Introduction to PySpark
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Start Course for Free4 Hours45 Exercises97,396 Learners3850 XPBig Data with PySpark TrackData Engineer TrackMachine Learning Scientist Track
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).Loved by learners at thousands of companies
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
Getting to know PySpark
FreeIn 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 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.
Machine Learning Pipelines50 xpJoin the DataFrames100 xpData types50 xpString to integer100 xpCreate a new column100 xpMaking a Boolean100 xpStrings and factors50 xpCarrier100 xpDestination100 xpAssemble a vector100 xpCreate the pipeline100 xpTest vs. Train50 xpTransform the data100 xpSplit the data100 xp - 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.
Collaborators

Prerequisites
Introduction to Python
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.

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.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Louis Maiden
Harvard Business School
DataCamp is by far my favorite website to learn from.
Ronald Bowers
Decision Science Analytics, USAA
Join over 9 million learners and start Introduction to PySpark today!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).