Data Science with Python and Dask

Created by: Jesse C. Daniel

Produced in 2019

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Course Description

Data Science with Python and Dask teaches you how to build distributed data projects that can handle huge amounts of data. You'll begin with an introduction to the Dask framework, concentrating on how Dask natively scales commonly-used Python libraries like Numpy and Pandas. With a particular focus on data analysis, you'll immediately start exploring the huge amount of data found in the NYC 2013-2017 Parking Ticket database. You'll be introduced to Dask DataFrames and learn helpful code patterns to streamline your analysis. You'll also dig into visualization with Seaborn and learn to build machine learning models using Dask-ML.As you work through Dask's features, you'll learn how to prepare and analyze the dataset to discover trends and patterns in NYC's parking enforcement operations. How does the time of year and weather affect issued citations? Is the number of citations rising or falling? You'll find out, and you'll figure out how to discover similar trends in your own data! Along the way, you'll look deeper into Dask Arrays and Bags, use Datashader to build interactive location-based visualizations, and learn to implement your own algorithms using custom task graphs. Finally, you'll learn how to scale your Dask apps and learn how to build your very own Dask cluster using AWS and Docker.

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Instructor Details

Jesse C. Daniel

Jesse Daniel has five years of experience writing applications in Python, including three years working with in the PyData stack (Pandas, NumPy, SciPy, Scikit-Learn). Jesse joined the faculty of the University of Denver in 2016 as an adjunct professor of business information and analytics, where he currently teaches a Python for Data Science course.

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