Introduction to Julia for Scientific Computing and Data Science
summary
Claims that Julia lets one write high‑level code but run at compiled speed, demonstrated on Julia 0.6 inside Jupyter notebooks with precompile overhead noted. Shows calling C/Fortran and Python via PyCall, multiple dispatch, package manager, and plotting with Plots.jl / DataFrame workflows. Builds a JuMP optimization (mixed‑integer) solved with JuMP + CBC to pick three stocks approximating the Dow Jones; result tracks the index in-sample. GPU OpenCL timings: 5000×5000×1000 case ~35s on GPU versus >5min on CPU; smaller case GPU 2s vs CPU 15s.
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chapters (12)
- 0:00Workshop overview
- 1:28Julia basics
- 13:42Julia syntax and math
- 34:00Functions and memory
- 44:52Package ecosystem and data frames
- 55:55Data visualization with Plots.jl
- 1:03:15Stock optimization
- 1:08:42Calling C/Fortran libraries
- 1:14:01Parallel computing with OpenCL
- 1:20:48Benchmarking and profiling
- 1:22:38Debugging and stability
- 1:26:16Q&A
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