Tutorial structure - Githubissues

coiled / data-science-at-scale

A Pythonic introduction to methods for scaling your data science and machine learning work to larger datasets and larger models, using the tools and APIs you know and love from the PyData stack (such as numpy, pandas, and scikit-learn).

MIT License

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Tutorial structure #11

Closed hugobowne closed 4 years ago

hugobowne commented 4 years ago

I think I've got the structure down pretty well in this commit.

Interested in @jrbourbeau's thoughts:

NB 1: motivating Dask with NYC-taxi example: dataset too big for memory so doing basic analytics on a Dask dataframe
NB 2a/2b: diving into Dask dataframes
NB3: diving into parallelization with Dask delayed
NB 4a/4b: Scalable machine learning -- ideally we could have a simple example of using Dask for CPU-bound ML & RAM-boun-ML (TBD with @jrbourbeau)

hugobowne commented 4 years ago

we're good here now