Introduction to PySpark
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
- 4 hours
- 0 Videos
- 46 Exercises
- 15,915 Participants
- 3,950 XP
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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
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
Prerequisites
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!
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1
Getting to know PySpark
FreeIn this chapter, you'll learn how Spark manages data and how can you read and write tables from Python.
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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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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.
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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!
Course Instructor
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.
See More
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.
See MoreCollaborator(s)
-
Colin Ricardo