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 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.ml 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. RStudio IDE – powerful user interface for R. It’s free and open source, 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 GU 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. 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 generating 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 . 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 . nlp-toolkit for node.js . 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, 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 which 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. LightTag Text Annotation Tool for teams 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). 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.