This is the Windows app named Statistics for Data Scientists whose latest release can be downloaded as statistics-for-data-scientistssourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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- 6. Download de applicatie en installeer deze.
- 7. Download Wine van de softwarebronnen voor Linux-distributies. Eenmaal geïnstalleerd, kunt u vervolgens dubbelklikken op de app om ze met Wine uit te voeren. Je kunt ook PlayOnLinux proberen, een mooie interface via Wine waarmee je populaire Windows-programma's en -games kunt installeren.
Wine is een manier om Windows-software op Linux uit te voeren, maar zonder dat Windows vereist is. Wine is een open-source Windows-compatibiliteitslaag die Windows-programma's rechtstreeks op elke Linux-desktop kan uitvoeren. In wezen probeert Wine genoeg van Windows opnieuw te implementeren, zodat het al die Windows-applicaties kan draaien zonder Windows echt nodig te hebben.
SCREENSHOTS
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Statistics for Data Scientists
PRODUCTBESCHRIJVING
The “statistics-for-data-scientists” repository is a pedagogical resource designed to bridge rigorous statistics theory and practical data science workflows. The code and materials are intended to help data scientists and analysts grasp statistical principles (e.g. inference, regressions, hypothesis testing, probability, confidence intervals) in contexts relevant to real data analysis tasks. The repository includes Jupyter notebooks, R scripts, worked examples, and possibly problem sets that illustrate how statistical methods are applied to real datasets. It aims to demystify the bridge between textbook statistics and empirical modeling by walking through assumption checking, visualization, interpreting outputs, and pitfalls of misuse. Throughout, the content emphasizes clarity and accessibility, showing not just how to run statistical tests or build models, but what they mean and when one method is preferred over another.
Kenmerken
- Jupyter notebooks and scripts demonstrating core statistical concepts (inference, regression, probability)
- Worked examples applying statistical methods to real datasets
- Emphasis on interpretation and assumption diagnostics
- Integration of theory with practical data science workflows
- Accessible teaching style that connects textbook ideas to applied modeling
- Code + narrative format to support learning and reference usage
Programmeertaal
R
Categorieën
This is an application that can also be fetched from https://sourceforge.net/projects/statistics-for-data-sc.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
