Search Results for "data modeling" - Page 11
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Showing 544 open source projects for "data modeling"
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Makani
Makani was developed a commercial-scale airborne wind turbine
Makani was an ambitious Google X project that sought to harness wind energy using airborne wind turbines — autonomous kites capable of generating power while flying in crosswind patterns. This open-source repository contains the complete software stack that powered Makani’s research and flight systems, including the flight simulator, autopilot controller, avionics firmware, visualization tools, and ground control software. The software enables simulation, control, and analysis of the Makani... -
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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... -
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EliteQuant
A list of online resources for quantitative modeling, trading, etc.
EliteQuant is a curated directory of online resources for quantitative finance: trading, portfolio management, quantitative modeling, data sources, libraries, platforms, and communities. It is not a software library per se, but a “list of things” - i.e., an aggregator of open source projects, blogs, tools etc., intended to help practitioners find useful resources. It is licensed under Apache-2.0, and maintained by volunteers. A list of online resources for quantitative modeling, trading, and portfolio management. ... -
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We are developing data standards and software tools that implement these standards to develop a systemic approach to modeling, capturing, analyzing and disseminating flow cytometry data.
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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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Quantitative-Notebooks
Educational notebooks on quantitative finance, algorithmic trading
...Because quantitative analysis often requires visualization, statistics, and time series processing, these notebooks also serve as templates for real financial research and strategy prototyping. Users can adapt the examples to their own data sources, financial instruments, and modeling techniques. -
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The Object-Role Modeling (ORM) standard version 2, associated schemas and generation tools, and a reference implementation in the form of the Natural Object-Role Modeling Architect for Visual Studio (NORMA) product.
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Albedo
A recommender system for discovering GitHub repos
Albedo is an open-source recommender system aimed at helping developers discover GitHub repositories by learning from activity signals. It treats repositories and developers as a graph of interactions and applies large-scale matrix factorization to model affinities, with Apache Spark providing the distributed data processing. The project focuses on implicit feedback—stars, watches, and other engagement metrics—so it can build useful recommendations without explicit ratings. A reproducible... -
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The latest ESMF development is happening in GitHub: https://github.com/esmf-org/esmf https://earthsystemmodeling.org The Earth System Modeling Framework provides high-performance software infrastructure and superstructure for the construction and coupling of climate, weather, and data assimilation applications.
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Newsvendor Model Simulation Spreadsheet
Excel Spreadsheet Model for Single Period Inventory Problems
The spreadsheet (Excel) of a single-period inventory model with stochastic demand can be used as a simulation tool for engineering education or Decision Support System. Based on spreadsheet techniques and examples described in the following sources: Albright S. C., & Winston W. L. (2005). Spreadsheet modeling and applications: essentials of practical management science, South-Western Pub. Albright, S. C. W. C., Winston, W., & Zappe, C. (2010). Data analysis and decision making. Cengage Learning. Hill, A. V. (2011). The newsvendor problem. White Paper, 57-23. Lawrence, J. A., & Pasternack, B. A. (2002). Applied management science. ... -
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PBTK Optimizer
Application for optimization of parameters in PBTK models
...Other parameters can be determined through in-vitro experiments or through extrapolation using published equations. When it is impractical to use these methods to estimate a parameter, techniques can be used to optimize parameters so that model results best fit validation data. This tool was designed to optimize a user-specified list of parameters to a user-specified PBTK model. The user also controls validation data and optimization algorithms. In addition to optimized parameters, the tool outputs statistical information about the fit of the optimized model. -
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swirl
Learn R, in R
swirl is an R package that allows interactive, in-R learning of statistics, data science, R programming etc. The idea is that you load swirl in R, and it presents you with lessons (within R’s console or RStudio) that ask you to type commands, check results, and progress through tutorial material—without leaving the R environment. It is used for teaching R, especially for beginners, as well as for self-paced learning of packages, data manipulation, visualization, etc. Lessons and content are... -
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YouTube-8M
Starter code for working with the YouTube-8M dataset
youtube-8m is Google’s open source starter code and reference implementation for training and evaluating machine learning models on the YouTube-8M dataset, one of the largest video understanding datasets publicly released. The repository provides a complete pipeline for video-level and frame-level modeling using TensorFlow, including data reading, model training, evaluation, and inference. It was developed to support the YouTube-8M Video Understanding Challenge (hosted on Kaggle and featured at ICCV 2019), enabling researchers and practitioners to benchmark video classification models on large-scale datasets with over millions of labeled videos. ... -
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Scikit-learn Tutorial
An introductory tutorial for scikit-learn
Scikit-learn Tutorial contains the materials for Jake VanderPlas’s introductory scikit-learn tutorial, originally used at major Python conferences. It provides a collection of notebooks that walk attendees from basic machine-learning concepts into practical modeling using the scikit-learn library. The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. The repository specifies a clear list of dependencies so that participants can reproduce the environment used in the tutorial, and many downstream forks keep the content updated for newer versions of scikit-learn. ... -
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openLCA
professional open source software for LCA
Software for sustainability assessment, highly modular; initially focused on Life Cycle Assessments. -
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Wind Path Visualization tool (WPV) is a simple model for tracking wind trajectories using measurement data (wind direction, wind speed, gas concentration) at a actual location. It has the following features.
