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This course aims to cover state-of-the-art methods in modern parallel Graphical Processing Unit (GPU) computing, supercomputing and code development with applications to natural sciences and engineering.

...The package is actively maintained by the JuliaData community, with contributions from over 200 developers worldwide. It is widely used for data science, research, and production applications, supported by extensive documentation, tutorials, and a free Julia Academy course. Cited in academic literature and trusted by practitioners, DataFrames.jl is one of the core libraries for Julia’s growing data ecosystem.

This repository holds slides and code for a full Bayesian statistics graduate course. Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution.

...This package has been written to be able to interface with both native Julia Array types, as well as the TS time series type from the Temporal package. Contributions are of course always welcome for wrapping any of these functions in methods for other types and/or packages out there, as are suggestions for other indicators to add to the lists.

This package aims to be a constraint solver completely written in Julia. The concepts are more or less fully described on my blog OpenSourc.es. There is of course also the general user manual here which explains how to solve your model.

This is an open source and open access book on how to do Data Science using Julia. Our target audience are researchers from all fields of applied sciences. Of course, we hope to be useful for industry too. You can navigate through the pages of the ebook by using the arrow keys (left/right) on your keyboard.