The Data Science Lifecycle Process The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo Data Science Lifecycle Template Repo Template repository for data science lifecycle project RexMex A general purpose recommender metrics library for fair evaluation. ChemicalX A PyTorch based deep learning library for drug pair scoring. PyTorch Geometric Temporal Representation learning on dynamic graphs. Little Ball of Fur A graph sampling library for NetworkX with a Scikit-Learn like API. Karate Club An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API. ML Workspace All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a Docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code) Neptune.ai Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility. steppy Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design. steppy-toolkit Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective. Datalab from Google easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively. Hortonworks Sandbox is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials. R is a free software environment for statistical computing and graphics. Tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. RStudio IDE – powerful user interface for R. It’s free and open source, and works on Windows, Mac, and Linux. Python - Pandas - Anaconda Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing Pandas GUI Pandas GUI Scikit-Learn Machine Learning in Python NumPy NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays. Vaex Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds. SciPy SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization. Data Science Toolbox Coursera Course Data Science Toolbox Blog Wolfram Data Science Platform Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generate rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language. Datadog Solutions, code, and devops for high-scale data science. Variance Build powerful data visualizations for the web without writing JavaScript Kite Development Kit The Kite Software Development Kit (Apache License, Version 2.0), or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem. Domino Data Labs Run, scale, share, and deploy your models — without any infrastructure or setup. Apache Flink A platform for efficient, distributed, general-purpose data processing. Apache Hama Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce. Weka Weka is a collection of machine learning algorithms for data mining tasks. Octave GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab) Apache Spark Lightning-fast cluster computing Hydrosphere Mist a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services. Data Mechanics A data science and engineering platform making Apache Spark more developer-friendly and cost-effective. Caffe Deep Learning Framework Torch A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT Nervana's python based Deep Learning Framework Intel® Nervana™ reference deep learning framework committed to best performance on all hardware. Skale High performance distributed data processing in NodeJS Aerosolve A machine learning package built for humans. Intel framework Intel® Deep Learning Framework Datawrapper An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at github.com Tensor Flow TensorFlow is an Open Source Software Library for Machine Intelligence Natural Language Toolkit An introductory yet powerful toolkit for natural language processing and classification Annotation Lab Free End-to-End No-Code platform for text annotation and DL model training/tuning. Out-of-the-box support for Named Entity Recognition, Classification, Relation extraction and Assertion Status Spark NLP models. Unlimited support for users, teams, projects, documents. nlp-toolkit for node.js This module covers some basic nlp principles and implementations. The main focus is performance. When we deal with sample or training data in nlp, we quickly run out of memory. Therefore every implementation in this module is written as stream to only hold that data in memory that is currently processed at any step. Julia high-level, high-performance dynamic programming language for technical computing IJulia a Julia-language backend combined with the Jupyter interactive environment Apache Zeppelin Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more Featuretools An open source framework for automated feature engineering written in python Optimus Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend. Albumentations А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, and detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops. DVC An open-source data science version control system. It helps track, organize and make data science projects reproducible. In its very basic scenario it helps version control and share large data and model files. Lambdo is a workflow engine that significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation. Feast A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving. Polyaxon A platform for reproducible and scalable machine learning and deep learning. UBIAI Easy-to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling Trains Auto-Magical Experiment Manager, Version Control & DevOps for AI Hopsworks Open-source data-intensive machine learning platform with a feature store. Ingest and manage features for both online (MySQL Cluster) and offline (Apache Hive) access, train and serve models at scale. MindsDB MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code. Lightwood A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code. AWS Data Wrangler An open-source Python package that extends the power of Pandas library to AWS