Julia for Data Science and Statistical Computing — Professional Development Continuing Education Course
ASA, Section on Statistical Computing
Julia (http://julialang.org) is a modern open source programming language for technical computing. Its design offers much greater speed and productivity compared to R or Python, as high-performance code does not need to be wrapped in a low level language like C or Fortran. After almost a decade of active development, Julia reached its first major release v1.0 on Aug 8, 2018 and is quickly gaining popularity in the communities of scientific computing and data science. This course comprises two parts. The first part introduces the Julia package ecosystem for data science, including data ingestion and cleaning, visualization, out-of-core processing, model fitting, and general analytics. The second part covers statistical computing using Julia. It begins with a comparison between Julia, R, and Python, and continues with a tutorial on using Julia for numerical linear algebra, numerical optimization, parallel/distributed computing, and GPU computing. Presenter Dr. Hua Zhou from UCLA has extensive experience in teaching statistical computing and Julia in university classrooms and conference venues. Presenter Dr. Josh Day from Julia Computing is a core developer of the JuliaDB and OnlineStats packages.
