Search Results for "sourceforge.net/projects/winpython/files/winpython_3.8/3.8.10.0/winpython64-3.8.10.0.7z"
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Showing 13 open source projects for "sourceforge.net/projects/winpython/files/winpython_3.8/3.8.10.0/winpython64-3.8.10.0.7z"
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AestheticsPro Medical Spa SoftwareAestheticsPro is the most complete Aesthetics Software on the market today. HIPAA Cloud Compliant with electronic charting, integrated POS, targeted marketing and results driven reporting; AestheticsPro delivers the tools you need to manage your medical spa business. It is our mission To Provide an All-in-One Cutting Edge Software to the Aesthetics Industry.
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Award-Winning Medical Office Software Designed for Your SpecialtyRXNT is an ambulatory healthcare technology pioneer that empowers medical practices and healthcare organizations to succeed and scale through innovative, data-backed, AI-powered software.
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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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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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targets
Function-oriented Make-like declarative workflows for R
...It’s something like GNU Make for R, but more integrated. Skipping computation for up-to-date targets so that unchanged parts of the workflow are not recomputed. Targets can represent files or R objects, and tracking file changes etc is incorporated. -
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marimo
A reactive notebook for Python
...Run one cell and marimo reacts by automatically running affected cells, eliminating the error-prone chore of managing the notebook state. marimo's reactive UI elements, like data frame GUIs and plots, make working with data feel refreshingly fast, futuristic, and intuitive. Version with git, run as Python scripts, import symbols from a notebook into other notebooks or Python files, and lint or format with your favorite tools. You'll always be able to reproduce your collaborators' results. Notebooks are executed in a deterministic order, with no hidden state, delete a cell and marimo deletes its variables while updating affected cells. -
Collect! is a highly configurable debt collection softwareThe flexible & scalable debt collection software built to automate your workflow. From startup to enterprise, we have the solution for you.
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AI Data Science Team
An AI-powered data science team of agents
AI Data Science Team is a Python library and agent ecosystem designed to accelerate and automate common data science workflows by modeling them as specialized AI “agents” that can be orchestrated to perform tasks like data cleaning, transformation, analysis, visualization, and machine learning. It provides a modular agent framework where each agent focuses on a step in the typical data science pipeline — for example, loading data from CSV/Excel files, cleaning and wrangling messy datasets, engineering predictive features, building models with AutoML, connecting to SQL databases, and producing visual outputs — all driven by natural language or programmatic instructions. The project includes ready-to-use applications that showcase these agents in action, such as an exploratory data analysis copilot that generates reports, a pandas data analyst that combines wrangling and plotting, and SQL database agents that can query business databases and output results directly. -
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Data Science Notes
Curated collection of data science learning materials
...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. -
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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. -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
...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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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... -
The AI workplace management platformBy combining AI workflows, predictive intelligence, and automated insights, OfficeSpace gives leaders a complete view of how their spaces are used and how people work. Facilities, IT, HR, and Real Estate teams use OfficeSpace to optimize space utilization, enhance employee experience, and reduce portfolio costs with precision.
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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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xsv
A fast CSV command line toolkit written in Rust
xsv is a command line program for indexing, slicing, analyzing, splitting and joining CSV files. Commands should be simple, fast and composable. Simple tasks should be easy. Performance trade offs should be exposed in the CLI interface. Composition should not come at the expense of performance. Let's say you're playing with some of the data from the Data Science Toolkit, which contains several CSV files. Maybe you're interested in the population counts of each city in the world. ... -
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Adele
Adhoc Data Exploration - Live & Easy
...For example realtime OLAP, transformations, charts, analysis tools,... Connectors (e.g. JDBC, SAP ABAP, OData) can be used to pre-analyse the data and extract it without saving the data as text files. A plugin concept for enhancements are available. Enjoy! Its free for commercial use too. Adele runs without installation from USB stick for Windows, Linux and MacOSX. Last added changes: - data science tools (V1, IQR) - export to remote and desktop databases (mysql,sqlite, ms access) - internet features for emails and domains -
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Data Science Specialization
Course materials for the Data Science Specialization on Coursera
...It spans essential topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. It also offers a structured view of how multiple disciplines—programming, statistics, and applied data analysis—come together in a professional workflow.
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