Introduction to PySpark

Learn to implement distributed data management and machine learning in Spark using the PySpark package.
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4 Hours45 Exercises80,438 Learners
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Course Description

In this course, you'll learn how to use Spark from Python! Spark is a tool for doing parallel computation with large datasets and it integrates well with Python. PySpark is the Python package that makes the magic happen. You'll use this package to work with data about flights from Portland and Seattle. You'll learn to wrangle this data and build a whole machine learning pipeline to predict whether or not flights will be delayed. Get ready to put some Spark in your Python code and dive into the world of high-performance machine learning!

  1. 1

    Getting to know PySpark

    In this chapter, you'll learn how Spark manages data and how can you read and write tables from Python.
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  2. 2

    Manipulating data

    In this chapter, you'll learn about the pyspark.sql module, which provides optimized data queries to your Spark session.
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  3. 3

    Getting started with machine learning pipelines

    PySpark has built-in, cutting-edge machine learning routines, along with utilities to create full machine learning pipelines. You'll learn about them in this chapter.
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  4. 4

    Model tuning and selection

    In this last chapter, you'll apply what you've learned to create a model that predicts which flights will be delayed.
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In the following tracks
Big Data with PySparkData EngineerMachine Learning Scientist
Colin Ricardo
Nick Solomon Headshot

Nick Solomon

Data Scientist
Nick has a degree in mathematics with a concentration in statistics from Reed College. He's worked on many data science projects in the past, doing everything from mapping crime data to developing new kinds of models for social networks. He's currently a data scientist in the New York City area.
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Lore Dirick Headshot

Lore Dirick

Director of Data Science Education at Flatiron School
Lore is a data scientist with expertise in applied finance. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is and is now Director of Data Science Education at Flatiron School, a coding school with branches in 8 cities and online programs.
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