Open Source Data Science Tools - Page 2
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
Welcome to Amazon SageMaker. This projects highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker. If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. Studio offers teams and companies easy on-boarding for their team members, freeing them up from complex systems admin and security processes. Administrators control data access and resource provisioning for their users. Notebook Instances are another option. They have the familiar Jupyter and JuypterLab interfaces that work well for single users, or small teams where users are also administrators. Advanced users also use SageMaker solely with the AWS CLI and Python scripts using boto3 and/or the SageMaker Python SDK. -
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Awesome Fraud Detection Research Papers
A curated list of data mining papers about fraud detection
A curated list of data mining papers about fraud detection from several conferences. -
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ClearML
Streamline your ML workflow
ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions for thousands of data science teams all over the world. It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments and other workflows with ClearML powerful and versatile set of classes and methods. The ClearML Server storing experiment, model, and workflow data, and supports the Web UI experiment manager, and ML-Ops automation for reproducibility and tuning. It is available as a hosted service and open source for you to deploy your own ClearML Server. The ClearML Agent for ML-Ops orchestration, experiment and workflow reproducibility, and scalability. -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
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Cookiecutter Data Science
Project structure for doing and sharing data science work
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards, ultimately, data science code quality is about correctness and reproducibility. It's no secret that good analyses are often the result of very scattershot and serendipitous explorations. Tentative experiments and rapidly testing approaches that might not work out are all part of the process for getting to the good stuff, and there is no magic bullet to turn data exploration into a simple, linear progression. -
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DEPRECATED - KVFinder
Cavity Detection PyMOL plugin
The KVFinder software, originally published in 2014, is deprecated. We published more recent software: parKVFinder and pyKVFinder. [parKVFinder] A Linux/macOS version is available in this GitHub repository, https://github.com/LBC-LNBio/parKVFinder, while a Windows version is in this GitHub repository, https://github.com/LBC-LNBio/parKVFinder-win. Please read and cite the original paper ParKVFinder: A thread-level parallel approach in biomolecular cavity detection (10.1016/j.softx.2020.100606). [pyKVFinder] pyKVFinder is available in this Python Package Index (PyPI) repository, https://pypi.org/project/pyKVFinder and this GitHub repository, https://github.com/LBC-LNBio/pyKVFinder. Please read and cite the original paper pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science (10.1186/s12859-021-04519-4). -
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DSTK - Data Science TooKit 3
Data and Text Mining Software for Everyone
DSTK - Data Science Toolkit 3 is a set of data and text mining softwares, following the CRISP DM model. DSTK offers data understanding using statistical and text analysis, data preparation using normalization and text processing, modeling and evaluation for machine learning and algorithms. It is based on the old version DSTK at https://sourceforge.net/projects/dstk2/ DSTK Engine is like R. DSTK ScriptWriter offers GUI to write DSTK script. DSTK Studio offers SPSS Statistics like GUI for data mining, and DSTK Text Explorer offers GUI for Text Mining. DSTK Engine and DSTK ScriptWriter are opensource, but DSTK Studio and Text Explorer requires small amount of payment. DSTK Studio and Text Explorer are free to use 10 times -
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Data Science
A learning library for Data Science
This project is a collection of sub-projects that contain various experiments in various languages for exploring the machine learning and data science fields. Notable languages are Scala and Python. -
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Data Science Notes
Curated collection of data science learning materials
Data Science Notes is a large, curated collection of data science learning materials, with explanations, code snippets, and structured notes across the typical end-to-end workflow. It spans foundational math and statistics through data wrangling, visualization, machine learning, and practical project organization. The content emphasizes hands-on understanding by pairing narrative notes with runnable examples, making it useful for both self-study and classroom settings. Because it aggregates topics in one place, learners can move linearly or jump into specific areas as needed during projects. The notes also highlight common pitfalls and good practices, which helps beginners adopt professional habits early. It’s a living resource that many students consult when revising fundamentals or exploring adjacent tools in the ecosystem. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Data Science at the Command Line
Data science at the command line
Command Line by Jeroen Janssens, published by O’Reilly Media in October 2021. Obtain, scrub, explore, and model data with Unix Power Tools. This repository contains the full text, data, and scripts used in the second edition of the book Data Science at the Command Line by Jeroen Janssens. This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 100 Unix power tools, useful whether you work with Windows, macOS, or Linux. You’ll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you’re comfortable processing data with Python or R, you’ll learn how to greatly improve your data science workflow by leveraging the command line’s power. -
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Deep Learning course
Slides and Jupyter notebooks for the Deep Learning lectures
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion. -
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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include DS-GA 1001 Intro to Data Science or a graduate-level machine learning course. To be able to follow the exercises, you are going to need a laptop with Miniconda (a minimal version of Anaconda) and several Python packages installed. The following instruction would work as is for Mac or Ubuntu Linux users, Windows users would need to install and work in the Git BASH terminal. JupyterLab has a built-in selectable dark theme, so you only need to install something if you want to use the classic notebook interface. -
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DeepLearningProject
An in-depth machine learning tutorial
This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made. The dataset is not one of the standard sets like MNIST or CIFAR, you will make you very own dataset. Then you will go through a couple conventional machine learning algorithms, before finally getting to deep learning! In the fall of 2016, I was a Teaching Fellow (Harvard's version of TA) for the graduate class on "Advanced Topics in Data Science (CS209/109)" at Harvard University. I was in charge of designing the class project given to the students, and this tutorial has been built on top of the project I designed for the class. -
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FlexiList.
