Introduction to Dask!

Presented by WWCode Washington DC
Speaker: Naty Clementi, Moderated by Genevieve Buckley
✨ Topic: Introduction to Dask

This lab is a beginner-intermediate lab - all levels welcome! :)

The purpose of this tutorial is to introduce folks to Dask and show them how to scale their python data-science and machine learning workflows.

The materials covered are:

1. Overview of dask - How it works and when to use it.
2. Dask Delayed: How to parallelize existing Python code and your custom algorithms.
3. Schedulers: Single Machine vs Distributed, and the Dashboard.
4. From pandas to Dask: How to manipulate bigger-than-memory DataFrames using Dask.
5. Dask-ML: Scalable machine learning using Dask.

Prerequisites
To follow along and get the most out of this tutorial it would help if you Know:

- Programming fundamentals in Python (e.g variables, data structures, for loops, etc).
- A bit of or are familiarized with numpy, pandas and scikit-learn.
Jupyter Lab/ Jupyter Notebooks
- Your way around the shell/terminal

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