects models.\n* [Simple MCMC](https://github.com/fredo-dedup/SimpleMCMC.jl) - basic mcmc sampler implemented in Julia.\n* [Distance](https://github.com/JuliaStats/Distance.jl) - Julia module for Distance evaluation.\n* [Decision Tree](https://github.com/bensadeghi/DecisionTree.jl) - Decision Tree Classifier and Regressor.\n* [Neural](https://github.com/compressed/BackpropNeuralNet.jl) - A neural network in Julia.\n* [MCMC](https://github.com/doobwa/MCMC.jl) - MCMC tools for Julia.\n* [Mamba](https://github.com/brian-j-smith/Mamba.jl) - Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia.\n* [GLM](https://github.com/JuliaStats/GLM.jl) - Generalized linear models in Julia.\n* [Gaussian Processes](https://github.com/STOR-i/GaussianProcesses.jl) - Julia package for Gaussian processes.\n* [Online Learning](https://github.com/lendle/OnlineLearning.jl)\n* [GLMNet](https://github.com/simonster/GLMNet.jl) - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet.\n* [Clustering](https://github.com/JuliaStats/Clustering.jl) - Basic functions for clustering data: k-means, dp-means, etc.\n* [SVM](https://github.com/JuliaStats/SVM.jl) - SVM's for Julia.\n* [Kernel Density](https://github.com/JuliaStats/KernelDensity.jl) - Kernel density estimators for julia.\n* [Dimensionality Reduction](https://github.com/JuliaStats/DimensionalityReduction.jl) - Methods for dimensionality reduction.\n* [NMF](https://github.com/JuliaStats/NMF.jl) - A Julia package for non-negative matrix factorization.\n* [ANN](https://github.com/EricChiang/ANN.jl) - Julia artificial neural networks.\n* [Mocha](https://github.com/pluskid/Mocha.jl) - Deep Learning framework for Julia inspired by Caffe.\n* [XGBoost](https://github.com/dmlc/XGBoost.jl) - eXtreme Gradient Boosting Package in Julia.\n* [ManifoldLearning](https://github.com/wildart/ManifoldLearning.jl) - A Julia package for manifold learning and nonlinear dimensionality reduction.\n* [MXNet](https://github.com/dmlc/mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.\n* [Merlin](https://github.com/hshindo/Merlin.jl) - Flexible Deep Learning Framework in Julia.\n* [ROCAnalysis](https://github.com/davidavdav/ROCAnalysis.jl) - Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers.\n* [GaussianMixtures](https://github.com/davidavdav/GaussianMixtures.jl) - Large scale Gaussian Mixture Models.\n* [ScikitLearn](https://github.com/cstjean/ScikitLearn.jl) - Julia implementation of the scikit-learn API.\n* [Knet](https://github.com/denizyuret/Knet.jl) - Koç University Deep Learning Framework.\n\n\u003ca name=\"julia-nlp\"\u003e\u003c/a\u003e\n#### Natural Language Processing\n\n* [Topic Models](https://github.com/slycoder/TopicModels.jl) - TopicModels for Julia.\n* [Text Analysis](https://github.com/johnmyleswhite/TextAnalysis.jl) - Julia package for text analysis.\n\n\n\u003ca name=\"julia-data-analysis\"\u003e\u003c/a\u003e\n### Data Analysis / Data Visualization\n\n* [Graph Layout](https://github.com/IainNZ/GraphLayout.jl) - Graph layout algorithms in pure Julia.\n* [LightGraphs](https://github.com/JuliaGraphs/LightGraphs.jl) - Graph modeling and analysis.\n* [Data Frames Meta](https://github.com/JuliaStats/DataFramesMeta.jl) - Metaprogramming tools for DataFrames.\n* [Julia Data](https://github.com/nfoti/JuliaData) - library for working with tabular data in Julia.\n* [Data Read](https://github.com/WizardMac/ReadStat.jl) - Read files from Stata, SAS, and SPSS.\n* [Hypothesis Tests](https://github.com/JuliaStats/HypothesisTests.jl) - Hypothesis tests for Julia.\n* [Gadfly](https://github.com/GiovineItalia/Gadfly.jl) - Crafty statistical graphics for Julia.\n* [Stats](https://github.com/JuliaStats/Stats.jl) - Statistical tests for Julia.\n* [RDataSets](https://github.com/johnmyleswhite/RDatasets.jl) - Julia package for loading many of the data sets available in R.\n* [DataFrames](https://github.com/JuliaStats/DataFrames.jl) - library for working with tabular data in Julia.\n* [Distributions](https://github.com/JuliaStats/Distributions.jl) - A Julia package for probability distributions and associated functions.\n* [Data Arrays](https://github.com/JuliaStats/DataArrays.jl) - Data structures that allow missing values.\n* [Time Series](https://github.com/JuliaStats/TimeSeries.jl) - Time series toolkit for Julia.\n* [Sampling](https://github.com/lindahua/Sampling.jl) - Basic sampling algorithms for Julia.\n\n\u003ca name=\"julia-misc\"\u003e\u003c/a\u003e\n### Misc Stuff / Presentations\n\n* [DSP](https://github.com/JuliaDSP/DSP.jl) - Digital Signal Processing (filtering, periodograms, spectrograms, window functions).\n* [JuliaCon Presentations](https://github.com/JuliaCon/presentations) - Presentations for JuliaCon.\n* [SignalProcessing](https://github.com/davidavdav/SignalProcessing.jl) - Signal Processing tools for Julia.\n* [Images](https://github.com/JuliaImages/Images.jl) - An image library for Julia.\n    \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwidged%2Fawesome-julia-datasciences","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwidged%2Fawesome-julia-datasciences","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwidged%2Fawesome-julia-datasciences/lists"}