Julia for Modern Data Analysis
summary
Julia claims high performance through JIT compilation and multiple dispatch and shows concrete tooling: interoperates with Python via PyCall and R via RCall, uses Plots.jl to swap back ends, and supports packages like Distributions, DataFrames and a pure‑Julia MCMC (mamba). Package counts rise from ~600 to ~1,000 and GitHub stars from ~8k to ~17k. A streaming library online_stats is built and runs, and mixed‑model fits run far faster than the same R workflow (which once took a day).
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chapters (14)
- 0:00Introduction
- 1:04What is Julia
- 3:54Multiple dispatch
- 6:20Julia vs other languages
- 8:28Julia adoption
- 10:35Julia for stats/data science
- 14:04Optimization & ML tools
- 15:07Upcoming ML tools
- 18:14Julia with R/Python
- 21:28Julia data packages
- 25:01OnlineStats for streaming
- 27:08MCMC with Mamba
- 28:52Julia future & limits
- 29:58Q&A
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