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Become an AI-Driven Enterprise with Automated Machine Learning.
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Data science is a team sport. Data scientists, citizen data scientists, business users, and developers need flexible and extensible tools that promote collaboration, automation, and...
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JASP, a low fat alternative to SPSS, a delicious alternative to R.
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jamovi is a free and open statistical platform which is intuitive to use, and can provide the...
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PSPP is a free software application for analysis of sampled data.
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BlueSky Statistics is a fully featured statistics application and development framework built on...
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ServerMonitoring.me provides a completely free server monitoring tool for Linux and Windows servers. Simply install agent on CentOS, Ubuntu, Debian!
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SOFA Statistics Open For All - the user-friendly, open-source statistics, analysis, & reporting software package.
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The Statistics Portal for Market Data, Market Research and Market Studies.
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Domino is a data science platform that enables collaborative and reusable analysis of data.
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Simply upload your spreadsheet or dataset, then select the relationships you want to explore. Statwing was built by and for analysts, so you can clean data, explore relationships, and create charts in minutes instead of hours.
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MonteCarlito is a free Excel-add-in to do Monte-Carlo-simulations.
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SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering.ย .
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Visual workflow designer for predictive analytics that brings data science and machine learning to everyone on the analytics team.
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Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
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It is a cloud based statistical software for beginners and business users.
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IBM ILOG CPLEX Optimization Studio is an easy-to-use, affordable data analytics solution for businesses of all sizes who want to optimize their operations.
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Displayr is a data science, visualization, and reporting platform for everyone.
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AIXON is an AI-powered data science solution that enables data scientists of all levels of experience to build machine learning models and deploy them into production with less code and without the need for a data science team.
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Equip data scientists with self-service access to any data, anywhere, so they can quickly develop and prototype machine learning projects and easily deploy them to production.