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LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone.

Tokenize is a Julia package that serves a similar purpose and API as the tokenize module in Python but for Julia. This is to take a string or buffer containing Julia code, perform lexical analysis and return a stream of tokens.

Package to call Python functions from the Julia language. This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them.

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. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from...

Kinetic is a computational fluid dynamics toolbox written in Julia. It aims to furnish efficient modeling and simulation methodologies for fluid dynamics, augmented by the power of machine learning. Based on differentiable programming, mechanical and neural network models are fused and solved in a unified framework. Simultaneous 1-3 dimensional numerical simulations can be performed on CPUs and GPUs.

The scikit-learn Python library has proven very popular with machine learning researchers and data scientists in the last five years. It provides a uniform interface for training and using models, as well as a set of tools for chaining (pipelines), evaluating, and tuning model hyperparameters. ScikitLearn.jl brings these capabilities to Julia. Its primary goal is to integrate both Julia- and Python-defined models together into the scikit-learn framework.

Indicators is a Julia package offering efficient implementations of many technical analysis indicators and algorithms. This work is inspired by the TTR package in R and the Python implementation of TA-Lib, and the ultimate goal is to implement all of the functionality of these offerings (and more) in Julia. 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.

...Spark.jl provides an interface to Apache Spark™ platform, including SQL / DataFrame and Structured Streaming. It closely follows the PySpark API, making it easy to translate existing Python code to Julia. Spark.jl supports multiple cluster types (in client mode), and can be considered as an analog to PySpark or RSpark within the Julia ecosystem. It supports running within on-premise installations, as well as hosted instances such as Amazon EMR and Azure HDInsight.