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RoboSat
Semantic segmentation on aerial and satellite imagery
RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars. -
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GEOMS2
Geostatistics and geosciences modeling software
GEOMS2 is a geostatistics and geosciences modeling software. Provides interface for grid (mesh), point, surface and data (non-spatial) objects. It has a 3D viewer and 2D plots using the well known Python engines Mayavi and Matplotlib. It has several functions to manipulate your data as well as provide univariate and multivariate analysis. NOTE: The software is still an early beta. -
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Machine Learning Mindmap
A mindmap summarising Machine Learning concepts
...The project organizes a wide range of machine learning topics into an interconnected diagram that helps learners understand how concepts relate to one another across the broader field of artificial intelligence. The mind map covers fundamental areas such as data preprocessing, statistical analysis, supervised learning, unsupervised learning, reinforcement learning, and deep learning architectures. By arranging these concepts visually, the repository allows students and practitioners to quickly explore the relationships between algorithms, techniques, and modeling approaches used in modern machine learning workflows. ... -
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TenorSpace.js
Neural network 3D visualization framework
TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports the visualization... -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and... -
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DS-Take-Home
Solution to the book A Collection of Data Science Take-Home Challenge
...The problems cover a broad set of applied data science topics including conversion rate analysis, fraud detection, employee retention modeling, marketing campaign evaluation, and recommendation-style problems. -
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PyMOL Molecular Graphics System
PyMOL is an OpenGL based molecular visualization system
The Open-Source PyMOL repository has been moved to github: https://github.com/schrodinger/pymol-open-source We still use the pymol-users mailing list here on sourceforge. Please subscribe for community support: https://pymol.org/maillist (Note: SourceForge email newsletter and special offers are optional and can be unchecked) The PyMOL community wiki has its own home: https://pymolwiki.org/ -
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ATLAS_mPBPK
Modeling and Simulation of mPBPK models
ATLAS mPBPK is a MATLAb-based tool for modeling and Simulation of minimal Physiology Based Pharmacokinetic (mPBPK) models of small and large molecules. The tool enables the users to perform: i) PK data visualization, ii) simulation, iii) parameter optimization, and iv) local sensitivity analysis (SA) of mPBPK models in a simple and efficient manner. -
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Learn_Data_Science_in_3_Months
This is the Curriculum for "Learn Data Science in 3 Months"
This project lays out a 12-week plan to go from basics to a portfolio-ready understanding of data science. It breaks the journey into clear stages: Python fundamentals, data wrangling, visualization, statistics, machine learning, and end-to-end projects. The schedule mixes learning and doing, encouraging you to build small deliverables each week—like notebooks, dashboards, and model demos—to reinforce skills. It also includes suggestions for datasets and problem domains so you aren’t stuck... -
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tcomb
Type checking and DDD for JavaScript
...Its main value is bringing lightweight, expressive runtime type modeling to JavaScript applications.
