Search Results for "data science"
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Showing 747 open source projects for "data science"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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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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Computer Science courses video lectures
List of Computer Science courses with video lectures
This repository is a curated list of full-length computer science video lecture series across many universities and MOOC platforms, helping learners assemble their own curriculum. The list spans foundational topics like algorithms, data structures, operating systems, computer networks, machine learning, and more, all delivered via lectures rather than just textual tutorials. The contributor guidelines encourage adding high-quality courses (not just casual tutorials) so the list remains academically oriented. ... -
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NYC Taxi Data
Import public NYC taxi and for-hire vehicle (Uber, Lyft)
...It also contains example analyses—spatial and temporal visualizations like maps, time-series plots, and hotspot detection—highlighting insights such as patterns of demand, peak times, and geospatial distributions. The repository is often used as a benchmark dataset and example for teaching, benchmarking, and demonstration purposes in the data science and urban analytics communities. -
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XGBoost
Scalable and Flexible Gradient Boosting
...XGBoost works by implementing machine learning algorithms under the Gradient Boosting framework. It also offers parallel tree boosting (GBDT, GBRT or GBM) that can quickly and accurately solve many data science problems. XGBoost can be used for Python, Java, Scala, R, C++ and more. It can run on a single machine, Hadoop, Spark, Dask, Flink and most other distributed environments, and is capable of solving problems beyond billions of examples. -
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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. -
PRTG Catches Network Issues Before They Cause DowntimeReactive troubleshooting usually means hearing about a problem from frustrated users, not your monitoring tool. PRTG sets threshold-based alerts across devices, servers and applications, notifying your team by email, SMS or push the moment a metric crosses a set limit. That means catching a failing disk or overloaded server before it becomes an outage and getting time back from firefighting. Start a free trial and set your first alerts today.
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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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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. ... -
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Computer Science Flash Cards
Mini website for testing both general CS knowledge and enforce coding
This repository collects concise flash cards that cover the core ideas of a traditional computer science curriculum with a focus on interview readiness. The cards distill topics like time and space complexity, classic data structures, algorithmic paradigms, operating systems, networking, and databases into short, testable prompts. They are designed for spaced-repetition style study so you can cycle frequently through fundamentals until recall feels automatic. -
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cuDF
GPU DataFrame Library
...The RAPIDS suite of open-source software libraries aims to enable the execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. -
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Nuclio
High-Performance Serverless event and data processing platform
Nuclio is an open source and managed serverless platform used to minimize development and maintenance overhead and automate the deployment of data-science-based applications. Real-time performance running up to 400,000 function invocations per second. Portable across low laptops, edge, on-prem and multi-cloud deployments. The first serverless platform supporting GPUs for optimized utilization and sharing. Automated deployment to production in a few clicks from Jupyter notebook. ... -
Demo Series - Small Business Backup By VeeamWatch this on-demand demo series and learn how to protect your Microsoft 365 data with clear, simple, actionable steps that are easy to implement for businesses of all sizes.
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Kedro
A Python framework for creating reproducible, maintainable code
Kedro is an open sourced Python framework for creating maintainable and modular data science code. Provides the scaffolding to build more complex data and machine-learning pipelines. In addition, there's a focus on spending less time on the tedious "plumbing" required to maintain data science code; this means that you have more time to solve new problems. Standardises team workflows; the modular structure of Kedro facilitates a higher level of collaboration when teams solve problems together. ... -
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dplyr
dplyr: A grammar of data manipulation
dplyr is an R package that provides a consistent and intuitive grammar for data manipulation, enabling users to filter, arrange, summarize, and transform data efficiently. Part of the tidyverse ecosystem, dplyr simplifies complex data operations through a clear and readable syntax, whether working with data frames, tibbles, or databases. It is widely used in data science and statistical analysis workflows. -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using 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. ... -
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SIT742
SIT742: Modern Data Science
SIT742: Modern Data Science. -
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gophernotes
The Go kernel for Jupyter notebooks and nteract
