DataSciPy | Julia Data Analysis
Jason Grafft will lead us through a data analysis using Julia programming language. The data set is from the State of California which makes a good deal of it's statewide testing and achievement data easily available (did not have this experience with Minnesota). My student (from LA) and I are looking at the outcome metric of meeting CSU (California State) minimums. The Julia programming language provides some features that are worth your consideration: Missing values: Julia 1.0 has full support for Missing values. Speed: Since this is somewhat live, we'll get see how fast Julia performs. DataFrames: They'll play a central role so we'll see them in greater detail. Broadcast operators and function chaining: Part necessity (see below), very useful, and a great example of Julia coding Libraries: Any list will be difficult to guarantee. Parts of Julia's ecosystem are sparse and it's sometimes easier to built what you need. I'd like to work FluxML in, and will definitely be using a handful of stats packages.
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