connecting DataFrames and AWS data related services (Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc). Amazon Rekognition AWS Rekognition is a service that lets developers working with Amazon Web Services add image analysis to their applications. Catalog assets, automate workflows, and extract meaning from your media and applications. Amazon Textract Automatically extract printed text, handwriting, and data from any document. Amazon Lookout for Vision Spot product defects using computer vision to automate quality inspection. Identify missing product components, vehicle and structure damage, and irregularities for comprehensive quality control. Amazon CodeGuru Automate code reviews and optimize application performance with ML-powered recommendations. CML An open source toolkit for using continuous integration in data science projects. Automatically train and test models in production-like environments with GitHub Actions & GitLab CI, and autogenerate visual reports on pull/merge requests. Dask An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data) Statsmodels A Python-based inferential statistics, hypothesis testing and regression framework Gensim An open-source library for topic modeling of natural language text spaCy A performant natural language processing toolkit Grid Studio Grid studio is a web-based spreadsheet application with full integration of the Python programming language. Python Data Science Handbook Python Data Science Handbook: full text in Jupyter Notebooks Shapley A data-driven framework to quantify the value of classifiers in a machine learning ensemble. DAGsHub A platform built on open source tools for data, model and pipeline management. Deepnote A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud. Valohai An MLOps platform that handles machine orchestration, automatic reproducibility and deployment. PyMC3 A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning) PyStan Python interface to Stan (Bayesian inference and modeling) hmmlearn Unsupervised learning and inference of Hidden Markov Models Chaos Genius ML powered analytics engine for outlier/anomaly detection and root cause analysis Nimblebox A full-stack MLOps platform designed to help data scientists and machine learning practitioners around the world discover, create, and launch multi-cloud apps from their web browser. Towhee A Python library that helps you encode your unstructured data into embeddings. LineaPy Ever been frustrated with cleaning up long, messy Jupyter notebooks? With LineaPy, an open source Python library, it takes as little as two lines of code to transform messy development code into production pipelines. envd 🏕️ machine learning development environment for data science and AI/ML engineering teams Explore Data Science Libraries A search engine 🔎 tool to discover & find a curated list of popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources MLEM 🐶 Version and deploy your ML models following GitOps principles MLflow MLOps framework for managing ML models across their full lifecycle cleanlab Python library for data-centric AI and automatically detecting various issues in ML datasets AutoGluon AutoML to easily produce accurate predictions for image, text, tabular, time-series, and multi-modal data Arize AI Arize AI community tier observability tool for monitoring machine learning models in production and root-causing issues such as data quality and performance drift. Aureo.io Aureo.io is a low-code platform that focuses on building artificial intelligence. It provides users with the capability to create pipelines, automations and integrate them with artificial intelligence models – all with their basic data. ERD Lab Free cloud based entity relationship diagram (ERD) tool made for developers. Arize-Phoenix MLOps in a notebook - uncover insights, surface problems, monitor, and fine tune your models. Comet An MLOps platform with experiment tracking, model production management, a model registry, and full data lineage to support your ML workflow from training straight through to production. Opik Evaluate, test, and ship LLM applications across your dev and production lifecycles. Synthical AI-powered collaborative environment for research. Find relevant papers, create collections to manage bibliography, and summarize content — all in one place teeplot Workflow tool to automatically organize data visualization output Streamlit App framework for Machine Learning and Data Science projects Gradio Create customizable UI components around machine learning models Weights & Biases Experiment tracking, dataset versioning, and model management DVC Open-source version control system for machine learning projects Optuna Automatic hyperparameter optimization software framework Ray Tune Scalable hyperparameter tuning library Apache Airflow Platform to programmatically author, schedule, and monitor workflows Prefect Workflow management system for modern data stacks Kedro Open-source Python framework for creating reproducible, maintainable data science code Hamilton Lightweight library to author and manage reliable data transformations SHAP Game theoretic approach to explain the output of any machine learning model LIME Explaining the predictions of any machine learning classifier flyte Workflow automation platform for machine learning dbt Data build tool SHAP Game theoretic approach to explain the output of any machine learning model LIME Explaining the predictions of any machine learning classifier zasper Supercharged IDE for Data Science  skrub A Python library to ease preprocessing and feature engineering for tabular machine learning  Codeflash Ship Blazing-Fast Python Code — Every Time