FlexiList is a Java data structure that combines the benefits of array
FlexiList is a Java data structure that combines the benefits of arrays and linked lists. Like an array, it allows for efficient access to elements by index. Like a linked list, it allows for efficient insertion and deletion of elements at any position in the list. Benefits Over Arrays and ArrayList ->Efficient Insertion and Deletion: FlexiList can insert or delete nodes at any position in the list in O(1) time, whereas arrays require shifting all elements after the insertion or deletion point. ->Dynamic Size: FlexiList can grow or shrink dynamically as elements are added or removed, whereas arrays have a fixed size. ->Good Memory Locality: FlexiList nodes are stored in a contiguous block of memory, making it more cache-friendly than arrays. ->Faster Insertion and Deletion: FlexiList can insert or delete nodes at any position in the list in O(1) time, whereas ArrayList requires shifting all elements after the insertion or deletion point. -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions. -
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LIFELINES
Survival analysis in Python
LIFELINES is a pure Python library for survival analysis, a statistical field focused on modeling time until an event occurs. It can be used for traditional cases like medical survival time, but also for business and product questions such as churn, subscription length, equipment failure, and customer retention. The library includes estimators such as Kaplan-Meier, Nelson-Aalen, and regression-based survival models. It is designed to be accessible to Python users and works well with common scientific computing workflows. Built-in plotting methods and datasets help users explore survival curves and compare groups visually. It is a practical tool for analysts, researchers, and data scientists who need event-time modeling without leaving Python. -
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LIFETIMES
Lifetime value in Python
LIFETIMES is a Python library for customer lifetime value and repeat purchase behavior modeling. It helps analysts estimate how frequently customers may return, how long they may remain active, and how much value they may generate over time. The library is built around probabilistic models commonly used in customer analytics, including transaction frequency and monetary value modeling. It is useful for ecommerce, subscription-adjacent businesses, retail analytics, and retention analysis. The repository is now archived, so it should be treated as a stable historical project rather than an actively developed package. For current work, its ideas remain valuable, but teams may want to consider newer successor tools for ongoing production use. -
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ML workspace
All-in-one web-based IDE specialized for machine learning
All-in-one web-based development environment for machine learning. The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH. Easy to deploy on Mac, Linux, and Windows via Docker. Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.By default, the workspace container has no resource constraints and can use as much of a given resource as the host’s kernel scheduler allows. -
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Metaflow
A framework for real-life data science
Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. -
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NannyML
Detecting silent model failure. NannyML estimates performance
NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and automatically detect silent model failure. By using NannyML, data scientists can finally maintain complete visibility and trust in their deployed machine learning models. When the actual outcome of your deployed prediction models is delayed, or even when post-deployment target labels are completely absent, you can use NannyML's CBPE-algorithm to estimate model performance. -
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NuzeBot
Finds interesting news headlines.
This is a bot to finds the news you want to see. It can be made to find the news that interests you and reject everything else. View on one page the most interesting headlines from many websites. -
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OGLDataScienceTool
Opengl tool for data science visualization
Data visualization tool written in LWJGL Compatible with libgdx and other opengl wrappers The project depends on apache poi, and apache commons, for office files support Planned features for next release: * reading json, and other nosql data structures * jdbc connection for creating dataframes * data heatmaps, and additional plots for questions, contact me kumar.santhi1982@hotmail.com more details: http://www.java-gaming.org/topics/ds/41920/view.html http://datascienceforindia.com/ -
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Orchest
Build data pipelines, the easy way
Code, run and monitor your data pipelines all from your browser! From idea to scheduled pipeline in hours, not days. Interactively build your data science pipelines in our visual pipeline editor. Versioned as a JSON file. Run scripts or Jupyter notebooks as steps in a pipeline. Python, R, Julia, JavaScript, and Bash are supported. Parameterize your pipelines and run them periodically on a cron schedule. Easily install language or system packages. Built on top of regular Docker container images. Creation of multiple instances with up to 8 vCPU & 32 GiB memory. A free Orchest instance with 2 vCPU & 8 GiB memory. Simple data pipelines with Orchest. Each step runs a file in a container. It's that simple! Spin up services whose lifetime spans across the entire pipeline run. Easily define your dependencies to run on any machine. Run any subset of the pipeline directly or periodically. -
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Positron
Positron, a next-generation data science IDE
Positron is a next-generation integrated development environment (IDE) created by Posit PBC (formerly RStudio Inc) specifically tailored for data science workflows in Python, R, and multi-language ecosystems. It aims to unify exploratory data analysis, production code, and data-app authoring in a single environment so that data scientists move from “question → insight → application” without switching tools. Built on the open-source Code-OSS foundation, Positron provides a familiar coding experience along with specialized panes and tooling for variable inspection, data-frame viewing, plotting previews, and interactive consoles designed for analytical work. The IDE supports notebook and script workflows, integration of data-app frameworks (such as Shiny, Streamlit, Dash), database and cloud connections, and built-in AI-assisted capabilities to help write code, explore data, and build models.
