4 Repos
Awesome GitHub RepositoriesData Science Toolkits
Collections of tools for data analysis and modeling.
Distinguishing note: Focuses on the toolset itself rather than general education.
Explore 4 awesome GitHub repositories matching data & databases · Data Science Toolkits. Refine with filters or upvote what's useful.
Awesome Data Science Toolkits GitHub Repositories
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- academic/awesome-datascience
academic/awesome-datascience
29,416Auf GitHub ansehenThis project is a comprehensive, community-driven knowledge repository that serves as a centralized hub for data science resources. It provides a structured index of educational materials, software packages, and professional development tools designed to support both students and practitioners in navigating the data science landscape. The repository distinguishes itself through a hierarchical taxonomy that organizes a vast collection of external links into a human-readable, markdown-based document. By relying on distributed contributions, the project maintains an up-to-date snapshot of the fi
Aggregates essential tools for data science workflows.
analyticsawesome-listdata-mining - biolab/orange3
biolab/orange3
5,635Auf GitHub ansehenOrange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The
Provides a comprehensive collection of data preprocessing, modeling, and evaluation components for exploratory analysis.
Python - susanli2016/machine-learning-with-python
susanli2016/Machine-Learning-with-Python
4,583Auf GitHub ansehenDieses Projekt ist eine Python-Bibliothek für maschinelles Lernen und ein Data-Science-Toolkit, das für den Aufbau prädiktiver Modelle und die Analyse komplexer Datensätze entwickelt wurde. Es bietet eine Sammlung von Implementierungen für gängige überwachte und unüberwachte Algorithmen unter Verwendung des Scikit-Learn-Frameworks. Das Toolkit enthält eine Suite für prädiktive Modellierung zur Generierung von Vorhersagen aus historischen Daten und ein statistisches Analyse-Framework zur Anwendung von Bayes-Modellierung und Kausalitätstests. Es bietet zudem eine Datenvisualisierungssuite basierend auf Matplotlib zum Rendern statischer Diagramme und Grafiken, um Klassifikatorgrenzen und Datentrends zu interpretieren. Das Projekt deckt Daten-Clustering-Workflows zur Identifizierung von Mustern und Segmenten, explorative Datenanalyse und die Vorverarbeitung von Daten unter Verwendung von Pandas und NumPy ab.
Provides a collection of scripts using Pandas and NumPy for cleaning, preprocessing, and analyzing complex datasets.
Jupyter Notebook - floydhub/dl-docker
floydhub/dl-docker
3,856Auf GitHub ansehenThis project provides a standardized, portable containerized workspace designed to streamline deep learning development. By bundling essential machine learning frameworks and system dependencies into a single image, it eliminates manual configuration and ensures consistent execution across different host machines. The environment facilitates interactive data science workflows by enabling browser-based access to notebooks and monitoring tools through automated network port mapping. It also supports persistent data management by mounting local host directories directly into the container, ensur
Provides a portable software stack bundling deep learning frameworks, persistent storage, and interactive notebook access.
Python
