With data visualization becoming a popular technique every day, analytics software developers have to make a trade-off between several things including speed and beauty, features and simplicity, and a dynamic and static interface. Some programs update their features in real-time while others create a user interface and never change it. Julia comes with a data visualization toolset and interface known as Plots. jl that provides a common Application Programming Interface across different backends like PlotlyJS.jl, PyPlot.jl, and GR.jl. It also allows users to process complex animations and graphics, all thanks to its sophisticated package, Makie.jl.
Julia includes a framework known as DataFrames.jl that enables users to work with complex sets of data and carry out data manipulations of all kinds. Reading CSV files can be done with CSV.jl, a fast multi-threaded package incorporated in the Julia data ecosystem. OnlineStats.jl can be used to perform online computations on streaming data and Queryverse can be used for query visualization and file Input/Output functions. The Julia data environment also includes JuliaGraphs that together with tables make it easy for researchers to manipulate combinatorial data.
In addition to the features listed above, Julia incorporates an MLJ.jl, a package designed to provide an interface to an abundance of machine learning algorithms such as decision trees, clustering, and generalized linear models, to name a few. Frameworks such as TextAnalysis.jl, ObjectDetector, and Metalhead provide pre-trained models that are ready to use, and Knet.jl and Flux.jl provide a platform for deep learning. AlphaZero.jl allows users to implement and reinforce learning algorithms.