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Lore Dirick
Lore Dirick

Senior Data Science Curriculum Writer 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 now a senior Data Science Curriculum Writer at Flatiron School, a coding bootcamp in NYC.

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Nick Solomon
Nick Solomon

Data Scientist

Nick is a data scientist with a background in mathematics. For his thesis project at Reed College, he worked with random graph models used in social network analysis. He has experience doing data science in both R and Python.

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  • Colin Ricardo

    Colin Ricardo

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 late. 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.

  2. Manipulating data

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

  3. Getting started with machine learning pipelines

    PySpark provides cutting edge machine learning routines built in, along with utilities for creating full machine learning pipelines. You'll learn about them in this chapter.

  4. Model tuning and selection

    In this last chapter, you'll apply what you've learned to create a model that predicts what flights will be delayed!