...Use gophernotes to create and share documents that contain live Go code, equations, visualizations and explanatory text. These notebooks, with the live Go code, can then be shared with others via email, Dropbox, GitHub and the Jupyter Notebook Viewer. Go forth and do data science, or anything else interesting, with Go notebooks! This project utilizes a Go interpreter called gomacro under the hood to evaluate Go code interactively. The gophernotes logo was designed by the brilliant Marcus Olsson and was inspired by Renee French's original Go Gopher design. If you have the JUPYTER_PATH environmental variable set or if you are using an older version of Jupyter, you may need to copy this kernel config to another directory. -
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RStudio
RStudio is an integrated development environment (IDE) for R
RStudio is a powerful, full-featured integrated development environment (IDE) tailored primarily for the R programming language but increasingly supportive of other languages like Python and Julia. It brings together console, editor, plotting, workspace, history, and file-management panes into a unified interface, helping data scientists, statisticians, and analysts to work more productively. The IDE is cross-platform: there are desktop versions for Windows, macOS and Linux, as well as a... -
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DearPyGui
Graphical User Interface Toolkit for Python with minimal dependencies
...DPG is well suited for creating simple user interfaces as well as developing complex and demanding graphical interfaces. DPG offers a solid framework for developing scientific, engineering, gaming, data science and other applications that require fast and interactive interfaces. The Tutorials will provide a great overview and links to each topic in the API Reference for more detailed reading. Complete theme and style control. GPU-based rendering and efficient C/C++ code. -
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tsfresh
Automatic extraction of relevant features from time series
...With tsfresh this process is automated and all your features can be calculated automatically. Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data Science endeavours in python. The extracted features can be used to describe or cluster time series based on the extracted characteristics. Further, they can be used to build models that perform classification/regression tasks on the time series. Often the features give new insights into time series and their dynamics. -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. ... -
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DocArray
The data structure for multimodal data
...The foundation data structure of Jina, CLIP-as-service, DALL·E Flow, DiscoArt etc. Data science powerhouse: greatly accelerate data scientists’ work on embedding, k-NN matching, querying, visualizing, evaluating via Torch/TensorFlow/ONNX/PaddlePaddle on CPU/GPU. Data in transit: optimized for network communication, ready-to-wire at anytime with fast and compressed serialization in Protobuf, bytes, base64, JSON, CSV, DataFrame. -
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PSLab Android App
PSLab Android App
Repository for the PSLab Android App for performing experiments with the Pocket Science Lab open-hardware platform. This repository holds the Android App for performing experiments with PSLab. PSLab is a tiny pocket science lab that provides an array of equipment for doing science and engineering experiments. It can function like an oscilloscope, waveform generator, frequency counter, programmable voltage and current source and also as a data logger. ... -
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RStudio Cheatsheets
Curated collection of official cheat sheets for data science tools
The cheatsheets repository from RStudio is a curated collection of official cheat sheets for R, RStudio, the tidyverse, Shiny, and related data science tools. Each cheat sheet is a single (or double) page PDF that condenses important syntax, functions, workflows, and best practices into a visually organized format ideal for quick reference. The repository contains source files (R Markdown or LaTeX) that generate the cheat sheets, version history, and metadata (title, author, description) for each. ... -
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Synapse Machine Learning
Simple and distributed Machine Learning
...SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit, and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of data sources. SynapseML also brings new networking capabilities to the Spark Ecosystem. ... -
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Apache Spark
A unified analytics engine for large-scale data processing
...With Spark Streaming (microbatches) and Structured Streaming, it delivers low-latency event processing suitable for real-time analytics. The built-in MLlib library provides scalable machine learning algorithms, while GraphX enables graph computations integrated with data pipelines. Spark supports multiple languages—Scala, Java, Python, R—and connects with many storage systems like HDFS, S3, Cassandra, and streaming platforms like Kafka, making it a versatile choice for big data workloads in analytics, ETL, and data science. -
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Lua
The Lua development repository, as seen by the Lua team
Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description. Lua combines simple procedural syntax with powerful data description constructs based on associative arrays and extensible semantics. Lua is dynamically typed, runs by interpreting bytecode with a register-based virtual machine, and has automatic memory management with incremental garbage collection, making it ideal for configuration, scripting, and rapid prototyping. ... -
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GeoStats.jl
An extensible framework for geospatial data science
GeoStats.jl is a Julia framework for geospatial data science and geostatistical modeling. It’s fully implemented in Julia and designed to provide an extensible, high-performance stack that handles spatial domains, interpolation, simulation, learning, and visualization. The package is modular: it breaks out geometry, spatial domains, transforms, variograms, covariance models, and modeling into subpackages (e.g., GeoStatsBase, GeoStatsModels, GeoStatsTransforms).
