The Best Data Science Talks of JuliaCon 2021
JuliaCon 2021 featured a wide range of talks. Here’s a curated selection of the most useful for practicing data scientists, with difficulty ratings: beginner, intermediate, or advanced.
Applied Measure Theory for Probabilistic Modeling (advanced)
Speaker: Chad Scherrer | Watch on YouTube
Overview of MeasureTheory.jl advantages relative to existing distributions packages. Loosely falls under data science but great for those with a mathematical statistics focus.
Bias Audit and Mitigation in Julia (beginner–intermediate)
Speaker: Ashrya Agrawal | Watch on YouTube
Introduces Fairness.jl toolkit for auditing and mitigating machine learning bias. Great introduction to fairness/bias issues with accessible examples.
Clearing the Pipeline Jungle with FeatureTransforms.jl (beginner–intermediate)
Speaker: Glenn Moynihan | Watch on YouTube
Addresses technical debt in feature engineering pipelines and demonstrates FeatureTransforms.jl solutions for sustainable practices without sacrificing flexibility.
DataFrames.jl 1.0 Tutorial — Workshop (beginner–intermediate)
Speaker: Bogumił Kamiński | Watch on YouTube
Comprehensive workshop on loading, transforming, and visualizing data. Assumes prior data frame experience from R or Python. Materials available on GitHub.
Easy, Featureful Parallelism with Dagger.jl (advanced)
Speaker: Julian P Samaroo | Watch on YouTube
Advanced parallelization beyond Distributed.jl, featuring GPU support and fault tolerance. Recommended for those struggling with Julia’s distributed computing primitives.
Introduction to Bayesian Data Analysis — Workshop (intermediate–advanced)
Speakers: Kusti Skytén, Chad Scherrer, Tor Fjelde | Watch on YouTube
In-depth tutorial on applied Bayesian workflows using increasingly sophisticated models. Compares Julia’s probabilistic programming advantages over Stan and Python.
Pluto – One Year Later (beginner–intermediate)
Speaker: Fons van der Plas | Watch on YouTube
Update on Pluto.jl, the interactive Julia notebook IDE. Strongly recommended for those frequently working in notebook environments.
Rewriting Pieces of a Python Codebase in Julia (intermediate)
Speaker: Satvik Souza Beri | Watch on YouTube
Practical guidance on migrating Python code to Julia using PyCall and PyJulia, with documented 10x-30x performance improvements. Covers gotchas and optimal use cases.
The State of DataFrames.jl (beginner)
Speaker: Bogumił Kamiński | Watch on YouTube
Discusses DataFrames.jl design philosophy, recent changes, and future plans without heavy code focus.
State of Julia (intermediate)
Speakers: Jeff Bezanson, Stefan Karpinski, Keno Fischer, Viral Shah | Watch on YouTube
Annual retrospective by Julia’s creators on development and community progress.
Statistics with Julia from the Ground Up — Workshop (beginner)
Speaker: Yoni Nazarathy | Watch on YouTube
My top recommendation for this list — a gentle introduction that works through probability, statistics, dataframes, inference, and regression. Great entry point for statisticians new to Julia.