Data Science and Machine Learning at Scale

Data Umbrella

In this tutorial, you'll learn everything you wanted to know about scaling your data science work to larger datasets and larger models. 

Meetup: https://www.meetup.com/data-umbrella/events/273593514/Webinar link: https://www.bigmarker.com/neo4j/Data-Umbrella-Webinar3-2020-10-27-05-00-pm## Pre-reqsNot a lot. It would help if you knew* programming fundamentals and the basics of the Python programming language (e.g., variables, for loops);* a bit about `pandas`, `numpy`, and `scikit-learn` (although not strictly necessary);* a bit about Jupyter Notebooks;* your way around the terminal/shell.**However, I have always found that the most important and beneficial prerequisite is a will to learn new things so if you have this quality, you&#x27;ll definitely get something out of this workshop.**## Prep WorkSee GitHub repo: https://github.com/coiled/data-science-at-scale## EventIn this tutorial, you&#x27;ll learn everything you wanted to know about scaling your data science work to larger datasets and larger models, while staying in the comfort of the PyData ecosystem (numpy, pandas, scikit-learn, Jupyter notebooks).You&#x27;ll come away knowing* How to reason about when you need to scale your data and machine learning work and when to not;* How to leverage distribute computation on your local workstation (such as your laptop) to analyze larger datasets and build larger, more complex models;* How to harness the power of clusters to support larger-than-memory computation, all from the comfort of your own laptop;* How to do all of this while writing code similar to the numpy, pandas, and/or sckit-learn code you already write.

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Data Science and Machine Learning at Scale

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Data Science and Machine Learning at Scale

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