Data analytics and the mathematical programming languages that support them are changing the world but they are also suffering from growing pains. Technical computing languages that have been around for decades have been slow to adopt new compiler technologies such as JIT, optional type indications, and others.
Julia was introduced to solve these limitations. Julia is built on a solid foundation of JIT compiling, parallelism, and a mathematical syntax that will look familiar to users of other mathematical languages. Julia supports a rich type system. Scalars, vectors, arrays, tuples, composite types and several others can be defined in Julia. Julia is designed for technical computing and supports a fully remote cloud computing mode. Julia is free, open source, and library-friendly. The core Julia language is licensed under the MIT free software license.
This session will demonstrate some of the special capabilities of Julia and give you the tools you need to get started using this exciting technical computing language.
This session will review examples where forecasting models and other predictive analytic tools are being used by people who aren’t analytics experts in the real world. The session will then provide recommendations for creating interactive predictive analytic tools for non-data scientists and a review of technologies and tools that can assist with development.