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Knowing Statistics & practicing it is of vital importance to any organization from both its operations and management perspective. Information is becoming more and more statistically important, and are being tightly coupled from applications point of view. Almost every application now has a statistical module. It is becoming normal to learn from acquired data and make some valid predictions, recommendations, to the user. So the shift of making the application not only ease of use, but also to aid the user with timely and reliable feedback as an assistance within the bounds of the applications purpose and design.
Now Data is no more just data alone, it has to be scientific, and thus Data Science. Data Acquisition, Storage, Interpretation, Measurement, Statistics, Inference, Reporting, Visualization, Communication, Sharing & Recommendation are important tools of broader ecosystem.
Data Collection, Survey Tools
Statistical Analysis, Data Science Tools
|1.||GNU Scientific Library||Library||GSL||GPL|
|5.||Orange||Application, DM & ML||Orange||GPLv3|
|7.||Jamovi||Applicaiton, Library||Jamovi||AGPLv3, GPL2+|
|8.||Shogun||Application, Library||ShogunToolbox||BSD 3 Clause|
|9.||Stan||Library, Modeling||Stan||New BSD, GPLv3|
|13.||GNU Data Language||Application, Library||GDL||GPLv2|
To Learn DataScience : https://datalab.cc/
|1.||Rawgraphs||Vector Data Visualization based on D3||Rawgraphs||Apache 2.0|